Initial TurtleBot subscriber demo

.mypy.ini

@@ -1,6 +1,6 @@
 [mypy]
 files = rerun_py/rerun_sdk/rerun, rerun_py/tests, examples/python
-exclude = examples/python/objectron/proto
+exclude = examples/python/objectron/proto|examples/python/ros
 namespace_packages = True
 show_error_codes = True
 strict = True

examples/python/ros/README.md

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+# Overview
+
+A minimal example of creating a ROS node that subscribes to topics and converts the messages to rerun log calls.
+
+The solution here is mostly a toy example to show how ROS concepts can be mapped to Rerun. Fore more information on
+future improved ROS support, see the tracking issue: [#1527](https://github.com/rerun-io/rerun/issues/1537)
+
+NOTE: Unlike many of the other examples, this example requires a system installation of ROS
+in addition to the packages from requirements.txt.
+
+# Dependencies
+
+This example was developed and tested on top of [ROS2 Humble Hawksbill](https://docs.ros.org/en/humble/index.html)
+and the the [turtlebot3 navigation example](https://navigation.ros.org/getting_started/index.html).
+
+Installing ROS is outside the scope of this example, but you will need the equivalent of the following packages:
+```
+sudo apt install ros-humble-desktop gazebo ros-humble-navigation2 ros-humble-turtlebot3 ros-humble-turtlebot3-gazebo
+```
+
+In addition to installing the dependencies from `requirements.txt` into a venv you will also need to source the
+ROS setup script:
+```
+source venv/bin/active
+source /opt/ros/humble/setup.bash
+```
+
+
+# Running
+
+First, in one terminal launch the nav2 turtlebot demo:
+```
+source /opt/ros/humble/setup.bash
+export TURTLEBOT3_MODEL=waffle
+export GAZEBO_MODEL_PATH=$GAZEBO_MODEL_PATH:/opt/ros/humble/share/turtlebot3_gazebo/models
+
+ros2 launch nav2_bringup tb3_simulation_launch.py headless:=False
+```
+
+As described in the nav demo, use the rviz window to initialize the pose estimate and set a navigation goal.
+
+You can now connect to the running ROS system by running:
+```
+python3 examples/python/ros/main.py
+```
+
+

examples/python/ros/main.py

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+#!/usr/bin/env python3
+"""
+Simple example of a ROS node that republishes some common types to Rerun.
+
+The solution here is mostly a toy example to show how ROS concepts can be
+mapped to Rerun. Fore more information on future improved ROS support,
+see the tracking issue: https://github.com/rerun-io/rerun/issues/1537
+
+NOTE: Unlike many of the other examples, this example requires a system installation of ROS
+in addition to the packages from requirements.txt.
+"""
+
+import argparse
+import sys
+
+import numpy as np
+import rerun as rr
+
+try:
+    import cv_bridge
+    import laser_geometry
+    import rclpy
+    import rerun_urdf
+    import trimesh
+    from image_geometry import PinholeCameraModel
+    from nav_msgs.msg import Odometry
+    from numpy.lib.recfunctions import structured_to_unstructured
+    from rclpy.callback_groups import ReentrantCallbackGroup
+    from rclpy.node import Node
+    from rclpy.qos import QoSDurabilityPolicy, QoSProfile
+    from rclpy.time import Duration, Time
+    from sensor_msgs.msg import CameraInfo, Image, LaserScan, PointCloud2, PointField
+    from sensor_msgs_py import point_cloud2
+    from std_msgs.msg import String
+    from tf2_ros import TransformException
+    from tf2_ros.buffer import Buffer
+    from tf2_ros.transform_listener import TransformListener
+
+except ImportError:
+    print(
+        """
+Could not import the required ROS2 packages.
+
+Make sure you have installed ROS2 (https://docs.ros.org/en/humble/index.html)
+and sourced /opt/ros/humble/setup.bash
+
+See: README.md for more details.
+"""
+    )
+    sys.exit(1)
+
+
+class TurtleSubscriber(Node):  # type: ignore[misc]
+    def __init__(self) -> None:
+        super().__init__("rr_turtlebot")
+
+        # Used for subscribing to latching topics
+        latching_qos = QoSProfile(depth=1, durability=QoSDurabilityPolicy.TRANSIENT_LOCAL)
+
+        # Allow concurrent callbacks
+        self.callback_group = ReentrantCallbackGroup()
+
+        # Subscribe to TF topics
+        self.tf_buffer = Buffer()
+        self.tf_listener = TransformListener(self.tf_buffer, self)
+
+        # Define a mapping for transforms
+        self.path_to_frame = {
+            "map": "map",
+            "map/points": "camera_depth_frame",
+            "map/robot": "base_footprint",
+            "map/robot/scan": "base_scan",
+            "map/robot/camera": "camera_rgb_optical_frame",
+            "map/robot/camera/points": "camera_depth_frame",
+        }
+
+        # Assorted helpers for data conversions
+        self.model = PinholeCameraModel()
+        self.cv_bridge = cv_bridge.CvBridge()
+        self.laser_proj = laser_geometry.laser_geometry.LaserProjection()
+
+        # Log a bounding box as a visual placeholder for the map
+        # # TODO(jleibs): Log the real map once [#1531](https://github.com/rerun-io/rerun/issues/1531) is merged
+        rr.log_obb(
+            "map/box",
+            half_size=[6, 6, 2],
+            position=[0, 0, 1],
+            color=[255, 255, 255, 255],
+            timeless=True,
+        )
+
+        # Subscriptions
+        self.info_sub = self.create_subscription(
+            CameraInfo,
+            "/intel_realsense_r200_depth/camera_info",
+            self.cam_info_callback,
+            10,
+            callback_group=self.callback_group,
+        )
+
+        self.odom_sub = self.create_subscription(
+            Odometry,
+            "/odom",
+            self.odom_callback,
+            10,
+            callback_group=self.callback_group,
+        )
+
+        self.img_sub = self.create_subscription(
+            Image,
+            "/intel_realsense_r200_depth/image_raw",
+            self.image_callback,
+            10,
+            callback_group=self.callback_group,
+        )
+
+        self.points_sub = self.create_subscription(
+            PointCloud2,
+            "/intel_realsense_r200_depth/points",
+            self.points_callback,
+            10,
+            callback_group=self.callback_group,
+        )
+
+        self.scan_sub = self.create_subscription(
+            LaserScan,
+            "/scan",
+            self.scan_callback,
+            10,
+            callback_group=self.callback_group,
+        )
+
+        # The urdf is published as latching
+        self.urdf_sub = self.create_subscription(
+            String,
+            "/robot_description",
+            self.urdf_callback,
+            qos_profile=latching_qos,
+            callback_group=self.callback_group,
+        )
+
+    def log_tf_as_rigid3(self, path: str, time: Time) -> None:
+        """
+        Helper to look up a transform with tf and log using `log_rigid3`.
+
+        Note: we do the lookup on the client side instead of re-logging the raw transforms until
+        Rerun has support for Derived Transforms [#1533](https://github.com/rerun-io/rerun/issues/1533)
+        """
+        # Get the parent path
+        parent_path = path.rsplit("/", 1)[0]
+
+        # Find the corresponding frames from the mapping
+        child_frame = self.path_to_frame[path]
+        parent_frame = self.path_to_frame[parent_path]
+
+        # Do the TF lookup to get transform from child (source) -> parent (target)
+        try:
+            tf = self.tf_buffer.lookup_transform(parent_frame, child_frame, time, timeout=Duration(seconds=0.1))
+            t = tf.transform.translation
+            q = tf.transform.rotation
+            rr.log_rigid3(path, parent_from_child=([t.x, t.y, t.z], [q.x, q.y, q.z, q.w]))
+        except TransformException as ex:
+            print("Failed to get transform: {}".format(ex))
+
+    def cam_info_callback(self, info: CameraInfo) -> None:
+        """Log a `CameraInfo` with `log_pinhole`."""
+        time = Time.from_msg(info.header.stamp)
+        rr.set_time_nanos("ros_time", time.nanoseconds)
+
+        self.model.fromCameraInfo(info)
+
+        rr.log_pinhole(
+            "map/robot/camera/img",
+            child_from_parent=self.model.intrinsicMatrix(),
+            width=self.model.width,
+            height=self.model.height,
+        )
+
+    def odom_callback(self, odom: Odometry) -> None:
+        """Update transforms when odom is updated."""
+        time = Time.from_msg(odom.header.stamp)
+        rr.set_time_nanos("ros_time", time.nanoseconds)
+
+        # Capture time-series data for the linear and angular velocities
+        rr.log_scalar("odometry/vel", odom.twist.twist.linear.x)
+        rr.log_scalar("odometry/ang_vel", odom.twist.twist.angular.z)
+
+        # Update the robot pose itself via TF
+        self.log_tf_as_rigid3("map/robot", time)
+
+    def image_callback(self, img: Image) -> None:
+        """Log an `Image` with `log_image` using `cv_bridge`."""
+        time = Time.from_msg(img.header.stamp)
+        rr.set_time_nanos("ros_time", time.nanoseconds)
+
+        rr.log_image("map/robot/camera/img", self.cv_bridge.imgmsg_to_cv2(img))
+        self.log_tf_as_rigid3("map/robot/camera", time)
+
+    def points_callback(self, points: PointCloud2) -> None:
+        """Log a `PointCloud2` with `log_points`."""
+        time = Time.from_msg(points.header.stamp)
+        rr.set_time_nanos("ros_time", time.nanoseconds)
+
+        pts = point_cloud2.read_points(points, field_names=["x", "y", "z"], skip_nans=True)
+
+        # The realsense driver exposes a float field called 'rgb', but the data is actually stored
+        # as bytes within the payload (not a float at all). Patch points.field to use the correct
+        # r,g,b, offsets so we can extract them with read_points.
+        points.fields = [
+            PointField(name="r", offset=16, datatype=PointField.UINT8, count=1),
+            PointField(name="g", offset=17, datatype=PointField.UINT8, count=1),
+            PointField(name="b", offset=18, datatype=PointField.UINT8, count=1),
+        ]
+
+        colors = point_cloud2.read_points(points, field_names=["r", "g", "b"], skip_nans=True)
+
+        pts = structured_to_unstructured(pts)
+        colors = colors = structured_to_unstructured(colors)
+
+        # Log points once rigidly under robot/camera/points. This is a robot-centric
+        # view of the world.
+        rr.log_points("map/robot/camera/points", positions=pts, colors=colors)
+        self.log_tf_as_rigid3("map/robot/camera/points", time)
+
+        # Log points a second time after transforming to the map frame. This is a map-centric
+        # view of the world.
+        #
+        # Once Rerun supports fixed-frame aware transforms [#1522](https://github.com/rerun-io/rerun/issues/1522)
+        # this will no longer be necessary.
+        rr.log_points("map/points", positions=pts, colors=colors)
+        self.log_tf_as_rigid3("map/points", time)
+
+    def scan_callback(self, scan: LaserScan) -> None:
+        """
+        Log a LaserScan after transforming it to line-segments.
+
+        Note: we do a client-side transformation of the LaserScan data into Rerun
+        points / lines until Rerun has native support for LaserScan style projections:
+        [#1534](https://github.com/rerun-io/rerun/issues/1534)
+        """
+        time = Time.from_msg(scan.header.stamp)
+        rr.set_time_nanos("ros_time", time.nanoseconds)
+
+        # Project the laser scan to a collection of points
+        points = self.laser_proj.projectLaser(scan)
+        pts = point_cloud2.read_points(points, field_names=["x", "y", "z"], skip_nans=True)
+        pts = structured_to_unstructured(pts)
+
+        # Turn every pt into a line-segment from the origin to the point.
+        origin = (pts / np.linalg.norm(pts, axis=1).reshape(-1, 1)) * 0.3
+        segs = np.hstack([origin, pts]).reshape(pts.shape[0] * 2, 3)
+
+        rr.log_line_segments("map/robot/scan", segs, stroke_width=0.005)
+        self.log_tf_as_rigid3("map/robot/scan", time)
+
+    def urdf_callback(self, urdf_msg: String) -> None:
+        """Log a URDF using `log_scene` from `rerun_urdf`."""
+        urdf = rerun_urdf.load_urdf_from_msg(urdf_msg)
+
+        # The turtlebot URDF appears to have scale set incorrectly for the camera-link
+        # Although rviz loads it properly `yourdfpy` does not.
+        orig, _ = urdf.scene.graph.get("camera_link")
+        scale = trimesh.transformations.scale_matrix(0.00254)
+        urdf.scene.graph.update(frame_to="camera_link", matrix=orig.dot(scale))
+        scaled = urdf.scene.scaled(1.0)
+
+        rerun_urdf.log_scene(scene=scaled, node=urdf.base_link, path="map/robot/urdf", timeless=True)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Simple example of a ROS node that republishes to Rerun.")
+    rr.script_add_args(parser)
+    args, unknownargs = parser.parse_known_args()
+    rr.script_setup(args, "turtlebot_viz")
+
+    # Any remaining args go to rclpy
+    rclpy.init(args=unknownargs)
+
+    turtle_subscriber = TurtleSubscriber()
+
+    # Use the MultiThreadedExecutor so that calls to `lookup_transform` don't block the other threads
+    rclpy.spin(turtle_subscriber, executor=rclpy.executors.MultiThreadedExecutor())
+
+    turtle_subscriber.destroy_node()
+    rclpy.shutdown()
+
+
+if __name__ == "__main__":
+    main()

examples/python/ros/requirements.txt

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+numpy
+opencv-python
+rerun-sdk
+yourdfpy