TagSLAM: Robust SLAM with Fiducial Markers
This work addresses the need for convenient and robust SLAM solutions in robotics and computer vision, though it is incremental as it builds on existing methods like GTSAM and AprilTags.
TagSLAM tackles the problem of performing Simultaneous Localization and Mapping (SLAM) by using AprilTag fiducial markers, providing a robust and flexible ROS-based open source package that enables rapid design of experiments like full SLAM, camera calibration, and loop closure.
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.