added local conf

scripts/calibrate_lidar.py

@@ -83,7 +83,7 @@ class LidarTransformPublisher(Node):
         self.lidar1_buffer = lidar1_buffer
         self.lidar2_buffer = lidar2_buffer
         self.lidar1_frame = lidar1_frame
-        self.lidar2_frame = lidar2_frame
+        self.lidar2_frame = "livox_base" # lidar2_frame
 
     def publish(self):
         self.pcd_1 = self.pcd_buffer_to_o3d(self.lidar1_buffer)

src/sherpa/launch/calibration.launch.py

@@ -0,0 +1,104 @@
+import os.path
+
+from ament_index_python.packages import get_package_share_directory
+
+from launch import LaunchDescription
+from launch_ros.actions import Node
+from launch.substitutions import LaunchConfiguration, PathJoinSubstitution
+from launch_ros.substitutions import FindPackageShare
+from launch.conditions import IfCondition
+import math
+
+def generate_launch_description():
+    # ----- microros -----
+    servo_uros_node = Node(
+        package='micro_ros_agent',
+        executable='micro_ros_agent',
+        arguments=["serial", "--dev", "/dev/ttyACM0", "--baud", "921600"],
+    )
+
+    servo_angle_tf_node = Node(
+        package='sherpa_lidar_utils',
+        executable='servo_angle_tf.py',
+    )
+
+    # ----- lidar position -----
+    lidar_position_base_tf_node = Node(
+        package='tf2_ros',
+        executable='static_transform_publisher',
+        arguments=['0', '0', '0', '0', '0', '0', '1', 'velodyne', 'lidar_base'],
+    )
+
+    # ----- lidar topics -----
+    vlp16_driver_node = Node(
+        package='velodyne_driver',
+        executable='velodyne_driver_node',
+        arguments=["--ros-args", "-p", "model:=VLP16", "-p", "rpm:=600.0", "-p", "device_ip:=10.42.200.201"],
+        remappings=[
+            ('/velodyne_packets', '/lidar/vlp16/packets'),
+        ],
+    )
+
+    vlp16_pointcloud_node = Node(
+        package='velodyne_pointcloud',
+        executable='velodyne_transform_node',
+        arguments=["--ros-args", "-p", f"calibration:={os.path.join(get_package_share_directory('sherpa'), 'config', 'vlp-16-pointcloud.yaml')}", "-p", "model:=VLP16"],
+        remappings=[
+            ('/velodyne_packets', '/lidar/vlp16/packets'),
+            ('/velodyne_points', '/lidar/vlp16/points'),
+        ]
+    )
+
+    mid40_driver_node = Node(
+        package='livox_ros2_driver',
+        executable='livox_ros2_driver_node',
+        arguments=[],
+        remappings=[
+            ('/livox/lidar', '/lidar/mid40/points'),
+        ],
+    )
+
+    # ----- pointcloud downsampling -----
+    downsample_vlp16_node = Node(
+        package='target_tracking',
+        executable='cloud_preprocessing_node',
+        parameters=[
+            {"topic_in": "/lidar/vlp16/points"},
+            {"topic_out": "/lidar/vlp16/downsampled"},
+            {"x_min": 0.0},
+            {"x_max": 100.0},
+            {"z_min": -5.0},
+            {"z_max": 10.0},
+            {"tan_h_fov": math.pi}, # 180°  
+            {"tan_v_fov": math.pi / 5}, # ~36°  
+        ],
+    )
+
+    downsample_mid40_node = Node(
+        package='target_tracking',
+        executable='cloud_preprocessing_node',
+        parameters=[
+            {"topic_in": "/lidar/mid40/points"},
+            {"topic_out": "/lidar/mid40/downsampled"},
+            {"x_min": 0.0},
+            {"x_max": 100.0},
+            {"z_min": -5.0},
+            {"z_max": 10.0},
+            {"tan_h_fov": math.pi / 4}, # 45°  
+            {"tan_v_fov": math.pi / 4}, # 45°  
+        ],
+    )
+
+    # ----- create ld -----
+    ld = LaunchDescription()   
+
+    ld.add_action(servo_uros_node)
+    ld.add_action(servo_angle_tf_node)
+    ld.add_action(lidar_position_base_tf_node)
+    ld.add_action(vlp16_driver_node)
+    ld.add_action(vlp16_pointcloud_node)
+    ld.add_action(mid40_driver_node)
+    ld.add_action(downsample_vlp16_node)
+    ld.add_action(downsample_mid40_node)
+    
+    return ld

src/sherpa/launch/tracking.launch.py

@@ -0,0 +1,159 @@
+import os.path
+
+from ament_index_python.packages import get_package_share_directory
+
+from launch import LaunchDescription
+from launch_ros.actions import Node
+from launch.substitutions import LaunchConfiguration, PathJoinSubstitution
+from launch_ros.substitutions import FindPackageShare
+from launch.conditions import IfCondition
+import math
+
+def generate_launch_description():
+    # ----- microros -----
+    servo_uros_node = Node(
+        package='micro_ros_agent',
+        executable='micro_ros_agent',
+        arguments=["serial", "--dev", "/dev/ttyACM0", "--baud", "921600"],
+    )
+
+    servo_angle_tf_node = Node(
+        package='sherpa_lidar_utils',
+        executable='servo_angle_tf.py',
+    )
+
+    # ----- lidar position -----
+    lidar_position_tf_node = Node(
+        package='sherpa_lidar_utils',
+        executable='publish_calibration.py',
+        arguments=["--ros-args", "-p", "calibration:=/home/sherpa/lidar_calibration"],
+    )
+
+    lidar_position_base_tf_node = Node(
+        package='tf2_ros',
+        executable='static_transform_publisher',
+        arguments=['0', '0', '0', '0', '0', '0', '1', 'velodyne', 'lidar_base'],
+    )
+
+    # ----- lidar topics -----
+    vlp16_driver_node = Node(
+        package='velodyne_driver',
+        executable='velodyne_driver_node',
+        arguments=["--ros-args", "-p", "model:=VLP16", "-p", "rpm:=600.0", "-p", "device_ip:=10.42.200.201"],
+        remappings=[
+            ('/velodyne_packets', '/lidar/vlp16/packets'),
+        ],
+    )
+
+    vlp16_pointcloud_node = Node(
+        package='velodyne_pointcloud',
+        executable='velodyne_transform_node',
+        arguments=["--ros-args", "-p", f"calibration:={os.path.join(get_package_share_directory('sherpa'), 'config', 'vlp-16-pointcloud.yaml')}", "-p", "model:=VLP16"],
+        remappings=[
+            ('/velodyne_packets', '/lidar/vlp16/packets'),
+            ('/velodyne_points', '/lidar/vlp16/points'),
+        ]
+    )
+
+    mid40_driver_node = Node(
+        package='livox_ros2_driver',
+        executable='livox_ros2_driver_node',
+        arguments=[],
+        remappings=[
+            ('/livox/lidar', '/lidar/mid40/points'),
+        ],
+    )
+
+    # ----- pointcloud downsampling -----
+    downsample_vlp16_node = Node(
+        package='target_tracking',
+        executable='cloud_preprocessing_node',
+        parameters=[
+            {"topic_in": "/lidar/vlp16/points"},
+            {"topic_out": "/lidar/vlp16/downsampled"},
+            {"x_min": 0.0},
+            {"x_max": 100.0},
+            {"z_min": -5.0},
+            {"z_max": 10.0},
+            {"tan_h_fov": math.pi}, # 180°  
+            {"tan_v_fov": math.pi / 5}, # ~36°  
+        ],
+    )
+
+    downsample_mid40_node = Node(
+        package='target_tracking',
+        executable='cloud_preprocessing_node',
+        parameters=[
+            {"topic_in": "/lidar/mid40/points"},
+            {"topic_out": "/lidar/mid40/downsampled"},
+            {"x_min": 0.0},
+            {"x_max": 100.0},
+            {"z_min": -5.0},
+            {"z_max": 10.0},
+            {"tan_h_fov": math.pi / 4}, # 45°  
+            {"tan_v_fov": math.pi / 4}, # 45°  
+        ],
+    )
+
+    # ----- pointcloud merging -----
+    pointcloud_merger_node = Node(
+        package='scans_merger',
+        executable='scans_merger',
+        parameters=[
+            {"destination_frame": "lidar_base"},
+            {"input_cloud_1": "/lidar/vlp16/downsampled"},
+            {"input_cloud_2": "/lidar/mid40/downsampled"},
+            {"merged_cloud": "/lidar/merged/points"},
+        ],
+    )
+
+    # ----- tracking -----
+    cloud_clustering_node = Node(
+        package='target_tracking',
+        executable='cloud_clustering_node',
+        parameters=[
+            {"topic_in": "/lidar/merged/points"},
+            {"frame_id": 'lidar_base'},
+            {"z_dim_scale": 1.0},
+            #{"cluster_tolerance": 0.065},
+            #{"min_cluster_size": 450},
+            #{"max_cluster_size": 1650},
+            {"min_cluster_size": 125},
+            {"max_cluster_size": 2250},
+            {"cluster_tolerance": 0.15},
+        ],
+        remappings=[
+            ('/cluster5', '/tracking/cluster0'),
+            ('/cluster4', '/tracking/cluster1'),
+            ('/cluster3', '/tracking/cluster2'),
+            ('/cluster2', '/tracking/cluster3'),
+            ('/cluster1', '/tracking/cluster4'),
+            ('/cluster0', '/tracking/cluster5'),
+        ],
+    )
+
+    servo_tracking_node = Node(
+        package='sherpa_lidar_utils',
+        executable='track_cluster.py',
+        remappings=[
+            ('/tracking/target', '/tracking/cluster0'),
+        ],
+    )
+
+    # ----- create ld -----
+    ld = LaunchDescription()   
+
+    ld.add_action(servo_uros_node)
+    ld.add_action(servo_angle_tf_node)
+    ld.add_action(lidar_position_tf_node)
+    ld.add_action(lidar_position_base_tf_node)
+    ld.add_action(vlp16_driver_node)
+    ld.add_action(vlp16_pointcloud_node)
+    ld.add_action(mid40_driver_node)
+    ld.add_action(downsample_vlp16_node)
+    ld.add_action(downsample_mid40_node)
+    ld.add_action(pointcloud_merger_node)
+    ld.add_action(cloud_clustering_node)
+    ld.add_action(servo_tracking_node)
+
+    return ld

src/sherpa_lidar_utils/CMakeLists.txt

@@ -0,0 +1,15 @@
+cmake_minimum_required(VERSION 3.8)
+project(sherpa_lidar_utils)
+
+find_package(ament_cmake REQUIRED)
+find_package(rclpy REQUIRED)
+
+# Install the Python script into the lib directory of the package
+install(PROGRAMS
+  src/publish_calibration.py
+  src/servo_angle_tf.py
+  src/track_cluster.py
+  DESTINATION lib/${PROJECT_NAME}
+)
+
+ament_package()

src/sherpa_lidar_utils/package.xml

@@ -0,0 +1,21 @@
+<?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>sherpa_lidar_utils</name>
+  <version>0.0.1</version>
+  <description>Studienprojekt</description>
+  <maintainer email="timoschneider04@gmail.com">timo</maintainer>
+  <license>Apache 2.0</license>
+
+  <buildtool_depend>ament_cmake</buildtool_depend>
+
+  <test_depend>ament_lint_auto</test_depend>
+  <test_depend>ament_lint_common</test_depend>
+
+  <!-- Dependencies for the robot control system -->
+  <depend>rclpy</depend>
+
+  <export>
+    <build_type>ament_cmake</build_type>
+  </export>
+</package>

src/sherpa_lidar_utils/src/publish_calibration.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python3
+"""
+average_tf_publisher.py
+Compute the mean of all 4x4 transform .npy files in a folder
+and broadcast it as a static TF at 50 Hz.
+
+Usage:
+    ros2 run <your_package> average_tf_publisher.py [directory] [parent_frame] [child_frame]
+
+Default parameters:
+    directory     = "calibration"
+    parent_frame  = "world"
+    child_frame   = "average_lidar"
+"""
+
+import os
+import sys
+import glob
+import numpy as np
+from scipy.spatial.transform import Rotation as R
+
+import rclpy
+from rclpy.node import Node
+from tf2_ros.static_transform_broadcaster import StaticTransformBroadcaster
+from geometry_msgs.msg import TransformStamped
+
+
+class AverageTFPublisher(Node):
+    def __init__(self):
+        super().__init__("average_tf_publisher")
+        self.declare_parameter('calibration', 'calibration')
+        self.declare_parameter('parent_frame', 'velodyne')
+        self.declare_parameter('child_frame', 'livox_base')
+        self.declare_parameter('hz', 50.0)
+
+        # Read the values
+        directory    = self.get_parameter('calibration').value
+        self.parent_frame = self.get_parameter('parent_frame').value
+        self.child_frame  = self.get_parameter('child_frame').value
+
+        self.get_logger().info(f"Loading transforms from: {directory}")
+        transforms = self._load_transforms(directory)
+        if len(transforms) == 0:
+            raise RuntimeError(f"No valid .npy transforms found in {directory}")
+
+        self.get_logger().info(f"Loaded {len(transforms)} transforms.")
+        self.mean_t, self.mean_q = self._compute_mean_transform(transforms)
+        self.get_logger().info(
+            f"Average translation: {self.mean_t}\n"
+            f"Average quaternion:   {self.mean_q}"
+        )
+
+        self.br = StaticTransformBroadcaster(self)
+
+        # Publish at 50 Hz (static TF can be published once, but continuous
+        # publishing helps late-joining nodes)
+        period = 1.0 / self.get_parameter('hz').value
+        self.timer = self.create_timer(period, self._timer_cb)
+
+    def _load_transforms(self, directory):
+        """Load all 4x4 numpy transforms from directory."""
+        files = sorted(glob.glob(os.path.join(directory, "*.npy")))
+        mats = []
+        for f in files:
+            try:
+                T = np.load(f)
+                if T.shape == (4, 4):
+                    mats.append(T)
+                else:
+                    self.get_logger().warn(f"Skipping {f}: wrong shape {T.shape}")
+            except Exception as e:
+                self.get_logger().warn(f"Failed loading {f}: {e}")
+        return mats
+
+    def _compute_mean_transform(self, mats):
+        """Return (mean_translation, mean_quaternion)"""
+        translations = []
+        quats = []
+        for T in mats:
+            translations.append(T[:3, 3])
+            q = R.from_matrix(T[:3, :3]).as_quat()  # [x,y,z,w]
+            quats.append(q)
+
+        # mean translation
+        t_mean = np.mean(np.vstack(translations), axis=0)
+
+        # quaternion average using eigenvector method
+        Q = np.vstack(quats)
+        A = Q.T @ Q
+        eigvals, eigvecs = np.linalg.eigh(A)
+        q_mean = eigvecs[:, np.argmax(eigvals)]
+        if q_mean[3] < 0:  # ensure w positive for consistency
+            q_mean = -q_mean
+        return t_mean, q_mean  # q_mean = [x,y,z,w]
+
+    def _timer_cb(self):
+        t = TransformStamped()
+        t.header.stamp = self.get_clock().now().to_msg()
+        t.header.frame_id = self.parent_frame
+        t.child_frame_id = self.child_frame
+
+        t.transform.translation.x = float(self.mean_t[0])
+        t.transform.translation.y = float(self.mean_t[1])
+        t.transform.translation.z = float(self.mean_t[2])
+        t.transform.rotation.x = float(self.mean_q[0])
+        t.transform.rotation.y = float(self.mean_q[1])
+        t.transform.rotation.z = float(self.mean_q[2])
+        t.transform.rotation.w = float(self.mean_q[3])
+
+        self.br.sendTransform(t)
+
+
+def main(argv=None):
+    rclpy.init(args=argv)
+
+    try:
+        node = AverageTFPublisher()
+        rclpy.spin(node)
+    except KeyboardInterrupt:
+        pass
+    finally:
+        rclpy.shutdown()
+
+
+if __name__ == "__main__":
+    main()

src/sherpa_lidar_utils/src/servo_angle_tf.py

@@ -0,0 +1,84 @@
+#!/usr/bin/env python3
+import rclpy
+from rclpy.node import Node
+from std_msgs.msg import Float32
+from geometry_msgs.msg import TransformStamped
+import tf2_ros
+import math
+
+def euler_to_quaternion(roll, pitch, yaw):
+    """Convert Euler angles to quaternion (x, y, z, w)."""
+    cr = math.cos(roll / 2)
+    sr = math.sin(roll / 2)
+    cp = math.cos(pitch / 2)
+    sp = math.sin(pitch / 2)
+    cy = math.cos(yaw / 2)
+    sy = math.sin(yaw / 2)
+
+    w = cr * cp * cy + sr * sp * sy
+    x = sr * cp * cy - cr * sp * sy
+    y = cr * sp * cy + sr * cp * sy
+    z = cr * cp * sy - sr * sp * cy
+    return x, y, z, w
+
+
+class ServoTfNode(Node):
+    """Publishes TF for a servo based on its current position."""
+
+    def __init__(self):
+        super().__init__("stepper_tf_node")
+
+        # Subscribe to current servo position
+        self.pos_sub = self.create_subscription(
+            Float32,
+            "/nucleo/stepper0/position",
+            self.pos_callback,
+            10
+        )
+
+        # TF broadcaster
+        self.br = tf2_ros.TransformBroadcaster(self)
+        self.get_logger().info("Servo TF node started, subscribing to /nucleo/stepper0/position")
+
+    def pos_callback(self, msg: Float32):
+        """Publish a TF corresponding to the servo position."""
+        angle_deg = msg.data  # get float from subscriber
+
+        t = TransformStamped()
+        #t.header.stamp = (self.get_clock().now() - rclpy.duration.Duration(seconds=0.1)).to_msg()
+        t.header.stamp = self.get_clock().now().to_msg()
+        t.header.frame_id = "livox_base"
+        t.child_frame_id = "frame_default"
+
+        t.transform.translation.x = 0.0
+        t.transform.translation.y = 0.0
+        t.transform.translation.z = 0.0
+
+        # Convert angle to yaw (radians) and then to quaternion
+        yaw = angle_deg * math.pi / 180.0
+        qx, qy, qz, qw = euler_to_quaternion(0, 0, yaw)
+
+        t.transform.rotation.x = qx
+        t.transform.rotation.y = qy
+        t.transform.rotation.z = qz
+        t.transform.rotation.w = qw
+
+        # Publish the transform
+        self.br.sendTransform(t)
+
+
+def main(args=None):
+    rclpy.init(args=args)
+    node = ServoTfNode()
+    try:
+        rclpy.spin(node)
+    except KeyboardInterrupt:
+        pass
+    finally:
+        node.destroy_node()
+        rclpy.shutdown()
+
+
+if __name__ == "__main__":
+    main()
+

src/sherpa_lidar_utils/src/track_cluster.py

@@ -0,0 +1,95 @@
+#!/usr/bin/env python3
+import rclpy
+from rclpy.node import Node
+from sensor_msgs.msg import PointCloud2
+from geometry_msgs.msg import PointStamped
+from std_msgs.msg import Float32
+from tf2_ros import Buffer, TransformListener
+import tf2_geometry_msgs
+import numpy as np
+import math
+import sensor_msgs_py.point_cloud2 as pc2
+
+class Cluster5Tracker(Node):
+    def __init__(self):
+        super().__init__('cluster_tracker')
+
+        self.source_frame = 'lidar_base'
+        self.target_frame = 'livox_base'
+
+        self.buf = []
+
+        # Subscribe to point cloud of cluster 5
+        self.sub = self.create_subscription(
+            PointCloud2,
+            '/tracking/target',
+            self.cb,
+            10
+        )
+
+        # Publishers
+        self.centroid_pub = self.create_publisher(PointStamped, '/tracking/target_pos', 10)
+        self.angle_pub = self.create_publisher(Float32, '/nucleo/stepper0/target', 10)
+
+        # TF
+        self.tf_buffer = Buffer()
+        self.tf_listener = TransformListener(self.tf_buffer, self)
+
+    def cb(self, msg: PointCloud2):
+        # Extract raw x, y, z as a regular list
+        points = [[p[0], p[1], p[2]] for p in pc2.read_points(msg, field_names=("x","y","z"), skip_nans=True)]
+        if len(points) == 0:
+            self.get_logger().warn("No points in cluster5 PointCloud2")
+            return
+
+        arr = np.array(points, dtype=np.float32)
+        centroid = np.mean(arr, axis=0)
+
+        pt = PointStamped()
+        pt.header.frame_id = msg.header.frame_id
+        pt.header.stamp = msg.header.stamp
+        pt.point.x, pt.point.y, pt.point.z = centroid
+
+        try:
+            # Transform to livox_base
+            t = self.tf_buffer.lookup_transform(
+                self.target_frame,
+                pt.header.frame_id,
+                pt.header.stamp,
+                timeout=rclpy.duration.Duration(seconds=1.0)
+            )
+            pt_transformed = tf2_geometry_msgs.do_transform_point(pt, t)
+        except Exception as e:
+            self.get_logger().warn(f"TF lookup failed: {e}")
+            return
+
+        # Publish centroid
+        self.centroid_pub.publish(pt_transformed)
+
+        # Compute yaw angle in degrees
+        x, y = pt_transformed.point.x, pt_transformed.point.y
+        yaw_deg = -math.degrees(math.atan2(y, x))
+
+        self.buf.append(yaw_deg)
+        if len(self.buf) > 15:
+            self.buf.pop(0)
+
+        angle_msg = Float32()
+        angle_msg.data = sum(self.buf) / len(self.buf)
+        self.angle_pub.publish(angle_msg)
+
+        #self.get_logger().info(f"Centroid: ({x:.2f}, {y:.2f}, {pt_transformed.point.z:.2f}), Yaw: {yaw_deg:.2f}°")
+
+def main():
+    rclpy.init()
+    node = Cluster5Tracker()
+    try:
+        rclpy.spin(node)
+    except KeyboardInterrupt:
+        pass
+    finally:
+        node.destroy_node()
+        rclpy.shutdown()
+
+if __name__ == "__main__":
+    main()

src/target_tracking/launch/target_tracking_launch.py

@@ -5,8 +5,13 @@ import math
 ################### user configure parameters for ros2 start ###################
 # Topics/Frames
 frame_id        = 'velodyne'
+<<<<<<< Updated upstream
 topic_preprocessing_in  = 'filtered_points'
 topic_preprocessing_out = 'new_filtered'
+=======
+topic_preprocessing_in  = 'merged_cloud'
+topic_preprocessing_out = 'filtered_points'
+>>>>>>> Stashed changes
 
 # Preprocessing
 x_min           = 0.0
@@ -17,6 +22,7 @@ tan_h_fov       = math.pi / 4 # ±45°
 tan_v_fov       = math.pi / 6 # ±30°
 
 # Clustering
+<<<<<<< Updated upstream
 z_dim_scale     = 0.1
 cluster_tolerance   = 0.3
 min_cluster_size    = 10
@@ -27,6 +33,12 @@ min_length  = 0.0
 max_width   = 1.5
 max_height  = 2.5
 max_length  = 1.5
+=======
+z_dim_scale     = 1.0
+cluster_tolerance   = 0.1
+min_cluster_size    = 350
+max_cluster_size    = 1500
+>>>>>>> Stashed changes
 
 ################### user configure parameters for ros2 end ##################### 
 

src/target_tracking/src/cloud_clustering.cpp

@@ -100,7 +100,7 @@ namespace cloud_clustering
     // Create a ROS subscriber for the input point cloud
     sub = this->create_subscription<sensor_msgs::msg::PointCloud2>(
         topic_in,
-        10, // queue size
+        rclcpp::SensorDataQoS(), // queue size
         std::bind(&CloudClustering::cloud_cb, this, std::placeholders::_1));
 
     std::cout << "Started clustering node with parameters:\n"

src/target_tracking/src/cloud_preprocessing.cpp

@@ -153,11 +153,12 @@ private:
     // Convert and publish
     sensor_msgs::msg::PointCloud2 out_msg;
     pcl::toROSMsg(*voxel_filtered, out_msg);
-    out_msg.header.stamp = this->get_clock()->now();
+    out_msg.header.stamp = msg->header.stamp;
     out_msg.header.frame_id = msg->header.frame_id;
     filtered_publisher_->publish(out_msg);
 
     //RCLCPP_INFO(this->get_logger(), "Filtered %zu -> %zu points", input->points.size(), voxel_filtered->points.size());
+<<<<<<< Updated upstream
     //auto stop = std::chrono::high_resolution_clock::now();
     //auto duration = std::chrono::duration_cast<std::chrono::microseconds>(stop - start);
 
@@ -165,6 +166,8 @@ private:
     // member function on the duration object
     //std::cout << duration.count() << std::endl;
 
+=======
+>>>>>>> Stashed changes
   }
 
   tf2_ros::Buffer tf_buffer_;