PALoc: Robust Prior-assisted Trajectory Generation for Benchmarking

• DOI: 10.48550/arxiv.2305.13147
• Publication Date: 2023-01-01
• Authors: Hu, Xiangcheng; Wu, Jin; Jiao, Jianhao; Geng, Ruoyu; Liu, Ming
• Keywords: FOS: Computer and information sciences; Computer Science - Robotics; Robotics (cs.RO)

4 pages, 6 figures<br/>Evaluating simultaneous localization and mapping (SLAM) algorithms necessitates high-precision and dense ground truth (GT) trajectories. But obtaining desirable GT trajectories is sometimes challenging without GT tracking sensors. As an alternative, in this paper, we propose a novel prior-assisted SLAM system to generate a full six-degree-of-freedom ($6$-DOF) trajectory at around $10$Hz for benchmarking under the framework of the factor graph. Our degeneracy-aware map factor utilizes a prior point cloud map and LiDAR frame for point-to-plane optimization, simultaneously detecting degeneration cases to reduce drift and enhancing the consistency of pose estimation. Our system is seamlessly integrated with cutting-edge odometry via a loosely coupled scheme to generate high-rate and precise trajectories. Moreover, we propose a norm-constrained gravity factor for stationary cases, optimizing pose and gravity to boost performance. Extensive evaluations demonstrate our algorithm's superiority over existing SLAM or map-based methods in diverse scenarios in terms of precision, smoothness, and robustness. Our approach substantially advances reliable and accurate SLAM evaluation methods, fostering progress in robotics research.<br/>