R-VoxelMap: Accurate Voxel Mapping With Recursive Plane Fitting for Online LiDAR Odometry

2026-01-18

IEEE Robotics and Automation Letters

AI Summary

This work addresses the susceptibility of conventional voxel-based maps to outliers in plane fitting, which often leads to biased parameter estimation, over-segmentation, or erroneous merging, thereby degrading LiDAR odometry accuracy. To mitigate these issues, the authors propose a geometry-driven recursive plane fitting method that leverages RANSAC to isolate outliers and recursively propagates them to deeper octree levels. An additional validity check based on point distribution is integrated to prevent spurious fits and incorrect plane merges. The resulting representation achieves a more accurate and robust geometric description of the environment. Evaluated on multiple public LiDAR (and LiDAR-inertial) SLAM datasets, the approach significantly improves localization accuracy while maintaining computational efficiency and memory consumption comparable to existing methods.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Local Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This letter proposes R-VoxelMap, a novel voxel mapping method that constructs accurate voxel maps using a geometry-driven recursive plane fitting strategy to enhance the localization accuracy of online LiDAR odometry. VoxelMap and its variants typically fit and check planes using all points in a voxel, which may lead to plane parameter deviation caused by outliers, over segmentation of large planes, and incorrect merging across different physical planes. To address these issues, R-VoxelMap utilizes a geometry-driven recursive construction strategy based on an outlier detect-and-reuse pipeline. Specifically, for each voxel, accurate planes are first fitted while separating outliers using random sample consensus (RANSAC). The remaining outliers are then propagated to deeper octree levels for recursive processing, ensuring a detailed representation of the environment. In addition, a point distribution-based validity check algorithm is devised to prevent erroneous plane merging. Extensive experiments on diverse open-source LiDAR(-inertial) simultaneous localization and mapping (SLAM) datasets validate that our method achieves higher accuracy than other state-of-the-art approaches, with comparable efficiency and memory usage.

Problem

• Voxel mapping
• LiDAR odometry
• Plane fitting
• Outliers
• Plane merging

Innovation

• Recursive plane fitting
• RANSAC-based outlier handling
• Octree-based voxel mapping
• Geometry-driven SLAM
• LiDAR odometry