# Direct LiDAR Odometry: Fast Localization with Dense Point Clouds

arXiv: https://arxiv.org/abs/2110.00605 · HTML: https://mlsanity.com/paper/2110.00605/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2021.

## Built on by (most cited first, 30 of 30)

- [Quadric Representations for LiDAR Odometry, Mapping and Localization](https://mlsanity.com/paper/2304.14190/) (2023)
- [Autonomous Driving in Unstructured Environments: How Far Have We Come?](https://mlsanity.com/paper/2410.07701/) (2024)
- [FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration](https://mlsanity.com/paper/2301.09213/) (2023)
- [WildLMa: Long Horizon Loco-Manipulation in the Wild](https://mlsanity.com/paper/2411.15131/) (2024)
- [FRAME: A Modular Framework for Autonomous Map Merging: Advancements in the Field](https://mlsanity.com/paper/2404.18006/) (2024)
- [MAD-ICP: It Is All About Matching Data -- Robust and Informed LiDAR Odometry](https://mlsanity.com/paper/2405.05828/) (2024)
- [Efficient Camera Exposure Control for Visual Odometry via Deep Reinforcement Learning](https://mlsanity.com/paper/2408.17005/) (2024)
- [Event Camera and LiDAR based Human Tracking for Adverse Lighting Conditions in Subterranean Environments](https://mlsanity.com/paper/2304.08908/) (2023)
- [Real-Time Simultaneous Localization and Mapping with LiDAR intensity](https://mlsanity.com/paper/2301.09257/) (2023)
- [DANet: Density Adaptive Convolutional Network with Interactive Attention for 3D Point Clouds](https://mlsanity.com/paper/2303.04473/) (2023)
- [Traj-LIO: A Resilient Multi-LiDAR Multi-IMU State Estimator Through Sparse Gaussian Process](https://mlsanity.com/paper/2402.09189/) (2024)
- [A Minimal Subset Approach for Informed Keyframe Sampling in Large-Scale SLAM](https://mlsanity.com/paper/2501.01791/) (2025)
- [Polar Perspectives: Evaluating 2-D LiDAR Projections for Robust Place Recognition with Visual Foundation Models](https://mlsanity.com/paper/2512.02897/) (2025)
- [Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration](https://mlsanity.com/paper/2304.14520/) (2023)
- [CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction](https://mlsanity.com/paper/2404.14042/) (2024)
- [ROLO-SLAM: Rotation-Optimized LiDAR-Only SLAM in Uneven Terrain with Ground Vehicle](https://mlsanity.com/paper/2501.02166/) (2025)
- [AgriGS-SLAM: Orchard Mapping Across Seasons via Multi-View Gaussian Splatting SLAM](https://mlsanity.com/paper/2510.26358/) (2025)
- [Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems](https://mlsanity.com/paper/2308.07264/) (2023)
- [BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detection and Localization](https://mlsanity.com/paper/2408.14941/) (2024)
- [Why Sample Space Matters: Keyframe Sampling Optimization for LiDAR-based Place Recognition](https://mlsanity.com/paper/2410.02643/) (2024)
- [GAN-SLAM: Real-Time GAN Aided Floor Plan Creation Through SLAM](https://mlsanity.com/paper/2504.19653/) (2025)
- [Transformation & Translation Occupancy Grid Mapping: 2-Dimensional Deep Learning Refined SLAM](https://mlsanity.com/paper/2504.19654/) (2025)
- [PRISM-Loc: a Lightweight Long-range LiDAR Localization in Urban Environments with Topological Maps](https://mlsanity.com/paper/2506.15849/) (2025)
- [An Adaptive ICP LiDAR Odometry Based on Reliable Initial Pose](https://mlsanity.com/paper/2509.22058/) (2025)
- [Automatic Map Density Selection for Locally-Performant Visual Place Recognition](https://mlsanity.com/paper/2602.21473/) (2026)
- [Fast and Accurate Outlier-Aware LiDAR Super-Resolution for SLAM Applications](https://mlsanity.com/paper/2606.28607/) (2026)
- [Real-Time LiDAR Gaussian Splatting SLAM](https://mlsanity.com/paper/2607.04127/) (2026)
- [Point Ladder Tuning: Parameter-Efficient Hierarchical Adaptation for 3D Point Cloud Understanding](https://mlsanity.com/paper/2607.19171/) (2026)
- [LF-GICP: Parameter-Free Degeneracy-Aware LiDAR Odometry via a Voxel-Normal Localizability Field](https://mlsanity.com/paper/2608.19522/) (2026)
- [ArborSplat: Online Semantic Gaussian Splatting SLAM for Orchards](https://mlsanity.com/paper/2609.26315/) (2026)

## Abstract (arXiv)

Field robotics in perceptually-challenging environments require fast and accurate state estimation, but modern LiDAR sensors quickly overwhelm current odometry algorithms. To this end, this paper presents a lightweight frontend LiDAR odometry solution with consistent and accurate localization for computationally-limited robotic platforms. Our Direct LiDAR Odometry (DLO) method includes several key algorithmic innovations which prioritize computational efficiency and enables the use of dense, minimally-preprocessed point clouds to provide accurate pose estimates in real-time. This is achieved through a novel keyframing system which efficiently manages historical map information, in addition to a custom iterative closest point solver for fast point cloud registration with data structure recycling. Our method is more accurate with lower computational overhead than the current state-of-the-art and has been extensively evaluated in multiple perceptually-challenging environments on aerial and legged robots as part of NASA JPL Team CoSTAR's research and development efforts for the DARPA Subterranean Challenge.