Observation Contribution Theory for Pose Estimation Accuracy

Abstract

The improvement of pose estimation accuracy is currently the fundamental problem in mobile robots. This study aims to improve the use of observations to enhance accuracy. The selection of feature points affects the accuracy of pose estimation, leading to the question of how the contribution of observation influences the system. Accordingly, the contribution of information to the pose estimation process is analyzed. Moreover, the uncertainty model, sensitivity model, and contribution theory are formulated, providing a method for calculating the contribution of every residual term. The proposed selection method has been theoretically proven capable of achieving a global statistical optimum. The proposed method is tested on artificial data simulations and compared with the KITTI benchmark. The experiments revealed superior results in contrast to ALOAM and MLOAM. The proposed algorithm is implemented in LiDAR odometry and LiDAR Inertial odometry both indoors and outdoors using diverse LiDAR sensors with different scan modes, demonstrating its effectiveness in improving pose estimation accuracy. A new configuration of two laser scan sensors is subsequently inferred. The configuration is valid for three-dimensional pose localization in a prior map and yields results at the centimeter level.

Drift‐free localisation using prior cross‐source map for indoor low‐light environments

“…Therefore, according to Ref. [33], we selected points from the point cloud and kept the points with low uncertainty for odometry estimation and the following point cloud for cross-source map registration. We can use the Jacobian matrix of the distance to measure their uncertainty.…”

“…Therefore, we conduct a selection of points according to Ref. [33] and retain the points with the high contribution for updating the particles' weights. This improves both the computational efficiency and the matching accuracy.…”