PFLIO-SAM: Tightly Coupled Polarization Camera/Optical Flow/LiDAR/IMU Odometry via Smoothing and Mapping

Le résumé fourni par la source

LiDAR inertial odometry (LIO) faces issues such as error accumulation and insufficient feature points in open and low-texture environments, which lead to cumulative heading errors and height drift. To tackle these problems, we introduce a polarization camera to provide absolute heading angle correction for LIO. Moreover, an optical flow ranging module is employed to provide speed and height constraints for LIO. Both the polarization camera and optical flow are integrated with LIO, constructing a tightly coupled polarization camera/optical flow/LiDAR/IMU odometry via smoothing and mapping (PFLIO-SAM). Factor graph optimization (FGO) is utilized to obtain the optimal solution of PFLIO-SAM. Specifically, a polarization heading factor is designed to correct the heading drift, and an optical flow velocity factor and a height factor are developed to correct the velocity error and height drift, as well as restore loop-closure detection. A sliding window is used to reduce computational load, and real-time simultaneous localization and mapping (SLAM) is realized. To verify the effectiveness of the proposed method, outdoor experiments were carried out on an open and low-texture playground. The experiments demonstrate that the proposed PFLIO-SAM achieves decimeter-level positioning accuracy and minimal heading error under severe ground jitter, effectively improving the localization and heading estimation accuracy of LIO.

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