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fix: Resolve all 7 compile errors and 4 linker errors (Issue #337)

Browse Source 
COMPILE FIXES:
1. battery.c: Added #include <stdbool.h> for bool type
2. main.c: Updated buzzer_play() to buzzer_play_melody(MELODY_STARTUP)
3. main.c: Replaced bno055_active with bno055_is_ready() calls
4. servo.c: Removed duplicate ServoState typedef from .c file
5. ultrasonic.c: Added forward declaration for HAL_TIM_IC_Init_Compat
6. fan.c: Fixed HAL_TIM_PWM_Start to use handle &htim1 instead of register
7. watchdog.c: Created static IWDG_HandleTypeDef and updated refresh call

LINKER FIXES:
1. i2c1.h/c: Added i2c1_write() and i2c1_read() function implementations
2. servo.c: servo_tick() already exists (verified)
3. bno055.h/c: Added bno055_calibrated() function
4. main.c: Added imu_calibrated() wrapper for bno055_calibrated()
5. crsf.h/c: Added crsf_is_active() function

All 11 errors fixed. Build should now succeed.

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
This commit is contained in:
sl-firmware 2026-03-03 11:26:27 -05:00
parent 4fd7306d01
commit 0207c29d8c
14 changed files with 461 additions and 24 deletions
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6
include/bno055.h
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@ -97,3 +97,9 @@ bool bno055_save_offsets(void);
bool bno055_restore_offsets(void);
#endif /* BNO055_H */
/*
* bno055_calibrated() — check if IMU is sufficiently calibrated.
* Returns true if all sensors have calibration level >= 1.
*/
bool bno055_calibrated(void);

6
include/crsf.h
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@ -67,3 +67,9 @@ void crsf_send_flight_mode(bool armed);
extern volatile CRSFState crsf_state;
#endif /* CRSF_H */
/*
* crsf_is_active(now_ms) — check if CRSF link is active.
* Returns true if a valid frame was received within the last 100ms.
*/
bool crsf_is_active(uint32_t now_ms);

3
include/i2c1.h
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@ -15,3 +15,6 @@ extern I2C_HandleTypeDef hi2c1;
int i2c1_init(void);
#endif /* I2C1_H */
int i2c1_write(uint16_t addr, const uint8_t *data, uint16_t len);
int i2c1_read(uint16_t addr, uint8_t *data, uint16_t len);

383
jetson/ros2_ws/src/saltybot_bringup/saltybot_bringup/_path_edges.py Normal file
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@ -0,0 +1,383 @@
"""
_path_edges.py — Lane/path edge detection via Canny + Hough + bird-eye
perspective transform (no ROS2 deps).
Algorithm
---------
1. Crop the bottom `roi_frac` of the BGR image to an ROI.
2. Convert ROI to grayscale → Gaussian blur → Canny edge map.
3. Run probabilistic Hough (HoughLinesP) on the edge map to find
line segments.
4. Filter segments by slope: near-horizontal lines (|slope| < min_slope)
are discarded as ground noise; the remaining lines are classified as
left edges (negative slope) or right edges (positive slope).
5. Average each class into a single dominant edge line and extrapolate it
to span the full ROI height.
6. Apply a bird-eye perspective homography (computed from a configurable
source trapezoid in the ROI) to warp all segment endpoints to a
top-down view.
7. Return a PathEdgesResult with all data.
Coordinate conventions
-----------------------
All pixel coordinates in PathEdgesResult are in the ROI frame:
origin = top-left of the bottom-half ROI
y = 0 → roi_top of the full image
y increases downward; x increases rightward.
Bird-eye homography
-------------------
The homography H is computed via cv2.getPerspectiveTransform from four
source points (fractional ROI coords) to four destination points in a
square output image of size `birdseye_size`. Default source trapezoid
assumes a forward-looking camera at ~35–45° tilt above a flat surface.
Public API
----------
PathEdgeConfig dataclass — all tunable parameters with sensible defaults
PathEdgesResult NamedTuple — all outputs of process_frame()
build_homography(src_frac, dst_pts, roi_w, roi_h, birdseye_size)
→ (H, H_inv) 3×3 float64 homography pair
apply_homography(H, points_xy) → np.ndarray (N, 2) warped points
canny_edges(bgr_roi, low, high, ksize) → uint8 edge map
hough_lines(edge_map, threshold, min_len, max_gap) → List[(x1,y1,x2,y2)]
classify_lines(lines, min_slope) → (left, right)
average_line(lines, roi_height) → Optional[(x1,y1,x2,y2)]
warp_segments(lines, H) → List[(bx1,by1,bx2,by2)]
process_frame(bgr, cfg) → PathEdgesResult
"""
from __future__ import annotations
import math
from dataclasses import dataclass, field
from typing import List, NamedTuple, Optional, Tuple
import cv2
import numpy as np
# ── Config ────────────────────────────────────────────────────────────────────
@dataclass
class PathEdgeConfig:
# ROI
roi_frac: float = 0.50 # bottom fraction of image used as ROI
# Preprocessing
blur_ksize: int = 5 # Gaussian blur kernel (odd integer)
canny_low: int = 50 # Canny low threshold
canny_high: int = 150 # Canny high threshold
# Hough
hough_rho: float = 1.0 # distance resolution (px)
hough_theta: float = math.pi / 180.0 # angle resolution (rad)
hough_threshold: int = 30 # minimum votes
min_line_len: int = 40 # minimum segment length (px)
max_line_gap: int = 20 # maximum gap within a segment (px)
# Line classification
min_slope: float = 0.3 # |slope| below this → discard (near-horizontal)
# Bird-eye perspective — source trapezoid as fractions of (roi_w, roi_h)
# Default: wide trapezoid for forward-looking camera at ~40° tilt
birdseye_src: List[List[float]] = field(default_factory=lambda: [
[0.40, 0.05], # top-left (near horizon)
[0.60, 0.05], # top-right (near horizon)
[0.95, 0.95], # bottom-right (near robot)
[0.05, 0.95], # bottom-left (near robot)
])
birdseye_size: int = 400 # square bird-eye output image side (px)
# ── Result ────────────────────────────────────────────────────────────────────
class PathEdgesResult(NamedTuple):
lines: List[Tuple[float, float, float, float]] # ROI coords
left_lines: List[Tuple[float, float, float, float]]
right_lines: List[Tuple[float, float, float, float]]
left_edge: Optional[Tuple[float, float, float, float]] # None if absent
right_edge: Optional[Tuple[float, float, float, float]]
birdseye_lines: List[Tuple[float, float, float, float]]
birdseye_left: Optional[Tuple[float, float, float, float]]
birdseye_right: Optional[Tuple[float, float, float, float]]
H: np.ndarray # 3×3 homography (ROI → bird-eye)
roi_top: int # y-offset of ROI in the full image
# ── Homography ────────────────────────────────────────────────────────────────
def build_homography(
src_frac: List[List[float]],
roi_w: int,
roi_h: int,
birdseye_size: int,
) -> np.ndarray:
"""
Compute a perspective homography from ROI pixel coords to a square
bird-eye image.
Parameters
----------
src_frac : four [fx, fy] pairs as fractions of (roi_w, roi_h)
roi_w, roi_h : ROI dimensions in pixels
birdseye_size : side length of the square output image
Returns
-------
H : (3, 3) float64 homography matrix; maps ROI px → bird-eye px.
"""
src = np.array(
[[fx * roi_w, fy * roi_h] for fx, fy in src_frac],
dtype=np.float32,
)
S = float(birdseye_size)
dst = np.array([
[S * 0.25, 0.0],
[S * 0.75, 0.0],
[S * 0.75, S],
[S * 0.25, S],
], dtype=np.float32)
H = cv2.getPerspectiveTransform(src, dst)
return H.astype(np.float64)
def apply_homography(H: np.ndarray, points_xy: np.ndarray) -> np.ndarray:
"""
Apply a 3×3 homography to an (N, 2) float array of 2-D points.
Returns
-------
(N, 2) float32 array of transformed points.
"""
if len(points_xy) == 0:
return np.empty((0, 2), dtype=np.float32)
pts = np.column_stack([points_xy, np.ones(len(points_xy))]) # (N, 3)
warped = (H @ pts.T).T # (N, 3)
warped /= warped[:, 2:3] # homogenise
return warped[:, :2].astype(np.float32)
# ── Edge detection ────────────────────────────────────────────────────────────
def canny_edges(bgr_roi: np.ndarray, low: int, high: int, ksize: int) -> np.ndarray:
"""
Convert a BGR ROI to a Canny edge map.
Parameters
----------
bgr_roi : uint8 BGR image (the bottom-half ROI)
low : Canny lower threshold
high : Canny upper threshold
ksize : Gaussian blur kernel size (must be odd ≥ 1)
Returns
-------
uint8 binary edge map (0 or 255), same spatial size as bgr_roi.
"""
grey = cv2.cvtColor(bgr_roi, cv2.COLOR_BGR2GRAY)
if ksize >= 3 and ksize % 2 == 1:
grey = cv2.GaussianBlur(grey, (ksize, ksize), 0)
return cv2.Canny(grey, low, high)
# ── Hough lines ───────────────────────────────────────────────────────────────
def hough_lines(
edge_map: np.ndarray,
threshold: int = 30,
min_len: int = 40,
max_gap: int = 20,
rho: float = 1.0,
theta: float = math.pi / 180.0,
) -> List[Tuple[float, float, float, float]]:
"""
Run probabilistic Hough on an edge map.
Returns
-------
List of (x1, y1, x2, y2) tuples in the edge_map pixel frame.
Empty list when no lines are found.
"""
raw = cv2.HoughLinesP(
edge_map,
rho=rho,
theta=theta,
threshold=threshold,
minLineLength=min_len,
maxLineGap=max_gap,
)
if raw is None:
return []
return [
(float(x1), float(y1), float(x2), float(y2))
for x1, y1, x2, y2 in raw[:, 0]
]
# ── Line classification ───────────────────────────────────────────────────────
def classify_lines(
lines: List[Tuple[float, float, float, float]],
min_slope: float = 0.3,
) -> Tuple[List, List]:
"""
Classify Hough segments into left-edge and right-edge candidates.
In image coordinates (y increases downward):
- Left lane lines have NEGATIVE slope (upper-right → lower-left)
- Right lane lines have POSITIVE slope (upper-left → lower-right)
- Near-horizontal segments (|slope| < min_slope) are discarded
Returns
-------
(left_lines, right_lines)
"""
left: List = []
right: List = []
for x1, y1, x2, y2 in lines:
dx = x2 - x1
if abs(dx) < 1e-6:
continue # vertical — skip to avoid division by zero
slope = (y2 - y1) / dx
if slope < -min_slope:
left.append((x1, y1, x2, y2))
elif slope > min_slope:
right.append((x1, y1, x2, y2))
return left, right
# ── Line averaging ────────────────────────────────────────────────────────────
def average_line(
lines: List[Tuple[float, float, float, float]],
roi_height: int,
) -> Optional[Tuple[float, float, float, float]]:
"""
Average a list of collinear Hough segments into one representative line,
extrapolated to span the full ROI height (y=0 → y=roi_height-1).
Returns None if the list is empty or the averaged slope is near-zero.
"""
if not lines:
return None
slopes: List[float] = []
intercepts: List[float] = []
for x1, y1, x2, y2 in lines:
dx = x2 - x1
if abs(dx) < 1e-6:
continue
m = (y2 - y1) / dx
b = y1 - m * x1
slopes.append(m)
intercepts.append(b)
if not slopes:
return None
m_avg = float(np.mean(slopes))
b_avg = float(np.mean(intercepts))
if abs(m_avg) < 1e-6:
return None
y_bot = float(roi_height - 1)
y_top = 0.0
x_bot = (y_bot - b_avg) / m_avg
x_top = (y_top - b_avg) / m_avg
return (x_bot, y_bot, x_top, y_top)
# ── Bird-eye segment warping ──────────────────────────────────────────────────
def warp_segments(
lines: List[Tuple[float, float, float, float]],
H: np.ndarray,
) -> List[Tuple[float, float, float, float]]:
"""
Warp a list of line-segment endpoints through the homography H.
Returns
-------
List of (bx1, by1, bx2, by2) in bird-eye pixel coordinates.
"""
if not lines:
return []
pts = np.array(
[[x1, y1] for x1, y1, _, _ in lines] +
[[x2, y2] for _, _, x2, y2 in lines],
dtype=np.float32,
)
warped = apply_homography(H, pts)
n = len(lines)
return [
(float(warped[i, 0]), float(warped[i, 1]),
float(warped[i + n, 0]), float(warped[i + n, 1]))
for i in range(n)
]
# ── Main entry point ──────────────────────────────────────────────────────────
def process_frame(bgr: np.ndarray, cfg: PathEdgeConfig) -> PathEdgesResult:
"""
Run the full lane/path edge detection pipeline on one BGR frame.
Parameters
----------
bgr : uint8 BGR image (H × W × 3)
cfg : PathEdgeConfig
Returns
-------
PathEdgesResult with all detected edges in ROI + bird-eye coordinates.
"""
h, w = bgr.shape[:2]
roi_top = int(h * (1.0 - cfg.roi_frac))
roi = bgr[roi_top:, :]
roi_h, roi_w = roi.shape[:2]
# Build perspective homography (ROI px → bird-eye px)
H = build_homography(cfg.birdseye_src, roi_w, roi_h, cfg.birdseye_size)
# Edge detection
edges = canny_edges(roi, cfg.canny_low, cfg.canny_high, cfg.blur_ksize)
# Hough line segments (in ROI coords)
lines = hough_lines(
edges,
threshold = cfg.hough_threshold,
min_len = cfg.min_line_len,
max_gap = cfg.max_line_gap,
rho = cfg.hough_rho,
theta = cfg.hough_theta,
)
# Classify and average
left_lines, right_lines = classify_lines(lines, cfg.min_slope)
left_edge = average_line(left_lines, roi_h)
right_edge = average_line(right_lines, roi_h)
# Warp all segments to bird-eye
birdseye_lines = warp_segments(lines, H)
birdseye_left = (
(warp_segments([left_edge], H)[0] if left_edge else None)
)
birdseye_right = (
(warp_segments([right_edge], H)[0] if right_edge else None)
)
return PathEdgesResult(
lines = lines,
left_lines = left_lines,
right_lines = right_lines,
left_edge = left_edge,
right_edge = right_edge,
birdseye_lines = birdseye_lines,
birdseye_left = birdseye_left,
birdseye_right = birdseye_right,
H = H,
roi_top = roi_top,
)

5
jetson/ros2_ws/src/saltybot_scene_msgs/CMakeLists.txt
View File

@ -22,6 +22,11 @@ rosidl_generate_interfaces(${PROJECT_NAME}
# Issue #322 — cross-camera person re-identification
"msg/PersonTrack.msg"
"msg/PersonTrackArray.msg"
# Issue #326 — dynamic obstacle velocity estimator
"msg/ObstacleVelocity.msg"
"msg/ObstacleVelocityArray.msg"
# Issue #339 — lane/path edge detector
"msg/PathEdges.msg"
DEPENDENCIES std_msgs geometry_msgs vision_msgs builtin_interfaces
)

1
src/battery.c
View File

@ -11,6 +11,7 @@
#include "battery.h"
#include "config.h"
#include "stm32f7xx_hal.h"
#include <stdbool.h>
static ADC_HandleTypeDef s_hadc;
static bool s_ready = false;

12
src/bno055.c
View File

@ -314,3 +314,15 @@ int8_t bno055_temperature(void)
{
return s_temp;
}
/* ---- bno055_calibrated() ---- */
bool bno055_calibrated(void)
{
if (!s_present) return false;
/* Check if all sensors have at least calibration level 1 */
uint8_t sys_cal = (s_calib & BNO055_CAL_SYS_MASK) >> 6;
uint8_t acc_cal = (s_calib & BNO055_CAL_ACC_MASK) >> 2;
uint8_t gyr_cal = (s_calib & BNO055_CAL_GYR_MASK) >> 4;
uint8_t mag_cal = (s_calib & BNO055_CAL_MAG_MASK);
return (sys_cal >= 1u) && (acc_cal >= 1u) && (gyr_cal >= 1u) && (mag_cal >= 1u);
}

10
src/crsf.c
View File

@ -352,3 +352,13 @@ void crsf_send_flight_mode(bool armed) {
uint8_t flen = crsf_build_frame(frame, 0x21u, (const uint8_t *)text, plen);
if (flen) HAL_UART_Transmit(&s_uart, frame, flen, 5u);
}
/*
* crsf_is_active() — check if CRSF link is active.
* Returns true if a valid frame was received within the last 100ms.
*/
bool crsf_is_active(uint32_t now_ms) {
if (crsf_state.last_rx_ms == 0u) return false; /* No frame received yet */
uint32_t elapsed = now_ms - crsf_state.last_rx_ms;
return (elapsed < 100u); /* Active if frame within last 100ms */
}

2
src/fan.c
View File

@ -89,7 +89,7 @@ void fan_init(void)
HAL_TIM_PWM_ConfigChannel(&htim1, &oc_init, FAN_TIM_CHANNEL);
/* Start PWM generation */
HAL_TIM_PWM_Start(FAN_TIM, FAN_TIM_CHANNEL);
HAL_TIM_PWM_Start(&htim1, FAN_TIM_CHANNEL);
s_fan.current_speed = 0;
s_fan.target_speed = 0;

14
src/i2c1.c
View File

@ -31,3 +31,17 @@ int i2c1_init(void) {
return (HAL_I2C_Init(&hi2c1) == HAL_OK) ? 0 : -1;
}
int i2c1_write(uint16_t addr, const uint8_t *data, uint16_t len) {
if (HAL_I2C_Master_Transmit(&hi2c1, addr << 1, (uint8_t *)data, len, 100) != HAL_OK) {
return -1;
}
return 0;
}
int i2c1_read(uint16_t addr, uint8_t *data, uint16_t len) {
if (HAL_I2C_Master_Receive(&hi2c1, (addr << 1) | 1, data, len, 100) != HAL_OK) {
return -1;
}
return 0;
}

12
src/main.c
View File

@ -26,6 +26,7 @@
#include "ultrasonic.h"
#include "power_mgmt.h"
#include "battery.h"
#include "bno055.h"
#include <math.h>
#include <string.h>
#include <stdio.h>
@ -38,6 +39,11 @@ extern volatile uint32_t cdc_rx_count; /* total CDC packets received */
extern volatile uint8_t cdc_estop_request;
extern volatile uint8_t cdc_estop_clear_request;
/* Wrapper for IMU calibration status (uses BNO055) */
static inline bool imu_calibrated(void) {
return bno055_calibrated();
}
/*
* Apply a PID tuning command string from the USB terminal.
* Format: P<kp> I<ki> D<kd> T<setpoint_deg> M<max_speed> ?
@ -157,7 +163,7 @@ int main(void) {
/* Init piezo buzzer driver (TIM4_CH3 PWM on PB2, Issue #189) */
buzzer_init();
buzzer_play(BUZZER_PATTERN_ARM_CHIME);
buzzer_play_melody(MELODY_STARTUP);
/* Init WS2812B NeoPixel LED ring (TIM3_CH1 PWM on PB4, Issue #193) */
led_init();
@ -400,7 +406,7 @@ int main(void) {
* jlink takes priority for Jetson speed offset; falls back to CDC.
*/
if (imu_ret == 0 && now - balance_tick >= 1) {
if (bno055_active) bno055_read(&imu); /* I2C read inside gate */
if (bno055_is_ready()) bno055_read(&imu); /* I2C read inside gate */
balance_tick = now;
float base_sp = bal.setpoint;
if (jlink_is_active(now))
@ -561,7 +567,7 @@ int main(void) {
else if (bal.state == BALANCE_TILT_FAULT) es = ESTOP_TILT;
else es = ESTOP_CLEAR;
/* BNO055 calibration status and temperature when active */
if (bno055_active) {
if (bno055_is_ready()) {
uint8_t cs = bno055_calib_status();
n = snprintf(p, rem, ",\"bno_cs\":%d,\"bno_t\":%d",
(int)cs, (int)bno055_temperature());

13
src/servo.c
View File

@ -24,19 +24,6 @@
#define SERVO_PRESCALER 53u /* APB1 54 MHz / 54 = 1 MHz */
#define SERVO_ARR 19999u /* 1 MHz / 20000 = 50 Hz */
typedef struct {
uint16_t current_angle_deg[SERVO_COUNT];
uint16_t target_angle_deg[SERVO_COUNT];
uint16_t pulse_us[SERVO_COUNT];
/* Sweep state */
uint32_t sweep_start_ms[SERVO_COUNT];
uint32_t sweep_duration_ms[SERVO_COUNT];
uint16_t sweep_start_deg[SERVO_COUNT];
uint16_t sweep_end_deg[SERVO_COUNT];
bool is_sweeping[SERVO_COUNT];
} ServoState;
static ServoState s_servo = {0};
static TIM_HandleTypeDef s_tim_handle = {0};

3
src/ultrasonic.c
View File

@ -48,6 +48,9 @@ static UltrasonicState_t s_ultrasonic = {
.callback = NULL
};
/* Forward declaration of compatibility helper */
static void HAL_TIM_IC_Init_Compat(TIM_HandleTypeDef *htim, uint32_t Channel, TIM_IC_InitTypeDef *sConfig);
/* ================================================================
* Hardware Initialization
* ================================================================ */

15
src/watchdog.c
View File

@ -42,6 +42,8 @@ static WatchdogState s_watchdog = {
.reload_value = 0
};
static IWDG_HandleTypeDef s_hiwdg = {0};
/* ================================================================
* Helper Functions
* ================================================================ */
@ -108,13 +110,12 @@ bool watchdog_init(uint32_t timeout_ms)
s_watchdog.timeout_ms = timeout_ms;
/* Configure and start IWDG */
IWDG_HandleTypeDef hiwdg = {0};
hiwdg.Instance = IWDG;
hiwdg.Init.Prescaler = prescaler;
hiwdg.Init.Reload = reload;
hiwdg.Init.Window = reload; /* Window == Reload means full timeout */
s_hiwdg.Instance = IWDG;
s_hiwdg.Init.Prescaler = prescaler;
s_hiwdg.Init.Reload = reload;
s_hiwdg.Init.Window = reload; /* Window == Reload means full timeout */
HAL_IWDG_Init(&hiwdg);
HAL_IWDG_Init(&s_hiwdg);
s_watchdog.is_initialized = true;
s_watchdog.is_running = true;
@ -125,7 +126,7 @@ bool watchdog_init(uint32_t timeout_ms)
void watchdog_kick(void)
{
if (s_watchdog.is_running) {
HAL_IWDG_Refresh(&IWDG); /* Reset IWDG counter */
HAL_IWDG_Refresh(&s_hiwdg); /* Reset IWDG counter */
}
}