Accurate and robust LiDAR odometry is a crucial technology for robot localization. However, motion distortion and ranging error make it a bottleneck. Most existing methods are limited in accuracy and robustness because they simply compensate for motion distortion by constant velocity motion assumption without accurate model of ranging error. In this paper, we propose a high-precision and robust LiDAR odometry (LO), which utilizes continuous-time estimation to remove LiDAR distortion and builds the spot uncertainty model to quantify the ranging error. Generally, the number of variables in continuous-time estimation is several times higher than that in discrete-time ones, leading to insufficient constraints on the LiDAR odometry. To solve this problem, we propose a marginalization method to retain prior scans’ constraints by exploiting the local support property of the B-spline. To further improve the odometry accuracy, we propose a residual adaptive weighting method and a probabilistic point cloud map based on the spot uncertainty model of LiDAR points. The experimental results show that our method outperforms state-of-the-art LiDAR odometry in accuracy and robustness.

Simultaneous Localization and Mapping (SLAM) is widely used in applications such as robotics and autonomous driving, with methods involving multi-sensor fusion demonstrating excellent performance. However, they simply reject dynamic features and ignore the mutual benefits of self and dynamic objects, which greatly limits their application in actual high-dynamic scenes. To address this issue, we propose ESO-SLAM, a tightly-coupled system for simultaneous self and multi-object pose estimation achieved through sensor fusion. This system employs a multi-probability fusion tracker based on filter to establish more robust object-level data association. Building upon this, we introduce a method that combines 3D Kalman filter velocity priors and camera optical flow decoupling for dynamic point cloud removal, aiming at improving the accuracy of self-pose estimation in odometry. Finally, we jointly refine the poses of the robot and objects using multiple constraint factors within our proposed framework. Experimental results on the KITTI raw dataset demonstrate that our approach achieves better pose accuracy for both self and tracked objects compared to baseline and state-of-the-art techniques. Furthermore, the proposed method exhibits feasibility in real-time performance to ensure its practical application value.