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Robot Localization Failure

Highly dynamic environments as the one depicted below still remain a challenge for localization systems of mobile robots. Predictively monitoring the performance of robot localization systems improves the reliability and efficiency of autonomous navigation. Predictively monitoring the performance of a robot localization system in a challenging environment

This example (which can be found in flowcean/examples/ros_offline ) is assuming the robot utilizes the Robot Operating System(ROS) which is a software framework that is widely used in the robotics community. We are using the version ROS 2 Humble. ROS allows users to record robotics data, such as sensor data or internal data of running processes in so called rosbags. Flowcean supports rosbag data using its RosbagLoader environment. Here is a code snippet from the run.py.

from flowcean.environments.rosbag import RosbagLoader

environment = RosbagLoader(
            path="rec_20241021_152106",
            topics={
                "/amcl_pose": [
                    "pose.pose.position.x",
                    "pose.pose.position.y",
                ],
                "/momo/pose": [
                    "pose.position.x",
                    "pose.position.y",
                ],
                "/scan": [
                    "ranges",
                ],
                "/particle_cloud": ["particles"],
                "/position_error": ["data"],
                "/heading_error": ["data"],
            },
            msgpaths=[
                "/opt/ros/humble/share/sensor_msgs/msg/LaserScan.msg",
                "/opt/ros/humble/share/nav2_msgs/msg/ParticleCloud.msg",
                "/opt/ros/humble/share/nav2_msgs/msg/Particle.msg",
            ],
        )

The RosbagLoader requires a list of ROS topics to be loaded along with a specification which fields of the ROS message should be extracted. Here is a brief explanation of the topics that are loaded in this example:

  • /amcl_pose: this is the pose that the localization algorithm of the robot estimates. AMCL stands for Adaptive Monte Carlo Localization and is a commonly used localization approach using a range sensor, e.g. a LiDAR sensor.
  • /momo/pose: this is the pose provided by a motion capturing system that is installed in our lab. This data is used to determine the error of the data provided by AMCL.
  • /scan: this is the data from the LiDAR sensor.
  • /particle_cloud: AMCL is a probabilistic localization approach that uses a set of particles (possible poses of the robot) to describe the probability distribution for the robot in space.
  • /position_error: The position error calculated using the euclidean distance.
  • /heading_error: The heading error in degrees.

This example is still under development. The learning pipeline and the documentation of it will be completed in the future.