Merge pull request 'feat: RealSense obstacle detection (Issue #611)' (#623) from sl-perception/issue-611-obstacle-detect into main

jetson/ros2_ws/src/saltybot_obstacle_detect/config/obstacle_detect_params.yaml

@@ -0,0 +1,35 @@
+obstacle_detect:
+  ros__parameters:
+    # ── Depth processing ──────────────────────────────────────────────────────
+    downsample_factor:    8       # stride-based downsample before processing
+                                  # 640x480 / 8 = 80x60 = 4800 pts (fast RANSAC)
+
+    # ── RANSAC ground plane fitting ───────────────────────────────────────────
+    ransac_n_iter:        50      # RANSAC iterations
+    ransac_inlier_m:      0.06    # inlier distance threshold (m)
+
+    # ── Obstacle height filter ────────────────────────────────────────────────
+    min_obstacle_h_m:     0.05    # min height above ground to count as obstacle (m)
+                                  #   — avoids false positives from ground noise
+    max_obstacle_h_m:     0.80    # max height above ground to count as obstacle (m)
+                                  #   — ignores ceiling / upper structure (D435i ~0.5m mount)
+
+    # ── Clustering ────────────────────────────────────────────────────────────
+    cluster_cell_m:       0.30    # 2D grid cell size for BFS clustering (m)
+    cluster_min_pts:      5       # minimum points for a cluster to be reported
+    max_obstacle_dist_m:  5.0     # discard obstacle points beyond this forward distance
+
+    # ── Alert / e-stop thresholds ─────────────────────────────────────────────
+    danger_dist_m:        0.40    # closest obstacle z < this → DANGER alert
+    warn_dist_m:          1.20    # closest obstacle z < this → WARN alert
+
+    # ── Safety zone integration ───────────────────────────────────────────────
+    # When enabled, DANGER obstacles publish zero Twist to depth_estop_topic.
+    # Wire this into the cmd_vel safety chain so safety_zone can latch e-stop.
+    # Typical chain:  depth_estop_topic=/cmd_vel_input  →  safety_zone  →  /cmd_vel
+    depth_estop_enabled:  false
+    depth_estop_topic:    /cmd_vel_input
+
+    # ── Frame / topic configuration ───────────────────────────────────────────
+    camera_frame:         camera_link    # frame_id for MarkerArray and PointCloud2
+    marker_lifetime_s:    0.20           # Marker.lifetime — 0.2 s = 6 frames at 30 Hz

jetson/ros2_ws/src/saltybot_obstacle_detect/launch/obstacle_detect.launch.py

@@ -0,0 +1,29 @@
+"""obstacle_detect.launch.py — RealSense depth obstacle detection (Issue #611)."""
+
+import os
+from ament_index_python.packages import get_package_share_directory
+from launch import LaunchDescription
+from launch.actions import DeclareLaunchArgument
+from launch.substitutions import LaunchConfiguration
+from launch_ros.actions import Node
+
+
+def generate_launch_description():
+    pkg_share = get_package_share_directory('saltybot_obstacle_detect')
+    default_params = os.path.join(pkg_share, 'config', 'obstacle_detect_params.yaml')
+
+    params_arg = DeclareLaunchArgument(
+        'params_file',
+        default_value=default_params,
+        description='Path to obstacle_detect_params.yaml',
+    )
+
+    obstacle_detect_node = Node(
+        package='saltybot_obstacle_detect',
+        executable='obstacle_detect',
+        name='obstacle_detect',
+        output='screen',
+        parameters=[LaunchConfiguration('params_file')],
+    )
+
+    return LaunchDescription([params_arg, obstacle_detect_node])

jetson/ros2_ws/src/saltybot_obstacle_detect/package.xml

@@ -0,0 +1,36 @@
+<?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_obstacle_detect</name>
+  <version>0.1.0</version>
+  <description>
+    RealSense D435i depth-based obstacle detection node (Issue #611).
+    RANSAC ground-plane fitting, 2D grid-BFS obstacle clustering, publishes
+    MarkerArray centroids (/saltybot/obstacles), PointCloud2 of non-ground
+    points (/saltybot/obstacles/cloud), and JSON alert (/saltybot/obstacles/alert)
+    with safety_zone e-stop integration via configurable Twist output.
+  </description>
+  <maintainer email="sl-perception@saltylab.local">sl-perception</maintainer>
+  <license>MIT</license>
+
+  <buildtool_depend>ament_python</buildtool_depend>
+
+  <depend>rclpy</depend>
+  <depend>std_msgs</depend>
+  <depend>sensor_msgs</depend>
+  <depend>geometry_msgs</depend>
+  <depend>visualization_msgs</depend>
+  <depend>cv_bridge</depend>
+
+  <exec_depend>python3-numpy</exec_depend>
+  <exec_depend>python3-opencv</exec_depend>
+
+  <test_depend>ament_copyright</test_depend>
+  <test_depend>ament_flake8</test_depend>
+  <test_depend>ament_pep257</test_depend>
+  <test_depend>python3-pytest</test_depend>
+
+  <export>
+    <build_type>ament_python</build_type>
+  </export>
+</package>

jetson/ros2_ws/src/saltybot_obstacle_detect/saltybot_obstacle_detect/ground_plane.py

@@ -0,0 +1,192 @@
+"""
+ground_plane.py — RANSAC ground plane fitting for D435i depth images (Issue #611).
+
+No ROS2 dependencies — pure Python + numpy, fully unit-testable.
+
+Algorithm
+─────────
+RANSAC over 3D point cloud in camera optical frame:
+  +X = right, +Y = down, +Z = forward (depth)
+
+1. Sample 3 random non-collinear points.
+2. Fit plane: normal n, offset d  (n·p = d, ‖n‖=1).
+3. Count inliers: |n·p - d| < inlier_thresh_m.
+4. Repeat n_iter times; keep plane with most inliers.
+5. Optionally refine: refit normal via SVD on inlier subset.
+
+Normal orientation:
+  After RANSAC the normal is flipped so it points in the −Y direction
+  (upward in world / toward the camera). This makes "height above ground"
+  unambiguous: h = d − n·p > 0 means the point is above the plane.
+
+  Equivalently: normal.y < 0 after orientation.
+
+Height above ground:
+  h(p) = d − n·p
+
+  h > 0 → above plane (obstacle candidate)
+  h ≈ 0 → on the ground
+  h < 0 → below ground (artefact / noise)
+
+Obstacle filter:
+  min_obstacle_h < h < max_obstacle_h   (configurable, metres)
+
+  Typical D435i camera mount ~0.5 m above ground:
+    min_obstacle_h = 0.05 m  (ignore ground noise)
+    max_obstacle_h = 0.80 m  (ignore ceiling / upper structure)
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Optional, Tuple
+
+import numpy as np
+
+
+PlaneParams = Tuple[np.ndarray, float]   # (normal_3, d_scalar)
+
+
+# ── RANSAC ─────────────────────────────────────────────────────────────────────
+
+def fit_ground_plane(
+    points:          np.ndarray,
+    n_iter:          int   = 50,
+    inlier_thresh_m: float = 0.06,
+    min_inlier_frac: float = 0.30,
+    refine:          bool  = True,
+) -> Optional[PlaneParams]:
+    """
+    RANSAC ground-plane estimation from an (N, 3) point array.
+
+    Parameters
+    ----------
+    points          : (N, 3) float32/64 point cloud in camera optical frame
+    n_iter          : RANSAC iterations
+    inlier_thresh_m : inlier distance threshold (m)
+    min_inlier_frac : minimum fraction of points that must be inliers
+    refine          : if True, refit plane via SVD on best inlier set
+
+    Returns
+    -------
+    (normal, d) if a plane was found, else None.
+    Normal is oriented so that normal.y < 0  (points upward in world).
+    """
+    n = len(points)
+    if n < 10:
+        return None
+
+    rng          = np.random.default_rng(seed=42)
+    best_n_in    = -1
+    best_params: Optional[PlaneParams] = None
+    pts          = points.astype(np.float64)
+
+    for _ in range(n_iter):
+        idx = rng.integers(0, n, size=3)
+        p0, p1, p2 = pts[idx[0]], pts[idx[1]], pts[idx[2]]
+
+        edge1 = p1 - p0
+        edge2 = p2 - p0
+        normal = np.cross(edge1, edge2)
+        norm_len = float(np.linalg.norm(normal))
+        if norm_len < 1e-8:
+            continue
+
+        normal /= norm_len
+        d = float(np.dot(normal, p0))
+
+        dists   = np.abs(pts @ normal - d)
+        inliers = dists < inlier_thresh_m
+        n_in    = int(inliers.sum())
+
+        if n_in > best_n_in:
+            best_n_in = n_in
+            best_params = (normal.copy(), d)
+
+    if best_params is None:
+        return None
+    if best_n_in < int(n * min_inlier_frac):
+        return None
+
+    normal, d = best_params
+
+    # ── SVD refinement ──────────────────────────────────────────────────────
+    if refine:
+        dists   = np.abs(pts @ normal - d)
+        in_mask = dists < inlier_thresh_m
+        if in_mask.sum() >= 4:
+            in_pts = pts[in_mask]
+            centroid = in_pts.mean(axis=0)
+            _, _, Vt = np.linalg.svd(in_pts - centroid)
+            normal = Vt[-1]               # smallest singular value = plane normal
+            normal /= float(np.linalg.norm(normal))
+            d = float(np.dot(normal, centroid))
+
+    # Orient normal upward: ensure n.y < 0 (−Y = up in camera frame)
+    if normal[1] > 0:
+        normal = -normal
+        d      = -d
+
+    return normal, d
+
+
+# ── Point classification ───────────────────────────────────────────────────────
+
+def height_above_plane(
+    points: np.ndarray,
+    plane:  PlaneParams,
+) -> np.ndarray:
+    """
+    Signed height of each point above the ground plane.
+
+    h > 0  → above ground (potential obstacle)
+    h ≈ 0  → on ground
+    h < 0  → subsurface artefact / sensor noise
+
+    Parameters
+    ----------
+    points : (N, 3) point cloud
+    plane  : (normal, d) from fit_ground_plane()
+
+    Returns
+    -------
+    heights : (N,) float64
+    """
+    normal, d = plane
+    # h = d - n·p  (positive when point is "above" the upward-facing normal)
+    return d - (points.astype(np.float64) @ normal)
+
+
+def obstacle_mask(
+    points:             np.ndarray,
+    plane:              PlaneParams,
+    min_height_m:       float = 0.05,
+    max_height_m:       float = 0.80,
+) -> np.ndarray:
+    """
+    Boolean mask: True where a point is an obstacle (above ground, in height window).
+
+    Parameters
+    ----------
+    points       : (N, 3) point cloud
+    plane        : ground plane params
+    min_height_m : minimum height above ground to count as obstacle
+    max_height_m : maximum height above ground (ceiling / upper structure cut-off)
+
+    Returns
+    -------
+    mask : (N,) bool
+    """
+    h = height_above_plane(points, plane)
+    return (h > min_height_m) & (h < max_height_m)
+
+
+def ground_mask(
+    points:         np.ndarray,
+    plane:          PlaneParams,
+    inlier_thresh:  float = 0.06,
+) -> np.ndarray:
+    """Boolean mask: True for ground-plane inlier points."""
+    normal, d = plane
+    dists = np.abs(points.astype(np.float64) @ normal - d)
+    return dists < inlier_thresh

jetson/ros2_ws/src/saltybot_obstacle_detect/saltybot_obstacle_detect/obstacle_clusterer.py

@@ -0,0 +1,170 @@
+"""
+obstacle_clusterer.py — 2D grid-based obstacle clustering (Issue #611).
+
+No ROS2 dependencies — pure Python + numpy, fully unit-testable.
+
+Algorithm
+─────────
+After RANSAC ground removal, obstacle points (non-ground) are clustered
+using a 2D occupancy grid on the horizontal (X–Z) plane in camera frame.
+
+Steps:
+  1. Project obstacle 3D points onto the (X, Z) bird's-eye plane.
+  2. Discretise into grid cells of side `cell_m`.
+  3. 4-connected BFS flood-fill to find connected components.
+  4. For each component with ≥ min_pts 3D points:
+       centroid = mean of the component's 3D points
+       radius   = max distance from centroid (bounding-sphere radius)
+
+Camera frame convention:
+  +X = right, +Y = down (not used for 2D projection), +Z = forward (depth)
+
+Output per cluster
+──────────────────
+  ObstacleCluster (dataclass):
+    centroid  : (3,) float array  — (cx, cy, cz) in camera frame
+    radius_m  : float             — bounding-sphere radius (m)
+    height_m  : float             — mean height above ground plane (m)
+    n_pts     : int               — number of 3D points in cluster
+"""
+
+from __future__ import annotations
+
+from collections import deque
+from dataclasses import dataclass, field
+from typing import List, Tuple
+
+import numpy as np
+
+
+@dataclass
+class ObstacleCluster:
+    centroid: np.ndarray          # (3,) float64 in camera optical frame
+    radius_m: float
+    height_m: float
+    n_pts:    int
+
+    @property
+    def distance_m(self) -> float:
+        """Forward distance (Z in camera frame)."""
+        return float(self.centroid[2])
+
+    @property
+    def lateral_m(self) -> float:
+        """Lateral offset (X in camera frame; positive = right)."""
+        return float(self.centroid[0])
+
+
+def cluster_obstacles(
+    points:    np.ndarray,
+    heights:   np.ndarray,
+    cell_m:    float = 0.30,
+    min_pts:   int   = 5,
+    x_range:   Tuple[float, float] = (-4.0, 4.0),
+    z_range:   Tuple[float, float] = (0.15, 6.0),
+) -> List[ObstacleCluster]:
+    """
+    Cluster obstacle points into connected groups.
+
+    Parameters
+    ----------
+    points   : (N, 3) float array — obstacle points in camera frame
+    heights  : (N,)  float array — height above ground for each point
+    cell_m   : grid cell side length (m)
+    min_pts  : minimum points for a cluster to be reported
+    x_range  : (min_x, max_x) bounds for the bird's-eye grid (m)
+    z_range  : (min_z, max_z) forward-distance bounds (m)
+
+    Returns
+    -------
+    clusters : list of ObstacleCluster, sorted by ascending distance (z)
+    """
+    if len(points) == 0:
+        return []
+
+    pts = points.astype(np.float64)
+
+    # ── Discard out-of-range points ─────────────────────────────────────────
+    x = pts[:, 0]
+    z = pts[:, 2]
+    valid = (
+        (x >= x_range[0]) & (x <= x_range[1]) &
+        (z >= z_range[0]) & (z <= z_range[1])
+    )
+    pts     = pts[valid]
+    heights = heights.astype(np.float64)[valid]
+
+    if len(pts) == 0:
+        return []
+
+    # ── Build 2D occupancy grid (X, Z) ──────────────────────────────────────
+    x_min, x_max = x_range
+    z_min, z_max = z_range
+    n_x = max(1, int(math.ceil((x_max - x_min) / cell_m)))
+    n_z = max(1, int(math.ceil((z_max - z_min) / cell_m)))
+
+    xi = np.clip(((pts[:, 0] - x_min) / cell_m).astype(np.int32), 0, n_x - 1)
+    zi = np.clip(((pts[:, 2] - z_min) / cell_m).astype(np.int32), 0, n_z - 1)
+
+    # Map each grid cell to list of 3D-point indices
+    cell_pts: dict[Tuple[int, int], list] = {}
+    for idx, (gx, gz) in enumerate(zip(xi.tolist(), zi.tolist())):
+        key = (gx, gz)
+        if key not in cell_pts:
+            cell_pts[key] = []
+        cell_pts[key].append(idx)
+
+    occupied = set(cell_pts.keys())
+
+    # ── BFS connected-component labelling ────────────────────────────────────
+    visited  = set()
+    clusters = []
+
+    for start in occupied:
+        if start in visited:
+            continue
+
+        # BFS flood fill
+        component_cells = []
+        queue = deque([start])
+        visited.add(start)
+
+        while queue:
+            cx, cz = queue.popleft()
+            component_cells.append((cx, cz))
+
+            for dx, dz in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
+                nb = (cx + dx, cz + dz)
+                if nb in occupied and nb not in visited:
+                    visited.add(nb)
+                    queue.append(nb)
+
+        # Collect 3D points in this component
+        pt_indices = []
+        for cell in component_cells:
+            pt_indices.extend(cell_pts[cell])
+
+        if len(pt_indices) < min_pts:
+            continue
+
+        cluster_pts    = pts[pt_indices]
+        cluster_h      = heights[pt_indices]
+        centroid       = cluster_pts.mean(axis=0)
+        dists          = np.linalg.norm(cluster_pts - centroid, axis=1)
+        radius_m       = float(dists.max())
+        mean_height    = float(cluster_h.mean())
+
+        clusters.append(ObstacleCluster(
+            centroid = centroid,
+            radius_m = radius_m,
+            height_m = mean_height,
+            n_pts    = len(pt_indices),
+        ))
+
+    # Sort by forward distance
+    clusters.sort(key=lambda c: c.distance_m)
+    return clusters
+
+
+# ── Local import for math ─────────────────────────────────────────────────────
+import math

jetson/ros2_ws/src/saltybot_obstacle_detect/saltybot_obstacle_detect/obstacle_detect_node.py

@@ -0,0 +1,512 @@
+"""
+obstacle_detect_node.py — RealSense depth obstacle detection node (Issue #611).
+
+Pipeline per frame (30 Hz)
+──────────────────────────
+1. Receive D435i aligned depth image (float32, metres) + camera_info.
+2. Downsample by `downsample_factor` for efficiency.
+3. Back-project non-zero pixels to 3D (camera optical frame, +Z forward).
+4. RANSAC ground-plane fitting on the 3D cloud.
+5. Classify points: ground (inlier) vs obstacle (above ground, in height window).
+6. Cluster obstacle points using 2D grid BFS on the (X, Z) plane.
+7. Publish:
+     /saltybot/obstacles          visualization_msgs/MarkerArray  (centroids)
+     /saltybot/obstacles/cloud    sensor_msgs/PointCloud2         (non-ground pts)
+     /saltybot/obstacles/alert    std_msgs/String                 (JSON alert)
+8. When `depth_estop_enabled` and DANGER detected: publish zero Twist to
+   `depth_estop_topic` (default /cmd_vel_input) for safety_zone integration.
+
+Obstacle alert levels (JSON on /saltybot/obstacles/alert)
+──────────────────────────────────────────────────────────
+  DANGER  — closest obstacle z < danger_dist_m  (requires immediate stop)
+  WARN    — closest obstacle z < warn_dist_m    (caution)
+  CLEAR   — no obstacles in range
+
+Safety zone integration
+───────────────────────
+When `depth_estop_enabled: true`, DANGER obstacles trigger a zero Twist
+published to `depth_estop_topic` (default: /cmd_vel_input).
+This feeds into the safety_zone's cmd_vel chain and triggers its e-stop,
+giving independent depth-based stopping complementary to the LIDAR zones.
+
+Subscribes
+──────────
+  /camera/depth/image_rect_raw    sensor_msgs/Image      30 Hz float32 (m)
+  /camera/depth/camera_info       sensor_msgs/CameraInfo (once)
+
+Publishes
+─────────
+  /saltybot/obstacles             visualization_msgs/MarkerArray
+  /saltybot/obstacles/cloud       sensor_msgs/PointCloud2
+  /saltybot/obstacles/alert       std_msgs/String   (JSON)
+  [depth_estop_topic]             geometry_msgs/Twist  (zero, when DANGER)
+
+Parameters (see config/obstacle_detect_params.yaml)
+────────────────────────────────────────────────────
+  downsample_factor    int    8       factor to reduce image before processing
+  ransac_n_iter        int    50      RANSAC iterations for ground plane
+  ransac_inlier_m      float  0.06    inlier threshold (m)
+  min_obstacle_h_m     float  0.05    min height above ground to be obstacle
+  max_obstacle_h_m     float  0.80    max height above ground to be obstacle
+  cluster_cell_m       float  0.30    clustering grid cell size (m)
+  cluster_min_pts      int    5       minimum points per cluster
+  max_obstacle_dist_m  float  5.0     discard obstacles beyond this distance
+  danger_dist_m        float  0.40    obstacle z < this → DANGER
+  warn_dist_m          float  1.20    obstacle z < this → WARN
+  depth_estop_enabled  bool   false   publish zero-vel when DANGER
+  depth_estop_topic    str    /cmd_vel_input
+  camera_frame         str    camera_link
+  marker_lifetime_s    float  0.2     MarkerArray marker lifetime (s)
+"""
+
+from __future__ import annotations
+
+import json
+import math
+import struct
+
+import rclpy
+from rclpy.node import Node
+from rclpy.qos import (
+    QoSProfile, ReliabilityPolicy, HistoryPolicy, DurabilityPolicy
+)
+
+import numpy as np
+from cv_bridge import CvBridge
+
+from geometry_msgs.msg import Point, Twist, Vector3
+from sensor_msgs.msg import Image, CameraInfo, PointCloud2, PointField
+from std_msgs.msg import Header, String, ColorRGBA
+from visualization_msgs.msg import Marker, MarkerArray
+
+from .ground_plane import fit_ground_plane, height_above_plane, obstacle_mask
+from .obstacle_clusterer import ObstacleCluster, cluster_obstacles
+
+
+# ── QoS ───────────────────────────────────────────────────────────────────────
+_SENSOR_QOS = QoSProfile(
+    reliability=ReliabilityPolicy.BEST_EFFORT,
+    history=HistoryPolicy.KEEP_LAST,
+    depth=5,
+)
+_LATCHED_QOS = QoSProfile(
+    reliability=ReliabilityPolicy.RELIABLE,
+    history=HistoryPolicy.KEEP_LAST,
+    depth=1,
+    durability=DurabilityPolicy.TRANSIENT_LOCAL,
+)
+
+# Alert levels
+CLEAR  = 'CLEAR'
+WARN   = 'WARN'
+DANGER = 'DANGER'
+
+# Marker colours per alert level
+_COLOUR = {
+    CLEAR:  (0.0, 1.0, 0.0, 0.8),   # green
+    WARN:   (1.0, 0.8, 0.0, 0.9),   # yellow
+    DANGER: (1.0, 0.1, 0.1, 1.0),   # red
+}
+
+# D435i depth scale (image is already float32 in metres when using 32FC1)
+_DEPTH_ENCODING = '32FC1'
+
+
+class ObstacleDetectNode(Node):
+    """RealSense depth-based obstacle detection — see module docstring."""
+
+    def __init__(self) -> None:
+        super().__init__('obstacle_detect')
+
+        # ── Parameters ────────────────────────────────────────────────────────
+        self.declare_parameter('downsample_factor',   8)
+        self.declare_parameter('ransac_n_iter',       50)
+        self.declare_parameter('ransac_inlier_m',     0.06)
+        self.declare_parameter('min_obstacle_h_m',    0.05)
+        self.declare_parameter('max_obstacle_h_m',    0.80)
+        self.declare_parameter('cluster_cell_m',      0.30)
+        self.declare_parameter('cluster_min_pts',     5)
+        self.declare_parameter('max_obstacle_dist_m', 5.0)
+        self.declare_parameter('danger_dist_m',       0.40)
+        self.declare_parameter('warn_dist_m',         1.20)
+        self.declare_parameter('depth_estop_enabled', False)
+        self.declare_parameter('depth_estop_topic',   '/cmd_vel_input')
+        self.declare_parameter('camera_frame',        'camera_link')
+        self.declare_parameter('marker_lifetime_s',   0.2)
+
+        self._ds          = self.get_parameter('downsample_factor').value
+        self._n_iter      = self.get_parameter('ransac_n_iter').value
+        self._inlier_m    = self.get_parameter('ransac_inlier_m').value
+        self._min_h       = self.get_parameter('min_obstacle_h_m').value
+        self._max_h       = self.get_parameter('max_obstacle_h_m').value
+        self._cell_m      = self.get_parameter('cluster_cell_m').value
+        self._min_pts     = self.get_parameter('cluster_min_pts').value
+        self._max_dist    = self.get_parameter('max_obstacle_dist_m').value
+        self._danger_m    = self.get_parameter('danger_dist_m').value
+        self._warn_m      = self.get_parameter('warn_dist_m').value
+        self._estop_en    = self.get_parameter('depth_estop_enabled').value
+        self._cam_frame   = self.get_parameter('camera_frame').value
+        self._marker_life = self.get_parameter('marker_lifetime_s').value
+
+        estop_topic = self.get_parameter('depth_estop_topic').value
+
+        # ── Internal state ────────────────────────────────────────────────────
+        self._bridge   = CvBridge()
+        self._fx: float | None = None
+        self._fy: float | None = None
+        self._cx: float | None = None
+        self._cy: float | None = None
+
+        # Ground plane stability: blend current with previous (exponential)
+        self._plane_normal: np.ndarray | None = None
+        self._plane_d:      float | None       = None
+        self._plane_alpha   = 0.3   # blend weight for new plane estimate
+
+        # ── Publishers ─────────────────────────────────────────────────────────
+        self._marker_pub = self.create_publisher(
+            MarkerArray, '/saltybot/obstacles', 10
+        )
+        self._cloud_pub = self.create_publisher(
+            PointCloud2, '/saltybot/obstacles/cloud', 10
+        )
+        self._alert_pub = self.create_publisher(
+            String, '/saltybot/obstacles/alert', 10
+        )
+        if self._estop_en:
+            self._estop_pub = self.create_publisher(
+                Twist, estop_topic, 10
+            )
+        else:
+            self._estop_pub = None
+
+        # ── Subscriptions ──────────────────────────────────────────────────────
+        self.create_subscription(
+            CameraInfo,
+            '/camera/depth/camera_info',
+            self._on_camera_info,
+            _LATCHED_QOS,
+        )
+        self.create_subscription(
+            Image,
+            '/camera/depth/image_rect_raw',
+            self._on_depth,
+            _SENSOR_QOS,
+        )
+
+        self.get_logger().info(
+            f'obstacle_detect ready — '
+            f'ds={self._ds} ransac={self._n_iter}it '
+            f'danger={self._danger_m}m warn={self._warn_m}m '
+            f'estop={"ON → " + estop_topic if self._estop_en else "OFF"}'
+        )
+
+    # ── Camera info ───────────────────────────────────────────────────────────
+
+    def _on_camera_info(self, msg: CameraInfo) -> None:
+        if self._fx is not None:
+            return
+        self._fx = float(msg.k[0])
+        self._fy = float(msg.k[4])
+        self._cx = float(msg.k[2])
+        self._cy = float(msg.k[5])
+        self.get_logger().info(
+            f'camera_info: fx={self._fx:.1f} fy={self._fy:.1f} '
+            f'cx={self._cx:.1f} cy={self._cy:.1f}'
+        )
+
+    # ── Main depth callback ───────────────────────────────────────────────────
+
+    def _on_depth(self, msg: Image) -> None:
+        if self._fx is None:
+            return  # wait for camera_info
+
+        # ── Decode depth ──────────────────────────────────────────────────────
+        try:
+            depth = self._bridge.imgmsg_to_cv2(msg, desired_encoding=_DEPTH_ENCODING)
+        except Exception as exc:
+            self.get_logger().warning(f'depth decode error: {exc}', throttle_duration_sec=5.0)
+            return
+
+        h_img, w_img = depth.shape
+        stamp  = msg.header.stamp
+
+        # ── Downsample ────────────────────────────────────────────────────────
+        ds = self._ds
+        depth_ds = depth[::ds, ::ds]   # stride-based downsample
+        h_ds, w_ds = depth_ds.shape
+
+        # Back-projection intrinsics at downsampled resolution
+        fx = self._fx / ds
+        fy = self._fy / ds
+        cx = self._cx / ds
+        cy = self._cy / ds
+
+        # ── Back-project to 3D ────────────────────────────────────────────────
+        points = _backproject(depth_ds, fx, fy, cx, cy, max_dist=self._max_dist)
+
+        if len(points) < 30:
+            self._publish_empty(stamp)
+            return
+
+        # ── RANSAC ground plane ───────────────────────────────────────────────
+        result = fit_ground_plane(
+            points,
+            n_iter          = self._n_iter,
+            inlier_thresh_m = self._inlier_m,
+        )
+
+        if result is None:
+            self._publish_empty(stamp)
+            return
+
+        new_normal, new_d = result
+
+        # Blend with previous plane for temporal stability
+        if self._plane_normal is None:
+            self._plane_normal = new_normal
+            self._plane_d      = new_d
+        else:
+            alpha = self._plane_alpha
+            blended = (1.0 - alpha) * self._plane_normal + alpha * new_normal
+            norm_len = float(np.linalg.norm(blended))
+            if norm_len > 1e-8:
+                self._plane_normal = blended / norm_len
+                self._plane_d      = (1.0 - alpha) * self._plane_d + alpha * new_d
+
+        plane = (self._plane_normal, self._plane_d)
+
+        # ── Obstacle mask ─────────────────────────────────────────────────────
+        obs_mask = obstacle_mask(points, plane, self._min_h, self._max_h)
+        obs_pts  = points[obs_mask]
+
+        heights_all = height_above_plane(points, plane)
+        obs_heights = heights_all[obs_mask]
+
+        # ── Cluster obstacles ─────────────────────────────────────────────────
+        clusters = cluster_obstacles(
+            obs_pts, obs_heights,
+            cell_m  = self._cell_m,
+            min_pts = self._min_pts,
+            z_range = (0.15, self._max_dist),
+        )
+
+        # ── Alert level ───────────────────────────────────────────────────────
+        alert_level  = CLEAR
+        closest_dist = float('inf')
+
+        for cluster in clusters:
+            d = cluster.distance_m
+            if d < closest_dist:
+                closest_dist = d
+            if d < self._danger_m:
+                alert_level = DANGER
+                break
+            if d < self._warn_m and alert_level != DANGER:
+                alert_level = WARN
+
+        if closest_dist == float('inf'):
+            closest_dist = -1.0
+
+        # ── Publish ───────────────────────────────────────────────────────────
+        self._publish_markers(clusters, stamp)
+        self._publish_cloud(obs_pts, stamp)
+        self._publish_alert(alert_level, clusters, closest_dist, stamp)
+
+        # Depth e-stop: feed zero vel to safety zone chain
+        if self._estop_en and self._estop_pub is not None and alert_level == DANGER:
+            self._estop_pub.publish(Twist())
+
+    # ── Publishers ────────────────────────────────────────────────────────────
+
+    def _publish_markers(self, clusters: list, stamp) -> None:
+        """Publish obstacle centroids as sphere MarkerArray."""
+        ma      = MarkerArray()
+        lifetime = _ros_duration(self._marker_life)
+
+        # Delete-all marker first for clean slate
+        del_marker         = Marker()
+        del_marker.action  = Marker.DELETEALL
+        del_marker.header.stamp    = stamp
+        del_marker.header.frame_id = self._cam_frame
+        ma.markers.append(del_marker)
+
+        for idx, cluster in enumerate(clusters):
+            level  = self._cluster_alert_level(cluster)
+            r, g, b, a = _COLOUR[level]
+            cx, cy, cz = cluster.centroid
+
+            m               = Marker()
+            m.header.stamp    = stamp
+            m.header.frame_id = self._cam_frame
+            m.ns              = 'obstacles'
+            m.id              = idx + 1
+            m.type            = Marker.SPHERE
+            m.action          = Marker.ADD
+            m.pose.position   = Point(x=float(cx), y=float(cy), z=float(cz))
+            m.pose.orientation.w = 1.0
+            rad = max(0.05, cluster.radius_m)
+            m.scale           = Vector3(x=rad * 2.0, y=rad * 2.0, z=rad * 2.0)
+            m.color           = ColorRGBA(r=r, g=g, b=b, a=a)
+            m.lifetime        = lifetime
+            ma.markers.append(m)
+
+            # Text label above sphere
+            lbl               = Marker()
+            lbl.header          = m.header
+            lbl.ns              = 'obstacle_labels'
+            lbl.id              = idx + 1
+            lbl.type            = Marker.TEXT_VIEW_FACING
+            lbl.action          = Marker.ADD
+            lbl.pose.position   = Point(x=float(cx), y=float(cy) - 0.15, z=float(cz))
+            lbl.pose.orientation.w = 1.0
+            lbl.scale.z         = 0.12
+            lbl.color           = ColorRGBA(r=1.0, g=1.0, b=1.0, a=0.9)
+            lbl.text            = f'{cluster.distance_m:.2f}m'
+            lbl.lifetime        = lifetime
+            ma.markers.append(lbl)
+
+        self._marker_pub.publish(ma)
+
+    def _publish_cloud(self, points: np.ndarray, stamp) -> None:
+        """Publish non-ground obstacle points as PointCloud2 (xyz float32)."""
+        if len(points) == 0:
+            # Publish empty cloud
+            cloud              = PointCloud2()
+            cloud.header.stamp    = stamp
+            cloud.header.frame_id = self._cam_frame
+            self._cloud_pub.publish(cloud)
+            return
+
+        pts_f32 = points.astype(np.float32)
+
+        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),
+        ]
+        cloud                 = PointCloud2()
+        cloud.header.stamp    = stamp
+        cloud.header.frame_id = self._cam_frame
+        cloud.height          = 1
+        cloud.width           = len(pts_f32)
+        cloud.fields          = fields
+        cloud.is_bigendian    = False
+        cloud.point_step      = 12   # 3 × float32
+        cloud.row_step        = cloud.point_step * cloud.width
+        cloud.data            = pts_f32.tobytes()
+        cloud.is_dense        = True
+        self._cloud_pub.publish(cloud)
+
+    def _publish_alert(
+        self,
+        level:       str,
+        clusters:    list,
+        closest_m:   float,
+        stamp,
+    ) -> None:
+        obstacles_json = [
+            {
+                'x':        round(float(c.centroid[0]), 3),
+                'y':        round(float(c.centroid[1]), 3),
+                'z':        round(float(c.centroid[2]), 3),
+                'radius_m': round(c.radius_m, 3),
+                'height_m': round(c.height_m, 3),
+                'n_pts':    c.n_pts,
+                'level':    self._cluster_alert_level(c),
+            }
+            for c in clusters
+        ]
+        alert = {
+            'alert_level':      level,
+            'closest_m':        round(closest_m, 3),
+            'obstacle_count':   len(clusters),
+            'obstacles':        obstacles_json,
+        }
+        msg      = String()
+        msg.data = json.dumps(alert)
+        self._alert_pub.publish(msg)
+
+    def _publish_empty(self, stamp) -> None:
+        """Publish empty state (no obstacles detected / ground plane failed)."""
+        self._publish_markers([], stamp)
+        self._publish_cloud(np.zeros((0, 3), np.float32), stamp)
+        alert = {
+            'alert_level':    CLEAR,
+            'closest_m':      -1.0,
+            'obstacle_count': 0,
+            'obstacles':      [],
+        }
+        msg      = String()
+        msg.data = json.dumps(alert)
+        self._alert_pub.publish(msg)
+
+    # ── Helpers ───────────────────────────────────────────────────────────────
+
+    def _cluster_alert_level(self, cluster: ObstacleCluster) -> str:
+        d = cluster.distance_m
+        if d < self._danger_m:
+            return DANGER
+        if d < self._warn_m:
+            return WARN
+        return CLEAR
+
+
+# ── Pure-function helpers ──────────────────────────────────────────────────────
+
+def _backproject(
+    depth:    np.ndarray,
+    fx: float, fy: float,
+    cx: float, cy: float,
+    min_dist: float = 0.15,
+    max_dist: float = 6.0,
+) -> np.ndarray:
+    """
+    Back-project a float32 depth image (metres) to a (N, 3) 3D point array.
+
+    Camera optical frame: +X right, +Y down, +Z forward.
+    Only returns pixels where depth is in [min_dist, max_dist].
+    """
+    h, w = depth.shape
+
+    # Build pixel-grid meshgrid
+    u_arr = np.arange(w, dtype=np.float32)
+    v_arr = np.arange(h, dtype=np.float32)
+    uu, vv = np.meshgrid(u_arr, v_arr)
+
+    z = depth.astype(np.float32).ravel()
+    u = uu.ravel()
+    v = vv.ravel()
+
+    valid = (z > min_dist) & (z < max_dist) & np.isfinite(z)
+    z = z[valid]
+    u = u[valid]
+    v = v[valid]
+
+    x = (u - cx) * z / fx
+    y = (v - cy) * z / fy
+
+    return np.stack([x, y, z], axis=1).astype(np.float64)
+
+
+def _ros_duration(seconds: float):
+    """Build a rclpy Duration-compatible value for Marker.lifetime."""
+    from rclpy.duration import Duration
+    sec  = int(seconds)
+    nsec = int((seconds - sec) * 1e9)
+    return Duration(seconds=sec, nanoseconds=nsec).to_msg()
+
+
+def main(args=None) -> None:
+    rclpy.init(args=args)
+    node = ObstacleDetectNode()
+    try:
+        rclpy.spin(node)
+    except KeyboardInterrupt:
+        pass
+    finally:
+        node.destroy_node()
+        rclpy.try_shutdown()
+
+
+if __name__ == '__main__':
+    main()

jetson/ros2_ws/src/saltybot_obstacle_detect/setup.cfg

@@ -0,0 +1,5 @@
+[develop]
+script_dir=$base/lib/saltybot_obstacle_detect
+
+[install]
+install_scripts=$base/lib/saltybot_obstacle_detect

jetson/ros2_ws/src/saltybot_obstacle_detect/setup.py

@@ -0,0 +1,30 @@
+import os
+from glob import glob
+from setuptools import setup
+
+package_name = 'saltybot_obstacle_detect'
+
+setup(
+    name=package_name,
+    version='0.1.0',
+    packages=[package_name],
+    data_files=[
+        ('share/ament_index/resource_index/packages',
+            ['resource/' + package_name]),
+        ('share/' + package_name, ['package.xml']),
+        (os.path.join('share', package_name, 'launch'), glob('launch/*.py')),
+        (os.path.join('share', package_name, 'config'), glob('config/*.yaml')),
+    ],
+    install_requires=['setuptools'],
+    zip_safe=True,
+    maintainer='sl-perception',
+    maintainer_email='sl-perception@saltylab.local',
+    description='RealSense depth obstacle detection — RANSAC ground + clustering (Issue #611)',
+    license='MIT',
+    tests_require=['pytest'],
+    entry_points={
+        'console_scripts': [
+            'obstacle_detect = saltybot_obstacle_detect.obstacle_detect_node:main',
+        ],
+    },
+)

jetson/ros2_ws/src/saltybot_obstacle_detect/test/test_ground_plane.py

@@ -0,0 +1,129 @@
+"""Unit tests for ground_plane.py — RANSAC fitting and point classification."""
+
+import math
+import numpy as np
+import pytest
+
+from saltybot_obstacle_detect.ground_plane import (
+    fit_ground_plane,
+    height_above_plane,
+    obstacle_mask,
+    ground_mask,
+)
+
+
+def make_flat_ground(n=500, y_ground=1.0, noise=0.02, rng=None):
+    """Flat ground plane at y = y_ground (camera frame: Y down)."""
+    if rng is None:
+        rng = np.random.default_rng(0)
+    x = rng.uniform(-2.0, 2.0, n).astype(np.float64)
+    z = rng.uniform(0.5, 4.0, n).astype(np.float64)
+    y = np.full(n, y_ground) + rng.normal(0, noise, n)
+    return np.stack([x, y, z], axis=1)
+
+
+def make_obstacle_above_ground(cx=0.0, y_ground=1.0, height=0.3, n=50, rng=None):
+    """Points forming a box above the ground plane."""
+    if rng is None:
+        rng = np.random.default_rng(1)
+    x = rng.uniform(cx - 0.15, cx + 0.15, n)
+    z = rng.uniform(1.5, 2.0, n)
+    y = np.full(n, y_ground - height)   # Y_down: smaller y = higher in world
+    return np.stack([x, y, z], axis=1)
+
+
+# ── fit_ground_plane ──────────────────────────────────────────────────────────
+
+def test_fit_flat_ground_returns_result():
+    pts   = make_flat_ground(n=400)
+    plane = fit_ground_plane(pts, n_iter=50)
+    assert plane is not None
+
+
+def test_fit_ground_normal_points_upward():
+    """Normal should have negative Y component (upward in camera frame)."""
+    pts   = make_flat_ground(n=400)
+    plane = fit_ground_plane(pts, n_iter=50)
+    assert plane is not None
+    normal, d = plane
+    assert normal[1] < 0.0, f"Expected normal.y < 0, got {normal[1]:.3f}"
+
+
+def test_fit_ground_normal_unit_vector():
+    pts   = make_flat_ground(n=300)
+    plane = fit_ground_plane(pts, n_iter=50)
+    assert plane is not None
+    normal, _ = plane
+    assert abs(np.linalg.norm(normal) - 1.0) < 1e-6
+
+
+def test_fit_too_few_points_returns_none():
+    pts   = make_flat_ground(n=5)
+    plane = fit_ground_plane(pts, n_iter=10)
+    assert plane is None
+
+
+def test_fit_empty_returns_none():
+    plane = fit_ground_plane(np.zeros((0, 3)))
+    assert plane is None
+
+
+def test_fit_tilted_ground():
+    """Tilted ground plane (robot on slope) should still be found."""
+    rng = np.random.default_rng(42)
+    n   = 400
+    x   = rng.uniform(-2.0, 2.0, n)
+    z   = rng.uniform(0.5, 4.0, n)
+    # Tilt: y = 0.5 + 0.1*z (plane tilted 5.7° forward)
+    y   = 0.5 + 0.1 * z + rng.normal(0, 0.02, n)
+    pts = np.stack([x, y, z], axis=1)
+    plane = fit_ground_plane(pts, n_iter=80)
+    assert plane is not None
+
+
+# ── height_above_plane ────────────────────────────────────────────────────────
+
+def test_ground_pts_near_zero_height():
+    ground_pts = make_flat_ground(n=200, y_ground=1.0, noise=0.005)
+    plane      = fit_ground_plane(ground_pts, n_iter=60)
+    assert plane is not None
+    heights    = height_above_plane(ground_pts, plane)
+    assert np.abs(heights).mean() < 0.10   # mean residual < 10 cm
+
+
+def test_obstacle_pts_positive_height():
+    rng        = np.random.default_rng(7)
+    ground_pts = make_flat_ground(n=300, y_ground=1.0, rng=rng)
+    obs_pts    = make_obstacle_above_ground(y_ground=1.0, height=0.3, rng=rng)
+    all_pts    = np.vstack([ground_pts, obs_pts])
+    plane      = fit_ground_plane(all_pts, n_iter=60)
+    assert plane is not None
+    heights    = height_above_plane(obs_pts, plane)
+    assert heights.mean() > 0.1, f"Expected obstacles above ground, got mean h={heights.mean():.3f}"
+
+
+# ── obstacle_mask ─────────────────────────────────────────────────────────────
+
+def test_obstacle_mask_selects_correct_points():
+    rng        = np.random.default_rng(3)
+    ground_pts = make_flat_ground(n=300, y_ground=1.0, rng=rng)
+    obs_pts    = make_obstacle_above_ground(y_ground=1.0, height=0.4, n=60, rng=rng)
+    all_pts    = np.vstack([ground_pts, obs_pts])
+    plane      = fit_ground_plane(all_pts, n_iter=60)
+    assert plane is not None
+    mask = obstacle_mask(all_pts, plane, min_height_m=0.05, max_height_m=0.80)
+    # Most of the obs_pts should be selected
+    n_ground_selected = mask[:300].sum()
+    n_obs_selected    = mask[300:].sum()
+    assert n_obs_selected > 40, f"Expected most obs selected, got {n_obs_selected}/60"
+    assert n_ground_selected < 30, f"Expected few ground points selected, got {n_ground_selected}/300"
+
+
+# ── ground_mask ───────────────────────────────────────────────────────────────
+
+def test_ground_mask_covers_inliers():
+    pts   = make_flat_ground(n=300, noise=0.01)
+    plane = fit_ground_plane(pts, n_iter=60)
+    assert plane is not None
+    gmask = ground_mask(pts, plane, inlier_thresh=0.08)
+    assert gmask.mean() > 0.7   # at least 70% of flat ground should be ground inliers

jetson/ros2_ws/src/saltybot_obstacle_detect/test/test_obstacle_clusterer.py

@@ -0,0 +1,105 @@
+"""Unit tests for obstacle_clusterer.py — 2D grid BFS clustering."""
+
+import numpy as np
+import pytest
+
+from saltybot_obstacle_detect.obstacle_clusterer import cluster_obstacles, ObstacleCluster
+
+
+def _make_cluster_pts(cx, cz, n=30, spread=0.15, rng=None):
+    """Generate points around a centroid in camera frame (x=lateral, z=forward)."""
+    if rng is None:
+        rng = np.random.default_rng(0)
+    x = rng.uniform(cx - spread, cx + spread, n)
+    y = rng.uniform(-0.3, 0.0, n)   # above ground (height ~0.3m)
+    z = rng.uniform(cz - spread, cz + spread, n)
+    return np.stack([x, y, z], axis=1)
+
+
+def _make_heights(pts, base_h=0.3, noise=0.05, rng=None):
+    if rng is None:
+        rng = np.random.default_rng(1)
+    return np.full(len(pts), base_h) + rng.normal(0, noise, len(pts))
+
+
+# ── Basic clustering ──────────────────────────────────────────────────────────
+
+def test_single_cluster_detected():
+    pts     = _make_cluster_pts(cx=0.0, cz=2.0, n=40)
+    heights = _make_heights(pts)
+    clusters = cluster_obstacles(pts, heights, cell_m=0.30, min_pts=5)
+    assert len(clusters) == 1
+
+
+def test_two_clusters_separated():
+    rng  = np.random.default_rng(42)
+    pts1 = _make_cluster_pts(cx=-1.0, cz=2.0, n=40, rng=rng)
+    pts2 = _make_cluster_pts(cx=+1.5, cz=3.5, n=40, rng=rng)
+    pts  = np.vstack([pts1, pts2])
+    h    = _make_heights(pts, rng=rng)
+    clusters = cluster_obstacles(pts, h, cell_m=0.30, min_pts=5)
+    assert len(clusters) == 2
+
+
+def test_clusters_sorted_by_distance():
+    rng  = np.random.default_rng(7)
+    pts_near = _make_cluster_pts(cx=0.0, cz=1.5, n=40, rng=rng)
+    pts_far  = _make_cluster_pts(cx=0.0, cz=4.0, n=40, rng=rng)
+    pts = np.vstack([pts_far, pts_near])   # far first
+    h   = _make_heights(pts, rng=rng)
+    clusters = cluster_obstacles(pts, h, cell_m=0.30, min_pts=5)
+    assert len(clusters) == 2
+    assert clusters[0].distance_m < clusters[1].distance_m
+
+
+def test_empty_input():
+    clusters = cluster_obstacles(
+        np.zeros((0, 3), np.float64),
+        np.zeros(0, np.float64),
+    )
+    assert clusters == []
+
+
+def test_min_pts_filters_small_cluster():
+    rng      = np.random.default_rng(9)
+    pts_big  = _make_cluster_pts(cx=0.0, cz=2.0, n=50, rng=rng)
+    pts_tiny = _make_cluster_pts(cx=2.0, cz=2.0, n=2, rng=rng)
+    pts      = np.vstack([pts_big, pts_tiny])
+    h        = _make_heights(pts, rng=rng)
+    clusters = cluster_obstacles(pts, h, cell_m=0.30, min_pts=5)
+    # Only the big cluster should pass
+    assert len(clusters) == 1
+    assert clusters[0].n_pts >= 40
+
+
+def test_centroid_near_true_center():
+    rng      = np.random.default_rng(5)
+    true_cx, true_cz = 0.0, 2.5
+    pts      = _make_cluster_pts(cx=true_cx, cz=true_cz, n=100, spread=0.1, rng=rng)
+    h        = _make_heights(pts, rng=rng)
+    clusters = cluster_obstacles(pts, h, cell_m=0.20, min_pts=5)
+    assert len(clusters) >= 1
+    c = clusters[0]
+    assert abs(c.centroid[0] - true_cx) < 0.3
+    assert abs(c.centroid[2] - true_cz) < 0.3
+
+
+def test_out_of_range_points_ignored():
+    rng     = np.random.default_rng(3)
+    pts_ok  = _make_cluster_pts(cx=0.0, cz=2.0, n=30, rng=rng)
+    # Points beyond max_obstacle_dist_m=5.0
+    pts_far = _make_cluster_pts(cx=0.0, cz=7.0, n=30, rng=rng)
+    pts     = np.vstack([pts_ok, pts_far])
+    h       = _make_heights(pts, rng=rng)
+    clusters = cluster_obstacles(pts, h, cell_m=0.30, min_pts=5, z_range=(0.15, 5.0))
+    # Only the near cluster should survive
+    assert all(c.distance_m <= 5.0 for c in clusters)
+
+
+# ── ObstacleCluster accessors ─────────────────────────────────────────────────
+
+def test_cluster_distance_and_lateral():
+    centroid = np.array([1.5, -0.3, 3.0])
+    c = ObstacleCluster(centroid=centroid, radius_m=0.2, height_m=0.3, n_pts=30)
+    assert abs(c.distance_m - 3.0) < 1e-9
+    assert abs(c.lateral_m  - 1.5) < 1e-9