# Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry

arXiv: https://arxiv.org/abs/2109.07082 · HTML: https://mlsanity.com/paper/2109.07082/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2021.

## Built on by (most cited first, 17 of 17)

- [FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry](https://mlsanity.com/paper/2408.14035/) (2024)
- [PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency](https://mlsanity.com/paper/2401.09101/) (2024)
- [What Is The Best 3D Scene Representation for Robotics? From Geometric to Foundation Models](https://mlsanity.com/paper/2512.03422/) (2025)
- [STD: Stable Triangle Descriptor for 3D place recognition](https://mlsanity.com/paper/2209.12435/) (2022)
- [Drone-assisted Road Gaussian Splatting with Cross-view Uncertainty](https://mlsanity.com/paper/2408.15242/) (2024)
- [ImMesh: An Immediate LiDAR Localization and Meshing Framework](https://mlsanity.com/paper/2301.05206/) (2023)
- [Autonomous Driving in Unstructured Environments: How Far Have We Come?](https://mlsanity.com/paper/2410.07701/) (2024)
- [OmniColor: A Global Camera Pose Optimization Approach of LiDAR-360Camera Fusion for Colorizing Point Clouds](https://mlsanity.com/paper/2404.04693/) (2024)
- [UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception](https://mlsanity.com/paper/2604.14089/) (2026)
- [JiSAM: Alleviate Labeling Burden and Corner Case Problems in Autonomous Driving via Minimal Real-World Data](https://mlsanity.com/paper/2503.08422/) (2025)
- [LiVisSfM: Accurate and Robust Structure-from-Motion with LiDAR and Visual Cues](https://mlsanity.com/paper/2410.22213/) (2024)
- [An Efficient Plane Extraction Approach for Bundle Adjustment on LiDAR Point clouds](https://mlsanity.com/paper/2305.00287/) (2023)
- [CAR-LOAM: Color-Assisted Robust LiDAR Odometry and Mapping](https://mlsanity.com/paper/2502.17249/) (2025)
- [Surfel-based 3D Registration with Equivariant SE(3) Features](https://mlsanity.com/paper/2508.20789/) (2025)
- [DCReg: Decoupled Characterization for Efficient Degenerate LiDAR Registration](https://mlsanity.com/paper/2509.06285/) (2025)
- [GroundLoc: Efficient Large-Scale Outdoor LiDAR-Only Localization](https://mlsanity.com/paper/2510.24623/) (2025)
- [ToF-Wheel Odometry: Navigating Degeneration of Localization With Limited Sensing Capabilities](https://mlsanity.com/paper/2610.14590/) (2026)

## Abstract (arXiv)

This paper proposes an efficient and probabilistic adaptive voxel mapping method for LiDAR odometry. The map is a collection of voxels; each contains one plane (or edge) feature that enables the probabilistic representation of the environment and accurate registration of a new LiDAR scan. We further analyze the need for coarse-to-fine voxel mapping and then use a novel voxel map organized by a Hash table and octrees to build and update the map efficiently. We apply the proposed voxel map to an iterated extended Kalman filter and construct a maximum a posteriori probability problem for pose estimation. Experiments on the open KITTI dataset show the high accuracy and efficiency of our method compared to other state-of-the-art methods. Outdoor experiments on unstructured environments with non-repetitive scanning LiDARs further verify the adaptability of our mapping method to different environments and LiDAR scanning patterns. Our codes and dataset are open-sourced on Github