# Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV

arXiv: https://arxiv.org/abs/1909.06700 · HTML: https://mlsanity.com/paper/1909.06700/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2019.

## Built on by (most cited first, 27 of 27)

- [RGBD GS-ICP SLAM](https://mlsanity.com/paper/2403.12550/) (2024)
- [R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package](https://mlsanity.com/paper/2109.07982/) (2021)
- [SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments](https://mlsanity.com/paper/2303.09095/) (2023)
- [FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry](https://mlsanity.com/paper/2408.14035/) (2024)
- [HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR](https://mlsanity.com/paper/2203.09215/) (2022)
- [MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square](https://mlsanity.com/paper/2102.03771/) (2021)
- [STD: Stable Triangle Descriptor for 3D place recognition](https://mlsanity.com/paper/2209.12435/) (2022)
- [(LC)2: LiDAR-Camera Loop Constraints For Cross-Modal Place Recognition](https://mlsanity.com/paper/2304.08660/) (2023)
- [High-Definition Map Generation Technologies For Autonomous Driving](https://mlsanity.com/paper/2206.05400/) (2022)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [General Place Recognition Survey: Towards the Real-world Autonomy Age](https://mlsanity.com/paper/2209.04497/) (2022)
- [ImMesh: An Immediate LiDAR Localization and Meshing Framework](https://mlsanity.com/paper/2301.05206/) (2023)
- [HiSC4D: Human-centered interaction and 4D Scene Capture in Large-scale Space Using Wearable IMUs and LiDAR](https://mlsanity.com/paper/2409.04398/) (2024)
- [End-To-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization](https://mlsanity.com/paper/2201.03860/) (2022)
- [Intensity Image-based LiDAR Fiducial Marker System](https://mlsanity.com/paper/2203.01816/) (2022)
- [R3LIVE++: A Robust, Real-time, Radiance reconstruction package with a tightly-coupled LiDAR-Inertial-Visual state Estimator](https://mlsanity.com/paper/2209.03666/) (2022)
- [ECTLO: Effective Continuous-time Odometry Using Range Image for LiDAR with Small FoV](https://mlsanity.com/paper/2206.08517/) (2022)
- [Real-Time Simultaneous Localization and Mapping with LiDAR intensity](https://mlsanity.com/paper/2301.09257/) (2023)
- [L-PR: Exploiting LiDAR Fiducial Marker for Unordered Low Overlap Multiview Point Cloud Registration](https://mlsanity.com/paper/2406.03298/) (2024)
- [DIDLM: A SLAM Dataset for Difficult Scenarios Featuring Infrared, Depth Cameras, LIDAR, 4D Radar, and Others under Adverse Weather, Low Light Conditions, and Rough Roads](https://mlsanity.com/paper/2404.09622/) (2024)
- [Perception Helps Planning: Facilitating Multi-Stage Lane-Level Integration via Double-Edge Structures](https://mlsanity.com/paper/2407.11644/) (2024)
- [ROLO-SLAM: Rotation-Optimized LiDAR-Only SLAM in Uneven Terrain with Ground Vehicle](https://mlsanity.com/paper/2501.02166/) (2025)
- [Resonant Scanning Design and Control for Fast Spatial Sampling](https://mlsanity.com/paper/2103.12996/) (2021)
- [Fiducial Tag Localization on a 3D LiDAR Prior Map](https://mlsanity.com/paper/2209.01072/) (2022)
- [LISNeRF Mapping: LiDAR-based Implicit Mapping via Semantic Neural Fields for Large-Scale 3D Scenes](https://mlsanity.com/paper/2311.02313/) (2023)
- [Robust Second-order LiDAR Bundle Adjustment Algorithm Using Mean Squared Group Metric](https://mlsanity.com/paper/2409.01856/) (2024)
- [CAR-LOAM: Color-Assisted Robust LiDAR Odometry and Mapping](https://mlsanity.com/paper/2502.17249/) (2025)

## Abstract (arXiv)

LiDAR odometry and mapping (LOAM) has been playing an important role in autonomous vehicles, due to its ability to simultaneously localize the robot's pose and build high-precision, high-resolution maps of the surrounding environment. This enables autonomous navigation and safe path planning of autonomous vehicles. In this paper, we present a robust, real-time LOAM algorithm for LiDARs with small FoV and irregular samplings. By taking effort on both front-end and back-end, we address several fundamental challenges arising from such LiDARs, and achieve better performance in both precision and efficiency compared to existing baselines. To share our findings and to make contributions to the community, we open source our codes on Github