ROLO‐SLAM: Rotation‐Optimized LiDAR‐Only SLAM in Uneven Terrain With Ground Vehicle

2025-01-03

Journal of Field Robotics

AI Summary

To address severe vertical pose drift and global map distortion of LiDAR-SLAM in off-road environments with undulating terrain, this paper proposes a novel rotation-prioritized framework. Our method introduces four key innovations: (1) spherical-registration-guided intra-voxel rotation estimation; (2) a pose frontend that decouples forward position prediction from rotation estimation; (3) geometric motion-constrained translation optimization; and (4) a joint error-suppression mechanism integrating submap alignment and global scale factor graph optimization. Leveraging voxelized parallel processing, spherical-coordinate rotation alignment, geometrically embedded nonlinear optimization, and scan-to-submap matching, the framework significantly improves vertical accuracy (>40% gain), effectively suppresses accumulated drift, and mitigates map distortion. Extensive evaluations on diverse real-world off-road terrains demonstrate superior overall performance compared to state-of-the-art LiDAR-SLAM approaches.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• LiDAR-based SLAM
• Vertical Position Estimation
• Rough Terrain Mapping

Innovation

• ROLO-SLAM
• LiDAR Optimization
• Complex Terrain Mapping