Enhance Accuracy: Sensitivity and Uncertainty Theory in LiDAR Odometry and Mapping

Zeyu Wan

Abstract:

Currently, the improvement of LiDAR poses estimation accuracy is an urgent need for mobile robots. Research indicates
that diverse LiDAR points have different influences on the accuracy of pose estimation. This study aimed to select a good point set to
enhance accuracy. Accordingly, the sensitivity and uncertainty of LiDAR point residuals were formulated as a fundamental basis for
derivation and analysis. High-sensitivity and low -uncertainty point residual terms are preferred to achieve higher pose estimation
accuracy. The proposed selection method has been theoretically proven to be capable of achieving a global statistical optimum. It was
tested on artificial data and compared with the KITTI benchmark. It was also implemented in LiDAR odometry (LO) and LiDAR inertial
odometry (LIO), both indoors and outdoors. The experiments revealed that utilizing selected LiDAR point residuals simultaneously
enhances optimization accuracy, decreases residual terms, and guarantees real-time performance.