A ROS package for robot-centric elevation mapping that handles pose uncertainty for navigation on rough terrain.
Elevation Mapping is a ROS package that creates local elevation maps for mobile robots navigating rough terrain. It uses robot-centric mapping to handle pose estimation drift by aggregating uncertainty through the robot's motion, providing a probabilistic terrain representation. The package processes distance sensor data (e.g., from LiDAR or depth cameras) to generate elevation maps that are essential for locomotion planning and local navigation.
Robotics researchers and engineers working on mobile robot navigation, particularly those using legged robots or ground vehicles in unstructured outdoor environments. It's designed for teams with ROS expertise who need reliable terrain perception for autonomous locomotion.
Developers choose Elevation Mapping for its explicit handling of pose uncertainty, which is critical for robots operating in GPS-denied or slippery terrain. Its robot-centric approach and probabilistic fusion provide more reliable maps than traditional methods when localization drifts, and its tight ROS integration makes it easy to incorporate into existing autonomy stacks.
Robot-centric elevation mapping for rough terrain navigation
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Explicitly handles localization uncertainty by maintaining a robot-centric map that aggregates drift, crucial for navigation in rough terrain as described in the cited papers.
Uses Mahalanobis distance thresholds to fuse sensor data probabilistically, providing reliable height estimates even with noisy inputs, as detailed in the parameters section.
Supports various distance sensors like Kinect and LiDAR through configurable input sources, demonstrated in the parameter examples for pointcloud topics.
Includes visibility cleanup and continuous cleanup options to manage moving objects and outdated measurements, enhancing map accuracy in changing terrains.
The README notes it is no longer actively maintained, risking compatibility issues with newer ROS versions and lack of bug fixes or support.
Depends on multiple ROS packages and libraries like Grid Map, kindr, and PCL, making initial installation and integration challenging for new users.
Advertised as research code with frequent changes and no fitness guarantee, making it unreliable for production deployments without significant customization.