Posture and Map Restoration in SLAM Using Trajectory Information

Abstract

SLAM algorithms generally use the last system posture to estimate its current posture. Errors in the previous estimations can build up and cause significant drift accumulation. This accumulation of error leads to the bias of choosing accuracy over robustness. On the contrary, sensors like GPS do not accumulate errors. But the noise distribution in the readings makes it difficult to apply in high-frequency SLAM systems. This paper presents an approach which uses the advantage of both tightly-coupled SLAM systems and highly robust absolute positioning systems to improve the robustness and accuracy of a SLAM process. The proposed method uses a spare reference trajectory frame to measure the trajectory of the targeted robotic system and use it to recover the system posture during the mapping process. This helps the robotic system to reduce its accumulated error and able the system to recover from major mapping failures. While the correction process happens whenever a gap is detected between the two trajectories, the external frame does not have to be always available. The correction process is only triggered when the spare trajectory sensors can communicate. Thus, it reduces the needed computational power and complexity. To further evaluate the proposed method, the algorithm was assessed in two field tests and a public dataset. We have demonstrated that the proposed algorithm has the ability to be adapted into different SLAM approaches with various map representations. To share our findings, the software constructed for this project is open-sourced on Github.