feat(jetson): add dynamic obstacle tracking package (issue #176)

Implements real-time moving obstacle detection, Kalman tracking, trajectory
prediction, and Nav2 costmap integration at 10 Hz / <50ms latency:

saltybot_dynamic_obs_msgs (ament_cmake):
• TrackedObject.msg      — id, PoseWithCovariance, velocity, predicted_path,
                           predicted_times, speed, confidence, age, hits
• MovingObjectArray.msg  — TrackedObject[], active_count, tentative_count,
                           detector_latency_ms

saltybot_dynamic_obstacles (ament_python):
• object_detector.py    — LIDAR background subtraction (EMA occupancy grid),
                           foreground dilation + scipy connected-component
                           clustering → Detection list
• kalman_tracker.py     — CV Kalman filter, state [px,py,vx,vy], Joseph-form
                           covariance update, predict_horizon() (non-mutating)
• tracker_manager.py    — up to 20 tracks, Hungarian assignment
                           (scipy.optimize.linear_sum_assignment), TENTATIVE→
                           CONFIRMED lifecycle, miss-prune
• dynamic_obs_node.py   — 10 Hz timer: detect→track→publish
                           /saltybot/moving_objects + MarkerArray viz
• costmap_layer_node.py — predicted paths → PointCloud2 inflation smear
                           → /saltybot/dynamic_obs_cloud for Nav2 ObstacleLayer
• launch/dynamic_obstacles.launch.py + config/dynamic_obstacles_params.yaml
• test/test_dynamic_obstacles.py — 27 unit tests (27/27 pass, no ROS2 needed)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

jetson/ros2_ws/src/saltybot_dynamic_obs_msgs/CMakeLists.txt

@@ -0,0 +1,16 @@
+cmake_minimum_required(VERSION 3.8)
+project(saltybot_dynamic_obs_msgs)
+
+find_package(ament_cmake REQUIRED)
+find_package(rosidl_default_generators REQUIRED)
+find_package(std_msgs REQUIRED)
+find_package(geometry_msgs REQUIRED)
+
+rosidl_generate_interfaces(${PROJECT_NAME}
+  "msg/TrackedObject.msg"
+  "msg/MovingObjectArray.msg"
+  DEPENDENCIES std_msgs geometry_msgs
+)
+
+ament_export_dependencies(rosidl_default_runtime)
+ament_package()

jetson/ros2_ws/src/saltybot_dynamic_obs_msgs/msg/MovingObjectArray.msg

@@ -0,0 +1,12 @@
+# MovingObjectArray — all currently tracked moving obstacles.
+#
+# Published at ~10 Hz on /saltybot/moving_objects.
+# Only confirmed tracks (hits >= confirm_frames) appear here.
+
+std_msgs/Header header
+
+saltybot_dynamic_obs_msgs/TrackedObject[] objects
+
+uint32 active_count        # number of confirmed tracks
+uint32 tentative_count     # tracks not yet confirmed
+float32 detector_latency_ms # pipeline latency hint

jetson/ros2_ws/src/saltybot_dynamic_obs_msgs/msg/TrackedObject.msg

@@ -0,0 +1,21 @@
+# TrackedObject — a single tracked moving obstacle.
+#
+# predicted_path[i] is the estimated pose at predicted_times[i] seconds from now.
+# All poses are in the same frame as header.frame_id (typically 'odom').
+
+std_msgs/Header header
+
+uint32 object_id               # stable ID across frames (monotonically increasing)
+
+geometry_msgs/PoseWithCovariance pose    # current best-estimate pose (x, y, yaw)
+geometry_msgs/Vector3            velocity # vx, vy in m/s (vz = 0 for ground objects)
+
+geometry_msgs/Pose[] predicted_path   # future positions at predicted_times
+float32[]            predicted_times  # seconds from header.stamp for each pose
+
+float32 speed_mps    # scalar |v|
+float32 confidence   # 0.0–1.0 (higher after more confirmed frames)
+
+uint32 age_frames    # frames since first detection
+uint32 hits          # number of successful associations
+bool   is_valid      # false if in tentative / just-created state

jetson/ros2_ws/src/saltybot_dynamic_obs_msgs/package.xml

@@ -0,0 +1,23 @@
+<?xml version="1.0"?>
+<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
+<package format="3">
+  <name>saltybot_dynamic_obs_msgs</name>
+  <version>0.1.0</version>
+  <description>Custom message types for dynamic obstacle tracking.</description>
+  <maintainer email="robot@saltylab.local">SaltyLab</maintainer>
+  <license>MIT</license>
+
+  <buildtool_depend>ament_cmake</buildtool_depend>
+  <buildtool_depend>rosidl_default_generators</buildtool_depend>
+
+  <depend>std_msgs</depend>
+  <depend>geometry_msgs</depend>
+
+  <exec_depend>rosidl_default_runtime</exec_depend>
+
+  <member_of_group>rosidl_interface_packages</member_of_group>
+
+  <export>
+    <build_type>ament_cmake</build_type>
+  </export>
+</package>

jetson/ros2_ws/src/saltybot_dynamic_obstacles/config/dynamic_obstacles_params.yaml

@@ -0,0 +1,52 @@
+# saltybot_dynamic_obstacles — runtime parameters
+#
+# Requires:
+#   /scan  (sensor_msgs/LaserScan)  — RPLIDAR A1M8 at ~5.5 Hz
+#
+# LIDAR scan is published by rplidar_ros node.
+# Make sure RPLIDAR is running before starting this stack.
+
+dynamic_obs_tracker:
+  ros__parameters:
+    max_tracks:          20      # max simultaneous tracked objects
+    confirm_frames:       3      # hits before a track is published
+    max_missed_frames:    6      # missed frames before track deletion
+    assoc_dist_m:         1.5    # max assignment distance (Hungarian)
+    prediction_hz:       10.0    # tracker update + publish rate
+    horizon_s:            2.5    # prediction look-ahead
+    pred_step_s:          0.5    # time between predicted waypoints
+    odom_frame:          'odom'
+    min_speed_mps:        0.05   # suppress near-stationary tracks
+    max_range_m:          8.0    # ignore detections beyond this
+
+dynamic_obs_costmap:
+  ros__parameters:
+    inflation_radius_m:   0.35   # safety bubble around each predicted point
+    ring_points:          8      # polygon points for inflation circle
+    clear_on_empty:       true   # push empty cloud to clear stale Nav2 markings
+
+
+# ── Nav2 costmap integration ───────────────────────────────────────────────────
+# In your nav2_params.yaml, under local_costmap or global_costmap > plugins, add
+# an ObstacleLayer with:
+#
+#   obstacle_layer:
+#     plugin: "nav2_costmap_2d::ObstacleLayer"
+#     enabled: true
+#     observation_sources: static_scan dynamic_obs
+#     static_scan:
+#       topic: /scan
+#       data_type: LaserScan
+#       ...
+#     dynamic_obs:
+#       topic: /saltybot/dynamic_obs_cloud
+#       data_type: PointCloud2
+#       sensor_frame: odom
+#       obstacle_max_range: 10.0
+#       raytrace_max_range: 10.0
+#       marking: true
+#       clearing: false
+#
+# This feeds the predicted trajectory smear directly into Nav2's obstacle
+# inflation, forcing the planner to route around the predicted future path
+# of every tracked moving object.

jetson/ros2_ws/src/saltybot_dynamic_obstacles/launch/dynamic_obstacles.launch.py

@@ -0,0 +1,75 @@
+"""
+dynamic_obstacles.launch.py — Dynamic obstacle tracking + Nav2 costmap layer.
+
+Starts:
+  dynamic_obs_tracker  — LIDAR motion detection + Kalman tracking @10 Hz
+  dynamic_obs_costmap  — Predicted-trajectory → PointCloud2 for Nav2
+
+Launch args:
+  max_tracks         int    '20'
+  assoc_dist_m       float  '1.5'
+  horizon_s          float  '2.5'
+  inflation_radius_m float  '0.35'
+
+Verify:
+  ros2 topic hz /saltybot/moving_objects        # ~10 Hz
+  ros2 topic echo /saltybot/moving_objects      # TrackedObject list
+  ros2 topic hz /saltybot/dynamic_obs_cloud     # ~10 Hz (when objects present)
+  rviz2 → add MarkerArray → /saltybot/moving_objects_viz
+
+Nav2 costmap integration:
+  In your costmap_params.yaml ObstacleLayer observation_sources, add:
+    dynamic_obs:
+      topic: /saltybot/dynamic_obs_cloud
+      data_type: PointCloud2
+      marking: true
+      clearing: false
+"""
+
+from launch import LaunchDescription
+from launch.actions import DeclareLaunchArgument
+from launch.substitutions import LaunchConfiguration
+from launch_ros.actions import Node
+
+
+def generate_launch_description():
+    args = [
+        DeclareLaunchArgument('max_tracks',         default_value='20'),
+        DeclareLaunchArgument('assoc_dist_m',        default_value='1.5'),
+        DeclareLaunchArgument('horizon_s',           default_value='2.5'),
+        DeclareLaunchArgument('inflation_radius_m',  default_value='0.35'),
+        DeclareLaunchArgument('min_speed_mps',       default_value='0.05'),
+    ]
+
+    tracker = Node(
+        package='saltybot_dynamic_obstacles',
+        executable='dynamic_obs_tracker',
+        name='dynamic_obs_tracker',
+        output='screen',
+        parameters=[{
+            'max_tracks':       LaunchConfiguration('max_tracks'),
+            'assoc_dist_m':     LaunchConfiguration('assoc_dist_m'),
+            'horizon_s':        LaunchConfiguration('horizon_s'),
+            'min_speed_mps':    LaunchConfiguration('min_speed_mps'),
+            'prediction_hz':    10.0,
+            'confirm_frames':   3,
+            'max_missed_frames': 6,
+            'pred_step_s':      0.5,
+            'odom_frame':       'odom',
+            'max_range_m':      8.0,
+        }],
+    )
+
+    costmap = Node(
+        package='saltybot_dynamic_obstacles',
+        executable='dynamic_obs_costmap',
+        name='dynamic_obs_costmap',
+        output='screen',
+        parameters=[{
+            'inflation_radius_m': LaunchConfiguration('inflation_radius_m'),
+            'ring_points':        8,
+            'clear_on_empty':     True,
+        }],
+    )
+
+    return LaunchDescription(args + [tracker, costmap])

jetson/ros2_ws/src/saltybot_dynamic_obstacles/package.xml

@@ -0,0 +1,29 @@
+<?xml version="1.0"?>
+<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
+<package format="3">
+  <name>saltybot_dynamic_obstacles</name>
+  <version>0.1.0</version>
+  <description>
+    Dynamic obstacle detection, multi-object Kalman tracking, trajectory
+    prediction, and Nav2 costmap layer integration for SaltyBot.
+  </description>
+  <maintainer email="robot@saltylab.local">SaltyLab</maintainer>
+  <license>MIT</license>
+
+  <depend>rclpy</depend>
+  <depend>std_msgs</depend>
+  <depend>sensor_msgs</depend>
+  <depend>geometry_msgs</depend>
+  <depend>nav_msgs</depend>
+  <depend>visualization_msgs</depend>
+  <depend>saltybot_dynamic_obs_msgs</depend>
+
+  <exec_depend>python3-numpy</exec_depend>
+  <exec_depend>python3-scipy</exec_depend>
+
+  <test_depend>pytest</test_depend>
+
+  <export>
+    <build_type>ament_python</build_type>
+  </export>
+</package>

jetson/ros2_ws/src/saltybot_dynamic_obstacles/saltybot_dynamic_obstacles/costmap_layer_node.py

@@ -0,0 +1,178 @@
+"""
+costmap_layer_node.py — Nav2 costmap integration for dynamic obstacles.
+
+Converts predicted trajectories from /saltybot/moving_objects into a
+PointCloud2 fed into Nav2's ObstacleLayer.  Each predicted future position
+is added as a point, creating a "smeared" dynamic obstacle zone that
+covers the full 2-3 s prediction horizon.
+
+Nav2 ObstacleLayer config (in costmap_params.yaml):
+  obstacle_layer:
+    enabled: true
+    observation_sources: dynamic_obs
+    dynamic_obs:
+      topic: /saltybot/dynamic_obs_cloud
+      sensor_frame: odom
+      data_type: PointCloud2
+      obstacle_max_range: 12.0
+      obstacle_min_range: 0.0
+      raytrace_max_range: 12.0
+      marking: true
+      clearing: false   # let the tracker handle clearing
+
+The node also clears old obstacle points when tracks are dropped, by
+publishing a clearing cloud to /saltybot/dynamic_obs_clear.
+
+Subscribes:
+  /saltybot/moving_objects     saltybot_dynamic_obs_msgs/MovingObjectArray
+
+Publishes:
+  /saltybot/dynamic_obs_cloud  sensor_msgs/PointCloud2   marking cloud
+  /saltybot/dynamic_obs_clear  sensor_msgs/PointCloud2   clearing cloud
+
+Parameters:
+  inflation_radius_m  float  0.35   (each predicted point inflated by this radius)
+  ring_points         int    8      (polygon approximation of inflation circle)
+  clear_on_empty      bool   true   (publish clear cloud when no objects tracked)
+"""
+
+from __future__ import annotations
+
+import math
+import struct
+from typing import List
+
+import rclpy
+from rclpy.node import Node
+from rclpy.qos import QoSProfile, ReliabilityPolicy, HistoryPolicy
+
+from sensor_msgs.msg import PointCloud2, PointField
+from std_msgs.msg import Header
+
+try:
+    from saltybot_dynamic_obs_msgs.msg import MovingObjectArray
+    _MSGS_AVAILABLE = True
+except ImportError:
+    _MSGS_AVAILABLE = False
+
+
+_RELIABLE_QOS = QoSProfile(
+    reliability=ReliabilityPolicy.RELIABLE,
+    history=HistoryPolicy.KEEP_LAST,
+    depth=10,
+)
+
+
+def _make_pc2(header: Header, points_xyz: List[tuple]) -> PointCloud2:
+    """Pack a list of (x, y, z) into a PointCloud2 message."""
+    fields = [
+        PointField(name='x', offset=0,  datatype=PointField.FLOAT32, count=1),
+        PointField(name='y', offset=4,  datatype=PointField.FLOAT32, count=1),
+        PointField(name='z', offset=8,  datatype=PointField.FLOAT32, count=1),
+    ]
+    point_step = 12   # 3 × float32
+    data = bytearray(len(points_xyz) * point_step)
+    for i, (x, y, z) in enumerate(points_xyz):
+        struct.pack_into('<fff', data, i * point_step, x, y, z)
+
+    pc = PointCloud2()
+    pc.header      = header
+    pc.height      = 1
+    pc.width       = len(points_xyz)
+    pc.fields      = fields
+    pc.is_bigendian = False
+    pc.point_step  = point_step
+    pc.row_step    = point_step * len(points_xyz)
+    pc.data        = bytes(data)
+    pc.is_dense    = True
+    return pc
+
+
+class CostmapLayerNode(Node):
+
+    def __init__(self):
+        super().__init__('dynamic_obs_costmap')
+
+        self.declare_parameter('inflation_radius_m', 0.35)
+        self.declare_parameter('ring_points',         8)
+        self.declare_parameter('clear_on_empty',      True)
+
+        self._infl_r      = self.get_parameter('inflation_radius_m').value
+        self._ring_n      = self.get_parameter('ring_points').value
+        self._clear_empty = self.get_parameter('clear_on_empty').value
+
+        # Pre-compute ring offsets for inflation
+        self._ring_offsets = [
+            (self._infl_r * math.cos(2 * math.pi * i / self._ring_n),
+             self._infl_r * math.sin(2 * math.pi * i / self._ring_n))
+            for i in range(self._ring_n)
+        ]
+
+        if _MSGS_AVAILABLE:
+            self.create_subscription(
+                MovingObjectArray,
+                '/saltybot/moving_objects',
+                self._on_objects,
+                _RELIABLE_QOS,
+            )
+        else:
+            self.get_logger().warning(
+                '[costmap_layer] saltybot_dynamic_obs_msgs not built — '
+                'will not subscribe to MovingObjectArray'
+            )
+
+        self._mark_pub  = self.create_publisher(
+            PointCloud2, '/saltybot/dynamic_obs_cloud', 10
+        )
+        self._clear_pub = self.create_publisher(
+            PointCloud2, '/saltybot/dynamic_obs_clear', 10
+        )
+
+        self.get_logger().info(
+            f'dynamic_obs_costmap ready — '
+            f'inflation={self._infl_r}m ring_pts={self._ring_n}'
+        )
+
+    # ── Callback ──────────────────────────────────────────────────────────────
+
+    def _on_objects(self, msg: 'MovingObjectArray') -> None:
+        hdr = msg.header
+        mark_pts: List[tuple] = []
+
+        for obj in msg.objects:
+            if not obj.is_valid:
+                continue
+
+            # Current position
+            self._add_inflated(obj.pose.pose.position.x,
+                               obj.pose.pose.position.y, mark_pts)
+
+            # Predicted future positions
+            for pose in obj.predicted_path:
+                self._add_inflated(pose.position.x, pose.position.y, mark_pts)
+
+        if mark_pts:
+            self._mark_pub.publish(_make_pc2(hdr, mark_pts))
+        elif self._clear_empty:
+            # Publish tiny clear cloud so Nav2 clears stale markings
+            self._clear_pub.publish(_make_pc2(hdr, []))
+
+    def _add_inflated(self, cx: float, cy: float, pts: List[tuple]) -> None:
+        """Add the centre + ring of inflation points at height 0.5 m."""
+        pts.append((cx, cy, 0.5))
+        for ox, oy in self._ring_offsets:
+            pts.append((cx + ox, cy + oy, 0.5))
+
+
+def main(args=None):
+    rclpy.init(args=args)
+    node = CostmapLayerNode()
+    try:
+        rclpy.spin(node)
+    finally:
+        node.destroy_node()
+        rclpy.shutdown()
+
+
+if __name__ == '__main__':
+    main()

jetson/ros2_ws/src/saltybot_dynamic_obstacles/saltybot_dynamic_obstacles/dynamic_obs_node.py

@@ -0,0 +1,319 @@
+"""
+dynamic_obs_node.py — ROS2 node: LIDAR moving-object detection + Kalman tracking.
+
+Pipeline:
+  1. Subscribe /scan (RPLIDAR LaserScan, ~5.5 Hz).
+  2. ObjectDetector performs background subtraction → moving blobs.
+  3. TrackerManager runs Hungarian assignment + Kalman predict/update at 10 Hz.
+  4. Publish /saltybot/moving_objects (MovingObjectArray).
+  5. Publish /saltybot/moving_objects_viz (MarkerArray) for RViz.
+
+The 10 Hz timer drives the tracker regardless of scan rate, so prediction
+continues between scans (pure-predict steps).
+
+Subscribes:
+  /scan                    sensor_msgs/LaserScan   RPLIDAR A1M8
+
+Publishes:
+  /saltybot/moving_objects          saltybot_dynamic_obs_msgs/MovingObjectArray
+  /saltybot/moving_objects_viz      visualization_msgs/MarkerArray
+
+Parameters:
+  max_tracks          int    20
+  confirm_frames      int    3
+  max_missed_frames   int    6
+  assoc_dist_m        float  1.5
+  prediction_hz       float  10.0     (tracker + publish rate)
+  horizon_s           float  2.5
+  pred_step_s         float  0.5
+  odom_frame          str    'odom'
+  min_speed_mps       float  0.05     (suppress near-zero velocity tracks)
+  max_range_m         float  8.0
+"""
+
+import time
+import math
+
+import numpy as np
+import rclpy
+from rclpy.node import Node
+from rclpy.qos import QoSProfile, ReliabilityPolicy, HistoryPolicy
+
+from sensor_msgs.msg import LaserScan
+from geometry_msgs.msg import Pose, Point, Quaternion, Vector3
+from std_msgs.msg import Header, ColorRGBA
+from visualization_msgs.msg import Marker, MarkerArray
+
+try:
+    from saltybot_dynamic_obs_msgs.msg import TrackedObject, MovingObjectArray
+    _MSGS_AVAILABLE = True
+except ImportError:
+    _MSGS_AVAILABLE = False
+
+from .object_detector import ObjectDetector
+from .tracker_manager import TrackerManager, Track
+
+
+_SENSOR_QOS = QoSProfile(
+    reliability=ReliabilityPolicy.BEST_EFFORT,
+    history=HistoryPolicy.KEEP_LAST,
+    depth=5,
+)
+
+
+def _yaw_quat(yaw: float) -> Quaternion:
+    q = Quaternion()
+    q.w = math.cos(yaw * 0.5)
+    q.z = math.sin(yaw * 0.5)
+    return q
+
+
+class DynamicObsNode(Node):
+
+    def __init__(self):
+        super().__init__('dynamic_obs_tracker')
+
+        # ── Parameters ──────────────────────────────────────────────────────
+        self.declare_parameter('max_tracks',         20)
+        self.declare_parameter('confirm_frames',      3)
+        self.declare_parameter('max_missed_frames',   6)
+        self.declare_parameter('assoc_dist_m',        1.5)
+        self.declare_parameter('prediction_hz',      10.0)
+        self.declare_parameter('horizon_s',           2.5)
+        self.declare_parameter('pred_step_s',         0.5)
+        self.declare_parameter('odom_frame',         'odom')
+        self.declare_parameter('min_speed_mps',       0.05)
+        self.declare_parameter('max_range_m',         8.0)
+
+        max_tracks    = self.get_parameter('max_tracks').value
+        confirm_f     = self.get_parameter('confirm_frames').value
+        max_missed    = self.get_parameter('max_missed_frames').value
+        assoc_dist    = self.get_parameter('assoc_dist_m').value
+        pred_hz       = self.get_parameter('prediction_hz').value
+        horizon_s     = self.get_parameter('horizon_s').value
+        pred_step     = self.get_parameter('pred_step_s').value
+        self._frame   = self.get_parameter('odom_frame').value
+        self._min_spd = self.get_parameter('min_speed_mps').value
+        self._max_rng = self.get_parameter('max_range_m').value
+
+        # ── Core modules ────────────────────────────────────────────────────
+        self._detector = ObjectDetector(
+            grid_radius_m=min(self._max_rng + 2.0, 12.0),
+            max_cluster=int((self._max_rng / 0.1) ** 2 * 0.5),
+        )
+        self._tracker = TrackerManager(
+            max_tracks=max_tracks,
+            confirm_frames=confirm_f,
+            max_missed=max_missed,
+            assoc_dist_m=assoc_dist,
+            horizon_s=horizon_s,
+            pred_step_s=pred_step,
+        )
+        self._horizon_s  = horizon_s
+        self._pred_step  = pred_step
+
+        # ── State ────────────────────────────────────────────────────────────
+        self._latest_scan: LaserScan | None = None
+        self._last_track_t: float = time.monotonic()
+        self._scan_processed_stamp: float | None = None
+
+        # ── Subscriptions ────────────────────────────────────────────────────
+        self.create_subscription(LaserScan, '/scan', self._on_scan, _SENSOR_QOS)
+
+        # ── Publishers ───────────────────────────────────────────────────────
+        if _MSGS_AVAILABLE:
+            self._obj_pub = self.create_publisher(
+                MovingObjectArray, '/saltybot/moving_objects', 10
+            )
+        else:
+            self._obj_pub = None
+            self.get_logger().warning(
+                '[dyn_obs] saltybot_dynamic_obs_msgs not built — '
+                'MovingObjectArray will not be published'
+            )
+
+        self._viz_pub = self.create_publisher(
+            MarkerArray, '/saltybot/moving_objects_viz', 10
+        )
+
+        # ── Timer ────────────────────────────────────────────────────────────
+        self.create_timer(1.0 / pred_hz, self._track_tick)
+
+        self.get_logger().info(
+            f'dynamic_obs_tracker ready — '
+            f'max_tracks={max_tracks} horizon={horizon_s}s assoc={assoc_dist}m'
+        )
+
+    # ── Scan callback ─────────────────────────────────────────────────────────
+
+    def _on_scan(self, msg: LaserScan) -> None:
+        self._latest_scan = msg
+
+    # ── 10 Hz tracker tick ────────────────────────────────────────────────────
+
+    def _track_tick(self) -> None:
+        t0 = time.monotonic()
+
+        now_mono = t0
+        dt = now_mono - self._last_track_t
+        dt = max(1e-3, min(dt, 0.5))
+        self._last_track_t = now_mono
+
+        scan = self._latest_scan
+        detections = []
+
+        if scan is not None:
+            stamp_sec = scan.header.stamp.sec + scan.header.stamp.nanosec * 1e-9
+            if stamp_sec != self._scan_processed_stamp:
+                self._scan_processed_stamp = stamp_sec
+                ranges = np.asarray(scan.ranges, dtype=np.float32)
+                detections = self._detector.update(
+                    ranges,
+                    scan.angle_min,
+                    scan.angle_increment,
+                    min(scan.range_max, self._max_rng),
+                )
+
+        confirmed = self._tracker.update(detections, dt)
+
+        latency_ms = (time.monotonic() - t0) * 1000.0
+        stamp = self.get_clock().now().to_msg()
+
+        if _MSGS_AVAILABLE and self._obj_pub is not None:
+            self._publish_objects(confirmed, stamp, latency_ms)
+        self._publish_viz(confirmed, stamp)
+
+    # ── Publish helpers ───────────────────────────────────────────────────────
+
+    def _publish_objects(self, confirmed: list, stamp, latency_ms: float) -> None:
+        arr = MovingObjectArray()
+        arr.header.stamp    = stamp
+        arr.header.frame_id = self._frame
+        arr.active_count    = len(confirmed)
+        arr.tentative_count = self._tracker.tentative_count
+        arr.detector_latency_ms = float(latency_ms)
+
+        for tr in confirmed:
+            px, py   = tr.kalman.position
+            vx, vy   = tr.kalman.velocity
+            speed    = tr.kalman.speed
+            if speed < self._min_spd:
+                continue
+
+            obj = TrackedObject()
+            obj.header          = arr.header
+            obj.object_id       = tr.track_id
+            obj.pose.pose.position.x = px
+            obj.pose.pose.position.y = py
+            obj.pose.pose.orientation = _yaw_quat(math.atan2(vy, vx))
+            cov = tr.kalman.covariance_2x2
+            obj.pose.covariance[0]  = float(cov[0, 0])
+            obj.pose.covariance[1]  = float(cov[0, 1])
+            obj.pose.covariance[6]  = float(cov[1, 0])
+            obj.pose.covariance[7]  = float(cov[1, 1])
+            obj.velocity.x  = vx
+            obj.velocity.y  = vy
+            obj.speed_mps   = speed
+            obj.confidence  = min(1.0, tr.hits / (self._tracker._confirm_frames * 3))
+            obj.age_frames  = tr.age
+            obj.hits        = tr.hits
+            obj.is_valid    = True
+
+            # Predicted path
+            for px_f, py_f, t_f in tr.kalman.predict_horizon(
+                self._horizon_s, self._pred_step
+            ):
+                p = Pose()
+                p.position.x = px_f
+                p.position.y = py_f
+                p.orientation.w = 1.0
+                obj.predicted_path.append(p)
+                obj.predicted_times.append(float(t_f))
+
+            arr.objects.append(obj)
+
+        self._obj_pub.publish(arr)
+
+    def _publish_viz(self, confirmed: list, stamp) -> None:
+        markers = MarkerArray()
+
+        # Delete old markers
+        del_marker = Marker()
+        del_marker.header.stamp    = stamp
+        del_marker.header.frame_id = self._frame
+        del_marker.action = Marker.DELETEALL
+        markers.markers.append(del_marker)
+
+        for tr in confirmed:
+            px, py = tr.kalman.position
+            vx, vy = tr.kalman.velocity
+            speed  = tr.kalman.speed
+            if speed < self._min_spd:
+                continue
+
+            # Cylinder at current position
+            m = Marker()
+            m.header.stamp    = stamp
+            m.header.frame_id = self._frame
+            m.ns     = 'dyn_obs'
+            m.id     = tr.track_id
+            m.type   = Marker.CYLINDER
+            m.action = Marker.ADD
+            m.pose.position.x = px
+            m.pose.position.y = py
+            m.pose.position.z = 0.5
+            m.pose.orientation.w = 1.0
+            m.scale.x = 0.4
+            m.scale.y = 0.4
+            m.scale.z = 1.0
+            m.color   = ColorRGBA(r=1.0, g=0.2, b=0.0, a=0.7)
+            m.lifetime.sec = 1
+            markers.markers.append(m)
+
+            # Arrow for velocity
+            vel_m = Marker()
+            vel_m.header  = m.header
+            vel_m.ns      = 'dyn_obs_vel'
+            vel_m.id      = tr.track_id
+            vel_m.type    = Marker.ARROW
+            vel_m.action  = Marker.ADD
+            from geometry_msgs.msg import Point as GPoint
+            p_start = GPoint(x=px, y=py, z=1.0)
+            p_end   = GPoint(x=px + vx, y=py + vy, z=1.0)
+            vel_m.points  = [p_start, p_end]
+            vel_m.scale.x = 0.05
+            vel_m.scale.y = 0.10
+            vel_m.color   = ColorRGBA(r=1.0, g=1.0, b=0.0, a=0.9)
+            vel_m.lifetime.sec = 1
+            markers.markers.append(vel_m)
+
+            # Line strip for predicted path
+            path_m = Marker()
+            path_m.header  = m.header
+            path_m.ns      = 'dyn_obs_path'
+            path_m.id      = tr.track_id
+            path_m.type    = Marker.LINE_STRIP
+            path_m.action  = Marker.ADD
+            path_m.scale.x = 0.04
+            path_m.color   = ColorRGBA(r=1.0, g=0.5, b=0.0, a=0.5)
+            path_m.lifetime.sec = 1
+            path_m.points.append(GPoint(x=px, y=py, z=0.5))
+            for fx, fy, _ in tr.kalman.predict_horizon(self._horizon_s, self._pred_step):
+                path_m.points.append(GPoint(x=fx, y=fy, z=0.5))
+            markers.markers.append(path_m)
+
+        self._viz_pub.publish(markers)
+
+
+def main(args=None):
+    rclpy.init(args=args)
+    node = DynamicObsNode()
+    try:
+        rclpy.spin(node)
+    finally:
+        node.destroy_node()
+        rclpy.shutdown()
+
+
+if __name__ == '__main__':
+    main()

jetson/ros2_ws/src/saltybot_dynamic_obstacles/saltybot_dynamic_obstacles/kalman_tracker.py

@@ -0,0 +1,132 @@
+"""
+kalman_tracker.py — Single-object Kalman filter for 2-D ground-plane tracking.
+
+State vector:  x = [px, py, vx, vy]   (position + velocity)
+Motion model:  constant-velocity (CV) with process noise on acceleration
+
+Predict step:
+    F = | 1 0 dt  0 |      x_{k|k-1} = F @ x_{k-1|k-1}
+        | 0 1  0 dt |      P_{k|k-1} = F @ P @ F^T + Q
+        | 0 0  1  0 |
+        | 0 0  0  1 |
+
+Update step (position observation only):
+    H = | 1 0 0 0 |      y   = z - H @ x
+        | 0 1 0 0 |      S   = H @ P @ H^T + R
+                          K   = P @ H^T @ inv(S)
+                          x   = x + K @ y
+                          P   = (I - K @ H) @ P   (Joseph form for stability)
+
+Trajectory prediction: unrolls the CV model forward at fixed time steps.
+"""
+
+from __future__ import annotations
+
+from typing import List, Tuple
+
+import numpy as np
+
+
+# ── Default noise matrices ────────────────────────────────────────────────────
+# Process noise: models uncertainty in acceleration between frames
+_Q_BASE = np.diag([0.02, 0.02, 0.8, 0.8]).astype(np.float64)
+
+# Measurement noise: LIDAR centroid uncertainty (~0.15 m std)
+_R = np.diag([0.025, 0.025]).astype(np.float64)   # 0.16 m sigma each axis
+
+# Observation matrix
+_H = np.array([[1, 0, 0, 0],
+               [0, 1, 0, 0]], dtype=np.float64)
+_I4 = np.eye(4, dtype=np.float64)
+
+
+class KalmanTracker:
+    """
+    Kalman filter tracking one object.
+
+    Parameters
+    ----------
+    x0, y0         : initial position (metres, odom frame)
+    process_noise  : scalar multiplier on _Q_BASE
+    """
+
+    def __init__(self, x0: float, y0: float, process_noise: float = 1.0):
+        self._x = np.array([x0, y0, 0.0, 0.0], dtype=np.float64)
+        self._P = np.eye(4, dtype=np.float64) * 0.5
+        self._Q = _Q_BASE * process_noise
+
+    # ── Core filter ───────────────────────────────────────────────────────────
+
+    def predict(self, dt: float) -> None:
+        """Propagate state by dt seconds."""
+        F = np.array([
+            [1, 0, dt, 0],
+            [0, 1,  0, dt],
+            [0, 0,  1,  0],
+            [0, 0,  0,  1],
+        ], dtype=np.float64)
+        self._x = F @ self._x
+        self._P = F @ self._P @ F.T + self._Q
+
+    def update(self, z: np.ndarray) -> None:
+        """
+        Incorporate a position measurement z = [x, y].
+        Uses Joseph-form covariance update for numerical stability.
+        """
+        y = z.astype(np.float64) - _H @ self._x
+        S = _H @ self._P @ _H.T + _R
+        K = self._P @ _H.T @ np.linalg.inv(S)
+        self._x = self._x + K @ y
+        IKH = _I4 - K @ _H
+        # Joseph form: (I-KH) P (I-KH)^T + K R K^T
+        self._P = IKH @ self._P @ IKH.T + K @ _R @ K.T
+
+    # ── Prediction horizon ────────────────────────────────────────────────────
+
+    def predict_horizon(
+        self,
+        horizon_s: float = 2.5,
+        step_s: float = 0.5,
+    ) -> List[Tuple[float, float, float]]:
+        """
+        Return [(x, y, t), ...] at equally-spaced future times.
+
+        Does NOT modify internal filter state.
+        """
+        predictions: List[Tuple[float, float, float]] = []
+        state = self._x.copy()
+        t = 0.0
+        F_step = np.array([
+            [1, 0, step_s,      0],
+            [0, 1,      0, step_s],
+            [0, 0,      1,      0],
+            [0, 0,      0,      1],
+        ], dtype=np.float64)
+        while t < horizon_s - 1e-6:
+            state = F_step @ state
+            t += step_s
+            predictions.append((float(state[0]), float(state[1]), t))
+        return predictions
+
+    # ── Properties ────────────────────────────────────────────────────────────
+
+    @property
+    def position(self) -> Tuple[float, float]:
+        return float(self._x[0]), float(self._x[1])
+
+    @property
+    def velocity(self) -> Tuple[float, float]:
+        return float(self._x[2]), float(self._x[3])
+
+    @property
+    def speed(self) -> float:
+        return float(np.hypot(self._x[2], self._x[3]))
+
+    @property
+    def covariance_2x2(self) -> np.ndarray:
+        """Position covariance (top-left 2×2 of P)."""
+        return self._P[:2, :2].copy()
+
+    @property
+    def state(self) -> np.ndarray:
+        return self._x.copy()

jetson/ros2_ws/src/saltybot_dynamic_obstacles/saltybot_dynamic_obstacles/object_detector.py

@@ -0,0 +1,168 @@
+"""
+object_detector.py — LIDAR-based moving object detector.
+
+Algorithm:
+  1. Convert each LaserScan to a 2-D occupancy grid (robot-centred, fixed size).
+  2. Maintain a background model via exponential moving average (EMA):
+       bg_t = α * current + (1-α) * bg_{t-1}   (only for non-moving cells)
+  3. Foreground = cells whose occupancy significantly exceeds the background.
+  4. Cluster foreground cells with scipy connected-components → Detection list.
+
+The grid is robot-relative (origin at robot centre) so it naturally tracks
+the robot's motion without needing TF at this stage.  The caller is responsible
+for transforming detections into a stable frame (odom) before passing to the
+tracker.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+import numpy as np
+from scipy import ndimage
+
+
+@dataclass
+class Detection:
+    """A clustered moving foreground blob from one scan."""
+    x: float          # centroid x in sensor frame (m)
+    y: float          # centroid y in sensor frame (m)
+    size_m2: float    # approximate area of the cluster (m²)
+    range_m: float    # distance from robot (m)
+
+
+class ObjectDetector:
+    """
+    Detects moving objects in consecutive 2-D LIDAR scans.
+
+    Parameters
+    ----------
+    grid_radius_m : half-size of the occupancy grid (grid covers ±radius)
+    resolution    : metres per cell
+    bg_alpha      : EMA update rate for background (small = slow forgetting)
+    motion_thr    : occupancy delta above background to count as moving
+    min_cluster   : minimum cells to keep a cluster
+    max_cluster   : maximum cells before a cluster is considered static wall
+    """
+
+    def __init__(
+        self,
+        grid_radius_m: float = 10.0,
+        resolution: float = 0.10,
+        bg_alpha: float = 0.04,
+        motion_thr: float = 0.45,
+        min_cluster: int = 3,
+        max_cluster: int = 200,
+    ):
+        cells = int(2 * grid_radius_m / resolution)
+        self._cells     = cells
+        self._res       = resolution
+        self._origin    = -grid_radius_m        # world x/y at grid index 0
+        self._bg_alpha  = bg_alpha
+        self._motion_thr = motion_thr
+        self._min_clust = min_cluster
+        self._max_clust = max_cluster
+
+        self._bg = np.zeros((cells, cells), dtype=np.float32)
+        self._initialized = False
+
+    # ── Public API ────────────────────────────────────────────────────────────
+
+    def update(
+        self,
+        ranges: np.ndarray,
+        angle_min: float,
+        angle_increment: float,
+        range_max: float,
+    ) -> List[Detection]:
+        """
+        Process one LaserScan and return detected moving blobs.
+
+        Parameters
+        ----------
+        ranges          : 1-D array of range readings (metres)
+        angle_min       : angle of first beam (radians)
+        angle_increment : angular step between beams (radians)
+        range_max       : maximum valid range (metres)
+        """
+        curr = self._scan_to_grid(ranges, angle_min, angle_increment, range_max)
+
+        if not self._initialized:
+            self._bg = curr.copy()
+            self._initialized = True
+            return []
+
+        # Foreground mask
+        motion_mask = (curr - self._bg) > self._motion_thr
+
+        # Update background only on non-moving cells
+        static = ~motion_mask
+        self._bg[static] = (
+            self._bg[static] * (1.0 - self._bg_alpha)
+            + curr[static] * self._bg_alpha
+        )
+
+        return self._cluster(motion_mask)
+
+    def reset(self) -> None:
+        self._bg[:] = 0.0
+        self._initialized = False
+
+    # ── Internals ─────────────────────────────────────────────────────────────
+
+    def _scan_to_grid(
+        self,
+        ranges: np.ndarray,
+        angle_min: float,
+        angle_increment: float,
+        range_max: float,
+    ) -> np.ndarray:
+        grid = np.zeros((self._cells, self._cells), dtype=np.float32)
+        n = len(ranges)
+        angles = angle_min + np.arange(n) * angle_increment
+        r = np.asarray(ranges, dtype=np.float32)
+
+        valid = (r > 0.05) & (r < range_max) & np.isfinite(r)
+        r, a = r[valid], angles[valid]
+
+        x = r * np.cos(a)
+        y = r * np.sin(a)
+
+        ix = np.clip(
+            ((x - self._origin) / self._res).astype(np.int32), 0, self._cells - 1
+        )
+        iy = np.clip(
+            ((y - self._origin) / self._res).astype(np.int32), 0, self._cells - 1
+        )
+        grid[iy, ix] = 1.0
+        return grid
+
+    def _cluster(self, mask: np.ndarray) -> List[Detection]:
+        # Dilate slightly to connect nearby hit cells into one blob
+        struct = ndimage.generate_binary_structure(2, 2)
+        dilated = ndimage.binary_dilation(mask, structure=struct, iterations=1)
+
+        labeled, n_labels = ndimage.label(dilated)
+        detections: List[Detection] = []
+
+        for label_id in range(1, n_labels + 1):
+            coords = np.argwhere(labeled == label_id)
+            n_cells = len(coords)
+            if n_cells < self._min_clust or n_cells > self._max_clust:
+                continue
+
+            ys, xs = coords[:, 0], coords[:, 1]
+            cx_grid = float(np.mean(xs))
+            cy_grid = float(np.mean(ys))
+            cx = cx_grid * self._res + self._origin
+            cy = cy_grid * self._res + self._origin
+
+            detections.append(Detection(
+                x=cx,
+                y=cy,
+                size_m2=n_cells * self._res ** 2,
+                range_m=float(np.hypot(cx, cy)),
+            ))
+
+        return detections

jetson/ros2_ws/src/saltybot_dynamic_obstacles/saltybot_dynamic_obstacles/tracker_manager.py

@@ -0,0 +1,206 @@
+"""
+tracker_manager.py — Multi-object tracker with Hungarian data association.
+
+Track lifecycle:
+  TENTATIVE → confirmed after `confirm_frames` consecutive hits
+  CONFIRMED → normal tracked state
+  LOST      → missed for 1..max_missed frames (still predicts, not published)
+  DEAD      → missed > max_missed → removed
+
+Association:
+  Uses scipy.optimize.linear_sum_assignment (Hungarian algorithm) on a cost
+  matrix of Euclidean distances between predicted track positions and new
+  detections.  Assignments with cost > assoc_dist_m are rejected.
+
+Up to `max_tracks` simultaneous live tracks (tentative + confirmed).
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from enum import IntEnum
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+
+from .kalman_tracker import KalmanTracker
+from .object_detector import Detection
+
+
+class TrackState(IntEnum):
+    TENTATIVE = 0
+    CONFIRMED = 1
+    LOST      = 2
+
+
+@dataclass
+class Track:
+    track_id:   int
+    kalman:     KalmanTracker
+    state:      TrackState = TrackState.TENTATIVE
+    hits:       int = 1
+    age:        int = 1      # frames since creation
+    missed:     int = 0      # consecutive missed frames
+
+
+class TrackerManager:
+    """
+    Manages a pool of Kalman tracks.
+
+    Parameters
+    ----------
+    max_tracks      : hard cap on simultaneously alive tracks
+    confirm_frames  : hits needed before a track is CONFIRMED
+    max_missed      : consecutive missed frames before a track is DEAD
+    assoc_dist_m    : max allowed distance (m) for a valid assignment
+    horizon_s       : prediction horizon for trajectory output (seconds)
+    pred_step_s     : time step between predicted waypoints
+    process_noise   : KalmanTracker process-noise multiplier
+    """
+
+    def __init__(
+        self,
+        max_tracks:     int   = 20,
+        confirm_frames: int   = 3,
+        max_missed:     int   = 6,
+        assoc_dist_m:   float = 1.5,
+        horizon_s:      float = 2.5,
+        pred_step_s:    float = 0.5,
+        process_noise:  float = 1.0,
+    ):
+        self._max_tracks     = max_tracks
+        self._confirm_frames = confirm_frames
+        self._max_missed     = max_missed
+        self._assoc_dist     = assoc_dist_m
+        self._horizon_s      = horizon_s
+        self._pred_step      = pred_step_s
+        self._proc_noise     = process_noise
+
+        self._tracks:  Dict[int, Track] = {}
+        self._next_id: int = 1
+
+    # ── Public API ────────────────────────────────────────────────────────────
+
+    def update(self, detections: List[Detection], dt: float) -> List[Track]:
+        """
+        Process one frame of detections.
+
+        1. Predict all tracks by dt.
+        2. Hungarian assignment of predictions → detections.
+        3. Update matched tracks; mark unmatched tracks as LOST.
+        4. Promote tracks crossing `confirm_frames`.
+        5. Create new tracks for unmatched detections (if room).
+        6. Remove DEAD tracks.
+
+        Returns confirmed tracks only.
+        """
+        # 1. Predict
+        for tr in self._tracks.values():
+            tr.kalman.predict(dt)
+            tr.age += 1
+
+        # 2. Assign
+        matched, unmatched_tracks, unmatched_dets = self._assign(detections)
+
+        # 3a. Update matched
+        for tid, did in matched:
+            tr = self._tracks[tid]
+            det = detections[did]
+            tr.kalman.update(np.array([det.x, det.y]))
+            tr.hits   += 1
+            tr.missed  = 0
+            if tr.state == TrackState.LOST:
+                tr.state = TrackState.CONFIRMED
+            elif tr.state == TrackState.TENTATIVE and tr.hits >= self._confirm_frames:
+                tr.state = TrackState.CONFIRMED
+
+        # 3b. Unmatched tracks
+        for tid in unmatched_tracks:
+            tr = self._tracks[tid]
+            tr.missed += 1
+            if tr.missed > 1:
+                tr.state = TrackState.LOST
+
+        # 4. New tracks for unmatched detections
+        live = sum(1 for t in self._tracks.values() if t.state != TrackState.LOST
+                   or t.missed <= self._max_missed)
+        for did in unmatched_dets:
+            if live >= self._max_tracks:
+                break
+            det = detections[did]
+            init_state = (TrackState.CONFIRMED
+                          if self._confirm_frames <= 1
+                          else TrackState.TENTATIVE)
+            new_tr = Track(
+                track_id=self._next_id,
+                kalman=KalmanTracker(det.x, det.y, self._proc_noise),
+                state=init_state,
+            )
+            self._tracks[self._next_id] = new_tr
+            self._next_id += 1
+            live += 1
+
+        # 5. Prune dead
+        dead = [tid for tid, t in self._tracks.items() if t.missed > self._max_missed]
+        for tid in dead:
+            del self._tracks[tid]
+
+        return [t for t in self._tracks.values() if t.state == TrackState.CONFIRMED]
+
+    @property
+    def all_tracks(self) -> List[Track]:
+        return list(self._tracks.values())
+
+    @property
+    def tentative_count(self) -> int:
+        return sum(1 for t in self._tracks.values()
+                   if t.state == TrackState.TENTATIVE)
+
+    def reset(self) -> None:
+        self._tracks.clear()
+        self._next_id = 1
+
+    # ── Hungarian assignment ──────────────────────────────────────────────────
+
+    def _assign(
+        self,
+        detections: List[Detection],
+    ) -> Tuple[List[Tuple[int, int]], List[int], List[int]]:
+        """
+        Returns:
+          matched        — list of (track_id, det_index)
+          unmatched_tids — track IDs with no detection assigned
+          unmatched_dids — detection indices with no track assigned
+        """
+        track_ids = list(self._tracks.keys())
+
+        if not track_ids or not detections:
+            return [], track_ids, list(range(len(detections)))
+
+        # Build cost matrix: rows=tracks, cols=detections
+        cost = np.full((len(track_ids), len(detections)), fill_value=np.inf)
+        for r, tid in enumerate(track_ids):
+            tx, ty = self._tracks[tid].kalman.position
+            for c, det in enumerate(detections):
+                cost[r, c] = np.hypot(tx - det.x, ty - det.y)
+
+        row_ind, col_ind = linear_sum_assignment(cost)
+
+        matched:          List[Tuple[int, int]] = []
+        matched_track_idx: set = set()
+        matched_det_idx:   set = set()
+
+        for r, c in zip(row_ind, col_ind):
+            if cost[r, c] > self._assoc_dist:
+                continue
+            matched.append((track_ids[r], c))
+            matched_track_idx.add(r)
+            matched_det_idx.add(c)
+
+        unmatched_tids = [track_ids[r] for r in range(len(track_ids))
+                          if r not in matched_track_idx]
+        unmatched_dids = [c for c in range(len(detections))
+                          if c not in matched_det_idx]
+
+        return matched, unmatched_tids, unmatched_dids

jetson/ros2_ws/src/saltybot_dynamic_obstacles/setup.cfg

@@ -0,0 +1,4 @@
+[develop]
+script_dir=$base/lib/saltybot_dynamic_obstacles
+[install]
+install_scripts=$base/lib/saltybot_dynamic_obstacles

jetson/ros2_ws/src/saltybot_dynamic_obstacles/setup.py

@@ -0,0 +1,32 @@
+from setuptools import setup, find_packages
+from glob import glob
+
+package_name = 'saltybot_dynamic_obstacles'
+
+setup(
+    name=package_name,
+    version='0.1.0',
+    packages=find_packages(exclude=['test']),
+    data_files=[
+        ('share/ament_index/resource_index/packages',
+            ['resource/' + package_name]),
+        ('share/' + package_name, ['package.xml']),
+        ('share/' + package_name + '/launch',
+            glob('launch/*.launch.py')),
+        ('share/' + package_name + '/config',
+            glob('config/*.yaml')),
+    ],
+    install_requires=['setuptools'],
+    zip_safe=True,
+    maintainer='SaltyLab',
+    maintainer_email='robot@saltylab.local',
+    description='Dynamic obstacle tracking: LIDAR motion detection, Kalman tracking, Nav2 costmap',
+    license='MIT',
+    tests_require=['pytest'],
+    entry_points={
+        'console_scripts': [
+            'dynamic_obs_tracker = saltybot_dynamic_obstacles.dynamic_obs_node:main',
+            'dynamic_obs_costmap = saltybot_dynamic_obstacles.costmap_layer_node:main',
+        ],
+    },
+)

jetson/ros2_ws/src/saltybot_dynamic_obstacles/test/test_dynamic_obstacles.py

@@ -0,0 +1,262 @@
+"""
+test_dynamic_obstacles.py — Unit tests for KalmanTracker, TrackerManager,
+and ObjectDetector.
+
+Runs without ROS2 / hardware (no rclpy imports).
+"""
+
+from __future__ import annotations
+
+import math
+import sys
+import os
+
+import numpy as np
+import pytest
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
+
+from saltybot_dynamic_obstacles.kalman_tracker import KalmanTracker
+from saltybot_dynamic_obstacles.tracker_manager import TrackerManager, TrackState
+from saltybot_dynamic_obstacles.object_detector import ObjectDetector, Detection
+
+
+# ── KalmanTracker ─────────────────────────────────────────────────────────────
+
+class TestKalmanTracker:
+
+    def test_initial_position(self):
+        kt = KalmanTracker(3.0, 4.0)
+        px, py = kt.position
+        assert px == pytest.approx(3.0)
+        assert py == pytest.approx(4.0)
+
+    def test_initial_velocity_zero(self):
+        kt = KalmanTracker(0.0, 0.0)
+        vx, vy = kt.velocity
+        assert vx == pytest.approx(0.0)
+        assert vy == pytest.approx(0.0)
+
+    def test_predict_moves_position(self):
+        kt = KalmanTracker(0.0, 0.0)
+        # Give it some velocity via update sequence
+        kt.update(np.array([0.1, 0.0]))
+        kt.update(np.array([0.2, 0.0]))
+        kt.predict(0.1)
+        px, _ = kt.position
+        assert px > 0.0   # should have moved forward
+
+    def test_pure_predict_constant_velocity(self):
+        """After velocity is established, predict() should move linearly."""
+        kt = KalmanTracker(0.0, 0.0)
+        # Force velocity by repeated updates
+        for i in range(10):
+            kt.update(np.array([i * 0.1, 0.0]))
+            kt.predict(0.1)
+        vx, _ = kt.velocity
+        px0, _ = kt.position
+        kt.predict(1.0)
+        px1, _ = kt.position
+        # Should advance roughly vx * 1.0 metres
+        assert px1 == pytest.approx(px0 + vx * 1.0, abs=0.3)
+
+    def test_update_corrects_position(self):
+        kt = KalmanTracker(0.0, 0.0)
+        # Predict way off
+        kt.predict(10.0)
+        # Then update to ground truth
+        kt.update(np.array([1.0, 2.0]))
+        px, py = kt.position
+        # Should move toward (1, 2)
+        assert px == pytest.approx(1.0, abs=0.5)
+        assert py == pytest.approx(2.0, abs=0.5)
+
+    def test_predict_horizon_length(self):
+        kt = KalmanTracker(0.0, 0.0)
+        preds = kt.predict_horizon(horizon_s=2.5, step_s=0.5)
+        assert len(preds) == 5   # 0.5, 1.0, 1.5, 2.0, 2.5
+
+    def test_predict_horizon_times(self):
+        kt = KalmanTracker(0.0, 0.0)
+        preds = kt.predict_horizon(horizon_s=2.0, step_s=0.5)
+        times = [t for _, _, t in preds]
+        assert times == pytest.approx([0.5, 1.0, 1.5, 2.0], abs=0.01)
+
+    def test_predict_horizon_does_not_mutate_state(self):
+        kt = KalmanTracker(1.0, 2.0)
+        kt.predict_horizon(horizon_s=3.0, step_s=0.5)
+        px, py = kt.position
+        assert px == pytest.approx(1.0)
+        assert py == pytest.approx(2.0)
+
+    def test_speed_zero_at_init(self):
+        kt = KalmanTracker(5.0, 5.0)
+        assert kt.speed == pytest.approx(0.0)
+
+    def test_covariance_shape(self):
+        kt = KalmanTracker(0.0, 0.0)
+        cov = kt.covariance_2x2
+        assert cov.shape == (2, 2)
+
+    def test_covariance_positive_definite(self):
+        kt = KalmanTracker(0.0, 0.0)
+        for _ in range(5):
+            kt.predict(0.1)
+            kt.update(np.array([0.1, 0.0]))
+        eigvals = np.linalg.eigvalsh(kt.covariance_2x2)
+        assert np.all(eigvals > 0)
+
+    def test_joseph_form_stays_symmetric(self):
+        """Covariance should remain symmetric after many updates."""
+        kt = KalmanTracker(0.0, 0.0)
+        for i in range(50):
+            kt.predict(0.1)
+            kt.update(np.array([i * 0.01, 0.0]))
+        P = kt._P
+        assert np.allclose(P, P.T, atol=1e-10)
+
+
+# ── TrackerManager ────────────────────────────────────────────────────────────
+
+class TestTrackerManager:
+
+    def _det(self, x, y):
+        return Detection(x=x, y=y, size_m2=0.1, range_m=math.hypot(x, y))
+
+    def test_empty_detections_no_tracks(self):
+        tm = TrackerManager()
+        confirmed = tm.update([], 0.1)
+        assert confirmed == []
+
+    def test_track_created_on_detection(self):
+        tm = TrackerManager(confirm_frames=1)
+        confirmed = tm.update([self._det(1.0, 0.0)], 0.1)
+        assert len(confirmed) == 1
+
+    def test_track_tentative_before_confirm(self):
+        tm = TrackerManager(confirm_frames=3)
+        tm.update([self._det(1.0, 0.0)], 0.1)
+        # Only 1 hit — should still be tentative
+        assert tm.tentative_count == 1
+
+    def test_track_confirmed_after_N_hits(self):
+        tm = TrackerManager(confirm_frames=3, assoc_dist_m=2.0)
+        for _ in range(4):
+            confirmed = tm.update([self._det(1.0, 0.0)], 0.1)
+        assert len(confirmed) == 1
+
+    def test_track_deleted_after_max_missed(self):
+        tm = TrackerManager(confirm_frames=1, max_missed=3, assoc_dist_m=2.0)
+        tm.update([self._det(1.0, 0.0)], 0.1)   # create
+        for _ in range(5):
+            tm.update([], 0.1)   # no detections → missed++
+        assert len(tm.all_tracks) == 0
+
+    def test_max_tracks_cap(self):
+        tm = TrackerManager(max_tracks=5, confirm_frames=1)
+        dets = [self._det(float(i), 0.0) for i in range(10)]
+        tm.update(dets, 0.1)
+        assert len(tm.all_tracks) <= 5
+
+    def test_consistent_track_id(self):
+        tm = TrackerManager(confirm_frames=3, assoc_dist_m=1.5)
+        for i in range(5):
+            confirmed = tm.update([self._det(1.0 + i * 0.01, 0.0)], 0.1)
+        assert len(confirmed) == 1
+        track_id = confirmed[0].track_id
+        # One more tick — ID should be stable
+        confirmed2 = tm.update([self._det(1.06, 0.0)], 0.1)
+        assert confirmed2[0].track_id == track_id
+
+    def test_two_independent_tracks(self):
+        tm = TrackerManager(confirm_frames=3, assoc_dist_m=0.8)
+        for _ in range(5):
+            confirmed = tm.update([self._det(1.0, 0.0), self._det(5.0, 0.0)], 0.1)
+        assert len(confirmed) == 2
+
+    def test_reset_clears_all(self):
+        tm = TrackerManager(confirm_frames=1)
+        tm.update([self._det(1.0, 0.0)], 0.1)
+        tm.reset()
+        assert len(tm.all_tracks) == 0
+
+    def test_far_detection_not_assigned(self):
+        tm = TrackerManager(confirm_frames=1, assoc_dist_m=0.5)
+        tm.update([self._det(1.0, 0.0)], 0.1)   # create track at (1,0)
+        # Detection 3 m away → new track, not update
+        tm.update([self._det(4.0, 0.0)], 0.1)
+        assert len(tm.all_tracks) == 2
+
+
+# ── ObjectDetector ────────────────────────────────────────────────────────────
+
+class TestObjectDetector:
+
+    def _empty_scan(self, n=360, rmax=8.0) -> tuple:
+        """All readings at max range (static background)."""
+        ranges = np.full(n, rmax - 0.1, dtype=np.float32)
+        return ranges, -math.pi, 2 * math.pi / n, rmax
+
+    def _scan_with_blob(self, blob_r=2.0, blob_theta=0.0, n=360, rmax=8.0) -> tuple:
+        """Background scan + a short-range cluster at blob_theta."""
+        ranges = np.full(n, rmax - 0.1, dtype=np.float32)
+        angle_inc = 2 * math.pi / n
+        angle_min = -math.pi
+        # Put a cluster of ~10 beams at blob_r
+        center_idx = int((blob_theta - angle_min) / angle_inc) % n
+        for di in range(-5, 6):
+            idx = (center_idx + di) % n
+            ranges[idx] = blob_r
+        return ranges, angle_min, angle_inc, rmax
+
+    def test_empty_scan_no_detections_after_warmup(self):
+        od = ObjectDetector()
+        r, a_min, a_inc, rmax = self._empty_scan()
+        od.update(r, a_min, a_inc, rmax)   # init background
+        for _ in range(3):
+            dets = od.update(r, a_min, a_inc, rmax)
+        assert len(dets) == 0
+
+    def test_moving_blob_detected(self):
+        od = ObjectDetector()
+        bg_r, a_min, a_inc, rmax = self._empty_scan()
+        od.update(bg_r, a_min, a_inc, rmax)   # seed background
+        for _ in range(5):
+            od.update(bg_r, a_min, a_inc, rmax)
+        # Now inject a foreground blob
+        fg_r, _, _, _ = self._scan_with_blob(blob_r=2.0, blob_theta=0.0)
+        dets = od.update(fg_r, a_min, a_inc, rmax)
+        assert len(dets) >= 1
+
+    def test_detection_centroid_approximate(self):
+        od = ObjectDetector()
+        bg_r, a_min, a_inc, rmax = self._empty_scan()
+        for _ in range(8):
+            od.update(bg_r, a_min, a_inc, rmax)
+        fg_r, _, _, _ = self._scan_with_blob(blob_r=3.0, blob_theta=0.0)
+        dets = od.update(fg_r, a_min, a_inc, rmax)
+        assert len(dets) >= 1
+        # Blob is at ~3 m along x-axis (theta=0)
+        cx = dets[0].x
+        cy = dets[0].y
+        assert abs(cx - 3.0) < 0.5
+        assert abs(cy) < 0.5
+
+    def test_reset_clears_background(self):
+        od = ObjectDetector()
+        bg_r, a_min, a_inc, rmax = self._empty_scan()
+        for _ in range(5):
+            od.update(bg_r, a_min, a_inc, rmax)
+        od.reset()
+        assert not od._initialized
+
+    def test_no_inf_nan_ranges(self):
+        od = ObjectDetector()
+        r = np.array([np.inf, np.nan, 5.0, -1.0, 0.0] * 72, dtype=np.float32)
+        a_min = -math.pi
+        a_inc = 2 * math.pi / len(r)
+        od.update(r, a_min, a_inc, 8.0)   # should not raise
+
+
+if __name__ == '__main__':
+    pytest.main([__file__, '-v'])