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.

翻译：评估同时定位与地图构建（SLAM）算法需要高精度且密集的真实轨迹数据。然而，在没有真实轨迹追踪传感器的情况下，获取理想的真实轨迹往往具有挑战性。为此，本文提出一种新颖的先验辅助SLAM系统，在因子图框架下以约10Hz频率生成完整的六自由度（$6$-DOF）轨迹用于基准测试。该系统采用退化感知的地图因子，利用先验点云地图与激光雷达帧进行点面优化，同时检测退化情况以减少漂移并增强位姿估计的一致性。通过松耦合方案与先进里程计算法无缝集成，生成高频高精度轨迹。此外，针对静态场景提出范数约束重力因子，通过优化位姿与重力提升性能。广泛评估表明，本算法在精度、平滑性和鲁棒性方面均优于现有SLAM或基于地图的方法。该方法显著推进了可靠且准确的SLAM评估技术，促进了机器人研究的发展。