PALoc: Advancing SLAM Benchmarking With Prior-Assisted 6-DoF Trajectory Generation and Uncertainty Estimation

Accurately generating ground truth (GT) trajectories is essential for simultaneous localization and mapping (SLAM) evaluation, particularly under varying environmental conditions. This study presents PALoc, a systematic approach that leverages a prior map-assisted framework for the first-time generation of dense six-degree-of-freedom GT poses, significantly enhancing the fidelity of SLAM benchmarks across both indoor and outdoor environments. Our method excels in handling degenerate and stationary conditions frequently encountered in SLAM datasets, thereby increasing robustness and precision. A critical feature of PALoc is the detailed derivation of covariance within the factor graph, enabling an in-depth analysis of pose uncertainty propagation. This analysis plays a pivotal role in illustrating specific pose uncertainty and in elevating trajectory reliability from both theoretical and practical perspectives. In addition, we provide an open-source toolbox for the criteria of map evaluation, facilitating the indirect assessment of overall trajectory precision. Experimental results show at least a 30% improvement in map accuracy and a 20% increase in direct trajectory accuracy compared to the iterative closest point algorithm across diverse environments, with substantially enhanced robustness Our publicly available solution, PALoc, extensively applied in the FusionPortable dataset, is geared toward SLAM benchmark augmentation and represents a significant advancement in SLAM evaluation.