Abstract
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.
Abstract (translated)
评估同时定位和映射(SLAM)算法需要高精度和高密度的地面真实(GT)轨迹。但是，在没有GT跟踪传感器的情况下获得理想的GT轨迹有时会非常困难。作为一种替代方案，在本文中，我们提出了一种 novel prior-assisted SLAM系统，以生成约10Hz的全六自由度($6$-DOF)轨迹，作为基准，在因子图框架下。我们的模态因子利用先前的点云地图和LiDAR框架进行点-平面优化，同时检测退化情况，减少漂移，增强姿态估计的一致性。我们的系统通过松散耦合的方法无缝与先进的相控测量系统集成，生成高速度和精度的轨迹。此外，我们提出了一个静态情况下受限于norm的重力因子，优化姿态和重力以提升性能。广泛的评估证明了我们的算法在多种场景下的优越性，在精度、流畅性和可靠性方面。我们的方法极大地推进了可靠和准确的SLAM评估方法，促进了机器人研究的进度。
URL
https://arxiv.org/abs/2305.13147
