机器人2026，Vol.48Issue(3)：556-567,12.DOI:10.13973/j.cnki.robot.240232
基于自适应体素地图的紧耦合激光惯性里程计
Tightly-coupled LiDAR-inertial Odometry Based on Adaptive Voxel Map
焦宇都 1江涛 1苏晓杰 1宋韬 2叶建川2
作者信息
- 1. 重庆大学自动化学院,重庆 400044||重庆大学机器人与无人系统研究所,重庆 400044
- 2. 北京理工大学宇航学院,北京 100081
- 折叠
摘要
Abstract
To enhance the precise navigation capability of robot in complex environments with uneven terrain such as stairs and hills,and overcome issues in existing LiDAR-inertial odometry systems including significant cumulative error drift along Z-axis and low-quality map,a tightly coupled LiDAR-inertial odometry based on an adaptive voxel feature map is proposed.Firstly,the environment is adaptively partitioned into grids using an octree and Hash indexing,enabling efficient management and indexing of point cloud feature data through the construction of a voxel feature map.Then,a direct voxel-based feature indexing method is employed for feature extraction from the current frame based on the voxel feature map,avoiding redundant feature fitting,which improves the robustness and efficiency of feature extraction and fitting in the pose estimation module.Finally,the concept of co-visible voxel grids is introduced.When the same voxel grid is observed jointly by multiple frames in a sliding window,a sliding window-based bundle adjustment(BA)optimization problem is formulated,to adjust all poses within the window and update the states maintained by the extended Kalman filter based state estimation.Experiments on public datasets and in real-world scenarios demonstrate that the proposed method effectively mitigates cumulative errors along the Z-axis,achieving better global consistency for both trajectories and maps compared to traditional algorithms.关键词
同步定位与地图构建/激光—惯导里程计/体素特征地图/滑动窗口优化Key words
simultaneous localization and mapping/LiDAR-inertial odometry/feature voxel map/sliding window opti-mization引用本文复制引用
焦宇都,江涛,苏晓杰,宋韬,叶建川..基于自适应体素地图的紧耦合激光惯性里程计[J].机器人,2026,48(3):556-567,12.
