A LiDAR fiducial marker framework detects printed visual markers in intensity images and uses them to register low-overlap point clouds, localize, and merge maps.
TagSLAM: Robust SLAM with Fiducial Markers
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abstract
TagSLAM provides a convenient, flexible, and robust way of performing Simultaneous Localization and Mapping (SLAM) with AprilTag fiducial markers. By leveraging a few simple abstractions (bodies, tags, cameras), TagSLAM provides a front end to the GTSAM factor graph optimizer that makes it possible to rapidly design a range of experiments that are based on tags: full SLAM, extrinsic camera calibration with non-overlapping views, visual localization for ground truth, loop closure for odometry, pose estimation etc. We discuss in detail how TagSLAM initializes the factor graph in a robust way, and present loop closure as an application example. TagSLAM is a ROS based open source package and can be found at https://berndpfrommer.github.io/tagslam_web.
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Mapping and Localization Using LiDAR Fiducial Markers
A LiDAR fiducial marker framework detects printed visual markers in intensity images and uses them to register low-overlap point clouds, localize, and merge maps.