Enhance Accuracy: Sensitivity and Uncertainty Theory in LiDAR Odometry and Mapping
This work addresses the need for more accurate pose estimation in mobile robots, but it is incremental as it builds on existing LiDAR odometry methods with a novel point selection approach.
The study tackled the problem of improving LiDAR pose estimation accuracy for mobile robots by selecting high-sensitivity and low-uncertainty point residuals, resulting in enhanced optimization accuracy, reduced residual terms, and maintained real-time performance in indoor and outdoor tests.
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.