Kavli Affiliate: Xiang Zhang
| First 5 Authors: Yinchuan Wang, Bin Ren, Xiang Zhang, Pengyu Wang, Chaoqun Wang
| Summary:
LiDAR-based SLAM is recognized as one effective method to offer localization
guidance in rough environments. However, off-the-shelf LiDAR-based SLAM methods
suffer from significant pose estimation drifts, particularly components
relevant to the vertical direction, when passing to uneven terrains. This
deficiency typically leads to a conspicuously distorted global map. In this
article, a LiDAR-based SLAM method is presented to improve the accuracy of pose
estimations for ground vehicles in rough terrains, which is termed
Rotation-Optimized LiDAR-Only (ROLO) SLAM. The method exploits a forward
location prediction to coarsely eliminate the location difference of
consecutive scans, thereby enabling separate and accurate determination of the
location and orientation at the front-end. Furthermore, we adopt a
parallel-capable spatial voxelization for correspondence-matching. We develop a
spherical alignment-guided rotation registration within each voxel to estimate
the rotation of vehicle. By incorporating geometric alignment, we introduce the
motion constraint into the optimization formulation to enhance the rapid and
effective estimation of LiDAR’s translation. Subsequently, we extract several
keyframes to construct the submap and exploit an alignment from the current
scan to the submap for precise pose estimation. Meanwhile, a global-scale
factor graph is established to aid in the reduction of cumulative errors. In
various scenes, diverse experiments have been conducted to evaluate our method.
The results demonstrate that ROLO-SLAM excels in pose estimation of ground
vehicles and outperforms existing state-of-the-art LiDAR SLAM frameworks.
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