launchfile and new keypoint fusion method

keypoint_pose/keypoint_pose/keypoint_node.py

@@ -1,26 +1,36 @@
 """
-ROS 2 node that subscribes to rectified stereo images, runs MMPose
+ROS 2 node: stereo keypoint triangulation using MMPose.
-keypoint detection, and displays the results in OpenCV windows.
+
+Subscribes to raw stereo images, runs pose estimation on each,
+matches keypoints across cameras, and triangulates 3D positions.
+
+Published topics:
+    /keypoint_markers  (visualization_msgs/MarkerArray)  -- for RViz
 """
 
+import colorsys
+
 import cv2
 import numpy as np
 import rclpy
 from rclpy.node import Node
-from sensor_msgs.msg import Image
+from sensor_msgs.msg import Image, CameraInfo
+from visualization_msgs.msg import Marker, MarkerArray
+from geometry_msgs.msg import Point
 from cv_bridge import CvBridge
+from message_filters import Subscriber, ApproximateTimeSynchronizer
 from mmpose.apis import MMPoseInferencer
 
 
-# ── COCO-17 drawing helpers ─────────────────────────────────────────────
+# ── COCO-17 constants ───────────────────────────────────────────────────
 
 SKELETON = [
-    (0, 1), (0, 2),           # nose → eyes
+    (0, 1), (0, 2),           # nose -> eyes
-    (1, 3), (2, 4),           # eyes → ears
+    (1, 3), (2, 4),           # eyes -> ears
     (5, 6),                   # shoulders
     (5, 7), (7, 9),           # left arm
     (6, 8), (8, 10),          # right arm
-    (5, 11), (6, 12),         # shoulders → hips
+    (5, 11), (6, 12),         # shoulders -> hips
     (11, 12),                 # hips
     (11, 13), (13, 15),       # left leg
     (12, 14), (14, 16),       # right leg
@@ -39,7 +49,6 @@ def draw_keypoints(frame, keypoints, keypoint_scores, threshold=0.3):
     h, w = frame.shape[:2]
 
     for person_kps, person_scores in zip(keypoints, keypoint_scores):
-        # Skeleton lines
         for j1, j2 in SKELETON:
             if person_scores[j1] > threshold and person_scores[j2] > threshold:
                 pt1 = (int(person_kps[j1][0]), int(person_kps[j1][1]))
@@ -48,7 +57,6 @@ def draw_keypoints(frame, keypoints, keypoint_scores, threshold=0.3):
                         0 <= pt2[0] < w and 0 <= pt2[1] < h):
                     cv2.line(frame, pt1, pt2, (0, 255, 0), 2)
 
-        # Joint circles
         for idx, (kp, score) in enumerate(zip(person_kps, person_scores)):
             if score > threshold:
                 x, y = int(kp[0]), int(kp[1])
@@ -62,8 +70,8 @@ def draw_keypoints(frame, keypoints, keypoint_scores, threshold=0.3):
 
 # ── ROS 2 Node ───────────────────────────────────────────────────────────
 
-class KeypointNode(Node):
+class KeypointTriangulationNode(Node):
-    """Subscribe to stereo rectified images, run MMPose, and display."""
+    """Stereo keypoint triangulation with MMPose."""
 
     def __init__(self):
         super().__init__('keypoint_node')
@@ -71,125 +79,360 @@ class KeypointNode(Node):
         # ── Parameters ──────────────────────────────────────────────────
         self.declare_parameter('threshold', 0.3)
         self.declare_parameter('device', 'cuda:0')
+        self.declare_parameter('max_residual', 0.10)  # metres
 
-        threshold = self.get_parameter('threshold').value
+        self._threshold = self.get_parameter('threshold').value
+        self._max_residual = self.get_parameter('max_residual').value
         device = self.get_parameter('device').value
-        self._threshold = threshold
 
         # ── MMPose (single shared instance) ─────────────────────────────
-        self.get_logger().info(
+        self.get_logger().info(f'Loading MMPose on {device} ...')
-            f'Loading MMPose model on {device} (this may take a moment)…'
+        self._inferencer = MMPoseInferencer(pose2d='human', device=device)
-        )
+        self.get_logger().info('MMPose loaded.')
-        self._inferencer = MMPoseInferencer(
-            pose2d='human',
-            device=device,
-        )
-        self.get_logger().info('MMPose model loaded.')
 
         # ── cv_bridge ───────────────────────────────────────────────────
         self._bridge = CvBridge()
 
-        # Latest frames (written by subscribers, read by display timer)
+        # ── Calibration (filled from camera_info subscribers) ───────────
-        self._left_frame: np.ndarray | None = None
+        self._calib = {'left': None, 'right': None}
-        self._right_frame: np.ndarray | None = None
+        self._stereo = None  # computed once both camera_infos arrive
 
-        # ── Subscribers (independent — no time-sync needed) ─────────────
         self.create_subscription(
-            Image,
+            CameraInfo, '/stereo/left/camera_info',
-            '/stereo/left/image_rect',
+            lambda m: self._info_cb(m, 'left'), 10)
-            self._left_cb,
-            10,
-        )
         self.create_subscription(
-            Image,
+            CameraInfo, '/stereo/right/camera_info',
-            '/stereo/right/image_rect',
+            lambda m: self._info_cb(m, 'right'), 10)
-            self._right_cb,
-            10,
-        )
 
-        # ── Timer for cv2.waitKey (keeps GUI responsive) ────────────────
+        # ── Time-synced image subscribers ───────────────────────────────
-        # ~30 Hz is plenty for display refresh
+        left_sub = Subscriber(self, Image, '/stereo/left/image_raw')
+        right_sub = Subscriber(self, Image, '/stereo/right/image_raw')
+        self._sync = ApproximateTimeSynchronizer(
+            [left_sub, right_sub], queue_size=5, slop=0.05)
+        self._sync.registerCallback(self._synced_cb)
+
+        # ── Publisher ───────────────────────────────────────────────────
+        self._marker_pub = self.create_publisher(
+            MarkerArray, '/keypoint_markers', 10)
+
+        # ── Display state ───────────────────────────────────────────────
+        self._left_display = None
+        self._right_display = None
         self.create_timer(1.0 / 30.0, self._display_timer_cb)
 
         self.get_logger().info(
-            'Subscribed to /stereo/left/image_rect and '
+            'Waiting for camera_info and synced image_raw from '
-            '/stereo/right/image_rect — waiting for images…'
+            '/stereo/left and /stereo/right ...')
-        )
 
-    # ── Subscriber callbacks ────────────────────────────────────────────
+    # ── Camera info / stereo geometry ───────────────────────────────────
 
-    def _process_frame(self, msg: Image, label: str) -> np.ndarray:
+    def _info_cb(self, msg: CameraInfo, side: str):
-        """Convert ROS Image -> OpenCV, run MMPose, draw keypoints."""
+        if self._calib[side] is not None:
-        # Log image metadata once to help diagnose blank-frame issues
+            return
-        if not hasattr(self, f'_logged_{label}'):
+        self._calib[side] = {
+            'K': np.array(msg.k, dtype=np.float64).reshape(3, 3),
+            'D': np.array(msg.d, dtype=np.float64),
+            'R_rect': np.array(msg.r, dtype=np.float64).reshape(3, 3),
+            'P': np.array(msg.p, dtype=np.float64).reshape(3, 4),
+        }
         self.get_logger().info(
-                f'[{label}] First frame: encoding={msg.encoding} '
+            f'{side} camera_info received ({msg.width}x{msg.height})')
-                f'size={msg.width}x{msg.height} step={msg.step}'
+        self._try_compute_stereo()
-            )
-            setattr(self, f'_logged_{label}', True)
 
-        # Use passthrough first so we can inspect the raw data
+    def _try_compute_stereo(self):
-        frame = self._bridge.imgmsg_to_cv2(msg, desired_encoding='passthrough')
+        if self._calib['left'] is None or self._calib['right'] is None:
+            return
+        if self._stereo is not None:
+            return
 
-        if not hasattr(self, f'_logged_px_{label}'):
+        R1 = self._calib['left']['R_rect']
+        R2 = self._calib['right']['R_rect']
+        P_R = self._calib['right']['P']
+
+        # Baseline: P_right[0,3] = fx_rect * tx  (tx is negative)
+        tx = P_R[0, 3] / P_R[0, 0]
+
+        # Right camera origin in left (world) frame:
+        #   O_right = -R1^T @ [tx, 0, 0]
+        O_right = -R1.T @ np.array([tx, 0.0, 0.0])
+
+        # Rotation that transforms a direction vector from the right
+        # camera frame into the left (world) frame:
+        #   d_world = R_right_to_world @ d_right_cam
+        R_right_to_world = R1.T @ R2
+
+        self._stereo = {
+            'O_right': O_right,
+            'R_right_to_world': R_right_to_world,
+        }
+
+        baseline = np.linalg.norm(O_right)
         self.get_logger().info(
-                f'[{label}] Frame dtype={frame.dtype} shape={frame.shape} '
+            f'Stereo geometry ready.  Baseline = {baseline:.4f} m  '
-                f'min={frame.min()} max={frame.max()}'
+            f'O_right = {O_right.round(4).tolist()}')
-            )
-            setattr(self, f'_logged_px_{label}', True)
 
-        # Convert grayscale to BGR so MMPose and drawing work correctly
+    # ── Pixel -> ray ────────────────────────────────────────────────────
-        if len(frame.shape) == 2 or frame.shape[2] == 1:
-            frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
-        elif frame.shape[2] == 4:
-            frame = cv2.cvtColor(frame, cv2.COLOR_BGRA2BGR)
 
+    def _pixel_to_ray(self, u, v, side):
+        """Undistort pixel (u,v) and return a unit ray in the world frame."""
+        K = self._calib[side]['K']
+        D = self._calib[side]['D']
+
+        # cv2.undistortPoints returns normalised coordinates (K^-1 applied
+        # and distortion corrected).
+        pts = np.array([[[u, v]]], dtype=np.float64)
+        norm = cv2.undistortPoints(pts, K, D)
+        d_cam = np.array([norm[0, 0, 0], norm[0, 0, 1], 1.0])
+
+        if side == 'right':
+            d_world = self._stereo['R_right_to_world'] @ d_cam
+        else:
+            d_world = d_cam  # left camera IS the world frame
+
+        return d_world / np.linalg.norm(d_world)
+
+    # ── Triangulation (closest point of approach) ───────────────────────
+
+    def _triangulate(self, d1, d2):
+        """
+        Given two unit rays from O_left and O_right, find the 3D midpoint
+        of closest approach and the residual (ray separation).
+
+        Returns (point_3d, residual) or (None, inf) if rays are parallel.
+        """
+        O1 = np.zeros(3)
+        O2 = self._stereo['O_right']
+        b = O2 - O1  # baseline vector
+
+        # Solve  s*d1 - t*d2 = b  in the least-squares sense:
+        #   | d1.d1  -d1.d2 | |s|   |d1.b|
+        #   | d1.d2  -d2.d2 | |t| = |d2.b|
+        d1d1 = d1 @ d1
+        d2d2 = d2 @ d2
+        d1d2 = d1 @ d2
+
+        denom = d1d1 * d2d2 - d1d2 * d1d2
+        if abs(denom) < 1e-12:
+            return None, float('inf')
+
+        d1b = d1 @ b
+        d2b = d2 @ b
+
+        s = (d1b * d2d2 - d2b * d1d2) / denom
+        t = (d1b * d1d2 - d2b * d1d1) / denom
+
+        p1 = O1 + s * d1
+        p2 = O2 + t * d2
+
+        return (p1 + p2) / 2.0, np.linalg.norm(p1 - p2)
+
+    # ── MMPose helper ───────────────────────────────────────────────────
+
+    def _run_mmpose(self, frame):
+        """Return list of dicts with 'keypoints' (N,2) and 'scores' (N,)."""
         result = next(self._inferencer(
-            frame,
+            frame, show=False, return_datasamples=False))
-            show=False,
-            return_datasamples=False,
-        ))
 
-        predictions = result.get('predictions', [[]])[0]
+        people = []
-        all_kps, all_scores = [], []
+        for pred in result.get('predictions', [[]])[0]:
-        for pred in predictions:
             kps = pred.get('keypoints', [])
             scores = pred.get('keypoint_scores', [])
             if len(kps) > 0:
-                all_kps.append(np.array(kps))
+                people.append({
-                all_scores.append(np.array(scores))
+                    'keypoints': np.array(kps),
+                    'scores': np.array(scores),
+                })
+        return people
 
-        if all_kps:
+    # ── Person matching across cameras ──────────────────────────────────
-            draw_keypoints(frame, all_kps, all_scores, self._threshold)
 
+    def _match_people(self, left_people, right_people):
+        """
+        Match people across stereo views using vertical-coordinate
+        similarity (epipolar heuristic: same person has similar y in
+        a roughly-rectified stereo pair).
+
+        Returns list of (left_person, right_person) tuples.
+        """
+        if not left_people or not right_people:
+            return []
+
+        thr = self._threshold
+        n_l, n_r = len(left_people), len(right_people)
+
+        # Cost matrix: mean |y-difference| for mutually confident keypoints
+        cost = np.full((n_l, n_r), np.inf)
+        for i, lp in enumerate(left_people):
+            for j, rp in enumerate(right_people):
+                mask = (lp['scores'] > thr) & (rp['scores'] > thr)
+                if mask.sum() < 3:
+                    continue
+                cost[i, j] = np.abs(
+                    lp['keypoints'][mask, 1] - rp['keypoints'][mask, 1]
+                ).mean()
+
+        # Greedy matching by lowest cost
+        matches = []
+        used_l, used_r = set(), set()
+        while True:
+            remaining = np.full_like(cost, np.inf)
+            for i in range(n_l):
+                for j in range(n_r):
+                    if i not in used_l and j not in used_r:
+                        remaining[i, j] = cost[i, j]
+            idx = np.unravel_index(remaining.argmin(), remaining.shape)
+            if remaining[idx] > 100.0 or np.isinf(remaining[idx]):
+                break
+            matches.append((left_people[idx[0]], right_people[idx[1]]))
+            used_l.add(idx[0])
+            used_r.add(idx[1])
+
+        return matches
+
+    # ── Marker builder ──────────────────────────────────────────────────
+
+    def _build_markers(self, all_points_3d, stamp):
+        ma = MarkerArray()
+
+        # Clear previous frame's markers
+        delete = Marker()
+        delete.action = Marker.DELETEALL
+        delete.header.frame_id = 'left'
+        delete.header.stamp = stamp
+        ma.markers.append(delete)
+
+        mid = 0
+        for person_idx, person_pts in enumerate(all_points_3d):
+            r, g, b_ = colorsys.hsv_to_rgb((person_idx * 0.3) % 1.0, 1.0, 1.0)
+
+            # Sphere per keypoint
+            for kp_idx, (pt, _res) in person_pts.items():
+                m = Marker()
+                m.header.frame_id = 'left'
+                m.header.stamp = stamp
+                m.ns = f'person_{person_idx}'
+                m.id = mid; mid += 1
+                m.type = Marker.SPHERE
+                m.action = Marker.ADD
+                m.pose.position.x = float(pt[0])
+                m.pose.position.y = float(pt[1])
+                m.pose.position.z = float(pt[2])
+                m.pose.orientation.w = 1.0
+                m.scale.x = m.scale.y = m.scale.z = 0.05
+                m.color.r, m.color.g, m.color.b = r, g, b_
+                m.color.a = 1.0
+                m.lifetime.nanosec = 300_000_000
+                ma.markers.append(m)
+
+            # Skeleton lines
+            for j1, j2 in SKELETON:
+                if j1 in person_pts and j2 in person_pts:
+                    m = Marker()
+                    m.header.frame_id = 'left'
+                    m.header.stamp = stamp
+                    m.ns = f'skel_{person_idx}'
+                    m.id = mid; mid += 1
+                    m.type = Marker.LINE_STRIP
+                    m.action = Marker.ADD
+                    m.points = [
+                        Point(x=float(person_pts[j1][0][0]),
+                              y=float(person_pts[j1][0][1]),
+                              z=float(person_pts[j1][0][2])),
+                        Point(x=float(person_pts[j2][0][0]),
+                              y=float(person_pts[j2][0][1]),
+                              z=float(person_pts[j2][0][2])),
+                    ]
+                    m.scale.x = 0.02
+                    m.color.r, m.color.g, m.color.b = r, g, b_
+                    m.color.a = 0.8
+                    m.lifetime.nanosec = 300_000_000
+                    ma.markers.append(m)
+
+        return ma
+
+    # ── Main synced callback ────────────────────────────────────────────
+
+    def _synced_cb(self, left_msg: Image, right_msg: Image):
+        if self._stereo is None:
+            return  # waiting for camera_info
+
+        # Convert images (handles Bayer -> BGR automatically)
+        left_frame = self._bridge.imgmsg_to_cv2(
+            left_msg, desired_encoding='bgr8')
+        right_frame = self._bridge.imgmsg_to_cv2(
+            right_msg, desired_encoding='bgr8')
+
+        # Run MMPose on both views
+        left_people = self._run_mmpose(left_frame)
+        right_people = self._run_mmpose(right_frame)
+
+        # Match people across cameras
+        matches = self._match_people(left_people, right_people)
+
+        # Triangulate every mutually-confident keypoint
+        all_points_3d = []
+        for lp, rp in matches:
+            person_pts = {}  # kp_idx -> (xyz, residual)
+            for kp_idx in range(17):
+                if (lp['scores'][kp_idx] <= self._threshold or
+                        rp['scores'][kp_idx] <= self._threshold):
+                    continue
+
+                u_l, v_l = lp['keypoints'][kp_idx]
+                u_r, v_r = rp['keypoints'][kp_idx]
+
+                d1 = self._pixel_to_ray(u_l, v_l, 'left')
+                d2 = self._pixel_to_ray(u_r, v_r, 'right')
+
+                pt3d, residual = self._triangulate(d1, d2)
+                if pt3d is not None and residual < self._max_residual:
+                    person_pts[kp_idx] = (pt3d, residual)
+
+            if person_pts:
+                all_points_3d.append(person_pts)
+
+        # Publish 3D markers
+        self._marker_pub.publish(
+            self._build_markers(all_points_3d, left_msg.header.stamp))
+
+        # Draw 2D keypoints on display frames
+        if left_people:
+            draw_keypoints(
+                left_frame,
+                [p['keypoints'] for p in left_people],
+                [p['scores'] for p in left_people],
+                self._threshold)
+        if right_people:
+            draw_keypoints(
+                right_frame,
+                [p['keypoints'] for p in right_people],
+                [p['scores'] for p in right_people],
+                self._threshold)
+
+        n_3d = sum(len(p) for p in all_points_3d)
         cv2.putText(
-            frame,
+            left_frame,
-            f'Subjects: {len(all_kps)}',
+            f'People: {len(left_people)}  3D pts: {n_3d}',
-            (10, 30),
+            (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
-            cv2.FONT_HERSHEY_SIMPLEX,
+        cv2.putText(
-            0.8,
+            right_frame,
-            (255, 255, 255),
+            f'People: {len(right_people)}  Matched: {len(matches)}',
-            2,
+            (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
-        )
-        return frame
 
-    def _left_cb(self, msg: Image) -> None:
+        self._left_display = left_frame
-        self._left_frame = self._process_frame(msg, 'left')
+        self._right_display = right_frame
-
-    def _right_cb(self, msg: Image) -> None:
-        self._right_frame = self._process_frame(msg, 'right')
 
     # ── Display timer ───────────────────────────────────────────────────
 
-    def _display_timer_cb(self) -> None:
+    def _display_timer_cb(self):
-        if self._left_frame is not None:
+        if self._left_display is not None:
-            cv2.imshow('Left - MMPose', self._left_frame)
+            cv2.imshow('Left - Keypoints', self._left_display)
-        if self._right_frame is not None:
+        if self._right_display is not None:
-            cv2.imshow('Right - MMPose', self._right_frame)
+            cv2.imshow('Right - Keypoints', self._right_display)
 
         key = cv2.waitKey(1) & 0xFF
         if key == ord('q'):
-            self.get_logger().info('Quit requested — shutting down.')
+            self.get_logger().info('Quit requested.')
             self.destroy_node()
             rclpy.shutdown()
 
@@ -198,7 +441,7 @@ class KeypointNode(Node):
 
 def main(args=None):
     rclpy.init(args=args)
-    node = KeypointNode()
+    node = KeypointTriangulationNode()
     try:
         rclpy.spin(node)
     except KeyboardInterrupt:

keypoint_pose/launch/keypoint_node.launch.py

@@ -0,0 +1,31 @@
+"""Launch keypoint_node using the mmpose conda environment's Python."""
+
+import os
+from launch import LaunchDescription
+from launch.actions import ExecuteProcess
+
+
+def generate_launch_description():
+    python_exe = os.path.expanduser(
+        '~/miniconda3/envs/mmpose/bin/python3'
+    )
+
+    node_module = 'keypoint_pose.keypoint_node'
+
+    return LaunchDescription([
+        ExecuteProcess(
+            cmd=[
+                python_exe, '-m', node_module,
+                '--ros-args',
+                '-p', 'threshold:=0.3',
+                '-p', 'device:=cuda:0',
+                '-p', 'max_residual:=0.10',
+            ],
+            output='screen',
+            env={
+                **os.environ,
+                'DISPLAY': os.environ.get('DISPLAY', ':1'),
+                'QT_QPA_PLATFORM_PLUGIN_PATH': '',
+            },
+        ),
+    ])

keypoint_pose/package.xml

@@ -10,6 +10,9 @@
   <depend>rclpy</depend>
   <depend>sensor_msgs</depend>
   <depend>cv_bridge</depend>
+  <depend>message_filters</depend>
+  <depend>visualization_msgs</depend>
+  <depend>geometry_msgs</depend>
 
   <test_depend>ament_copyright</test_depend>
   <test_depend>ament_flake8</test_depend>

keypoint_pose/setup.cfg

@@ -2,3 +2,5 @@
 script_dir=$base/lib/keypoint_pose
 [install]
 install_scripts=$base/lib/keypoint_pose
+[build_scripts]
+executable=/usr/bin/env python3

keypoint_pose/setup.py

@@ -1,4 +1,6 @@
 from setuptools import find_packages, setup
+import os
+from glob import glob
 
 package_name = 'keypoint_pose'
 
@@ -10,6 +12,7 @@ setup(
         ('share/ament_index/resource_index/packages',
             ['resource/' + package_name]),
         ('share/' + package_name, ['package.xml']),
+        (os.path.join('share', package_name, 'launch'), glob('launch/*.py')),
     ],
     install_requires=['setuptools'],
     zip_safe=True,

start_cameras_only.launch.py

@@ -0,0 +1,77 @@
+"""Launch camera drivers only (no rectification or stereo processing)."""
+
+import os
+from ament_index_python.packages import get_package_share_directory
+from launch import LaunchDescription
+from launch_ros.actions import ComposableNodeContainer, Node
+from launch_ros.descriptions import ComposableNode
+
+LEFT_CAMERA_NAME = 'left'
+RIGHT_CAMERA_NAME = 'right'
+
+
+def generate_launch_description():
+    config_path = os.path.join(
+        get_package_share_directory('spinnaker_camera_driver'),
+        'config',
+        'blackfly_s.yaml'
+    )
+
+    # ── Camera Drivers (component_container_mt) ─────────────────────────
+    #
+    # Both cameras share a single process because the FLIR Spinnaker SDK
+    # requires USB3 cameras to negotiate bandwidth within one process.
+    #
+    # Topics published (per camera):
+    #   /stereo/{left,right}/image_raw    (sensor_msgs/Image)
+    #   /stereo/{left,right}/camera_info  (sensor_msgs/CameraInfo)
+    camera_container = ComposableNodeContainer(
+        name='camera_container',
+        namespace='',
+        package='rclcpp_components',
+        executable='component_container_mt',
+        composable_node_descriptions=[
+            ComposableNode(
+                package='spinnaker_camera_driver',
+                plugin='spinnaker_camera_driver::CameraDriver',
+                name=LEFT_CAMERA_NAME,
+                namespace='stereo',
+                parameters=[{
+                    'parameter_file': config_path,
+                    'serial_number': '25282106',
+                    'camera_info_url': 'file:///home/sentry/camera_ws/stereoCal/left.yaml',
+                }],
+                extra_arguments=[{'use_intra_process_comms': True}],
+            ),
+            ComposableNode(
+                package='spinnaker_camera_driver',
+                plugin='spinnaker_camera_driver::CameraDriver',
+                name=RIGHT_CAMERA_NAME,
+                namespace='stereo',
+                parameters=[{
+                    'parameter_file': config_path,
+                    'serial_number': '25235293',
+                    'camera_info_url': 'file:///home/sentry/camera_ws/stereoCal/right.yaml',
+                }],
+                extra_arguments=[{'use_intra_process_comms': True}],
+            ),
+        ],
+        output='screen',
+    )
+
+    # ── Static TF: world -> left ─────────────────────────────────────────
+    tf_publisher = Node(
+        package='tf2_ros',
+        executable='static_transform_publisher',
+        arguments=[
+            '--x', '0', '--y', '0', '--z', '0',
+            '--yaw', '0', '--pitch', '0', '--roll', '0',
+            '--frame-id', 'world',
+            '--child-frame-id', 'left',
+        ],
+    )
+
+    return LaunchDescription([
+        camera_container,
+        tf_publisher,
+    ])