ROLO-SLAM: Rotation-Optimized LiDAR-Only SLAM in Uneven Terrain with Ground Vehicle

TL;DR

ROLO-SLAM tackles vertical pose drift in LiDAR-only SLAM for uneven terrain by decoupling rotation and translation in the front-end through forward location prediction and voxel-based rotation registration, complemented by continuous-time translation optimization. The back-end integrates scan-to-submap alignment and a global factor graph with loop closures to refine the global map. Across KITTI, Off-road, and SDU Campus datasets, ROLO-SLAM delivers superior 6-DOF pose accuracy, robust performance in rough terrain, and real-time operation (~100 ms per scan), outperforming several state-of-the-art LiDAR SLAM methods and approaching IMU-fused baselines in single-LiDAR scenarios. The work demonstrates the practical impact of rotation-optimized, LiDAR-only SLAM for reliable localization and mapping in challenging outdoor environments, with clear avenues for future improvements in decoupling strategies and terrain priors.

Abstract

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.

Figures (24)

• Figure 1: Top figures show a real vehicle moving in an off-road scenario. The bottom figure shows the point cloud map and the trajectory output by ROLO-SLAM.
• Figure 2: A simple case of suffering correspondence problems on uneven terrain.
• Figure 3: A system pipeline of our ROLO-SLAM, incorporating front-end LiDAR odometry module and the back-end mapping module.
• Figure 4: Snapshots of vehicle driving in uneven terrain. Here, the poses of any two vehicles are recorded at the same time intervals.
• Figure 5: Rotation alignment model. The green points are source points in $\mathcal{P}_s$ while the blue ellipse spheres are the Gaussian distribution in $\mathbf{m}_k$. The purple arrows represent the possible rotated direction.