# DMLO: Deep Matching LiDAR Odometry

arXiv: https://arxiv.org/abs/2004.03796 · HTML: https://mlsanity.com/paper/2004.03796/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2020.

## Built on by (most cited first, 20 of 20)

- [LiDAR R-CNN: An Efficient and Universal 3D Object Detector](https://mlsanity.com/paper/2103.15297/) (2021)
- [Efficient 3D Deep LiDAR Odometry](https://mlsanity.com/paper/2111.02135/) (2021)
- [A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments](https://mlsanity.com/paper/2203.06612/) (2022)
- [4DRVO-Net: Deep 4D Radar-Visual Odometry Using Multi-Modal and Multi-Scale Adaptive Fusion](https://mlsanity.com/paper/2308.06573/) (2023)
- [DELO: Deep Evidential LiDAR Odometry using Partial Optimal Transport](https://mlsanity.com/paper/2308.07153/) (2023)
- [What's in My LiDAR Odometry Toolbox?](https://mlsanity.com/paper/2103.09708/) (2021)
- [UnDeepLIO: Unsupervised Deep Lidar-Inertial Odometry](https://mlsanity.com/paper/2109.01533/) (2021)
- [DSLO: Deep Sequence LiDAR Odometry Based on Inconsistent Spatio-temporal Propagation](https://mlsanity.com/paper/2409.00744/) (2024)
- [RegFormer++: An Efficient Large-Scale 3D LiDAR Point Registration Network with Projection-Aware 2D Transformer](https://mlsanity.com/paper/2603.14290/) (2026)
- [Deep Bayesian ICP Covariance Estimation](https://mlsanity.com/paper/2202.11607/) (2022)
- [Mitigating Shadows in Lidar Scan Matching using Spherical Voxels](https://mlsanity.com/paper/2208.01150/) (2022)
- [UnPWC-SVDLO: Multi-SVD on PointPWC for Unsupervised Lidar Odometry](https://mlsanity.com/paper/2205.08150/) (2022)
- [ICET Online Accuracy Characterization for Geometry-Based Laser Scan Matching](https://mlsanity.com/paper/2306.08690/) (2023)
- [Attention-Guided Lidar Segmentation and Odometry Using Image-to-Point Cloud Saliency Transfer](https://mlsanity.com/paper/2308.14332/) (2023)
- [TSTTC: A Large-Scale Dataset for Time-to-Contact Estimation in Driving Scenarios](https://mlsanity.com/paper/2309.01539/) (2023)
- [DNN Filter for Bias Reduction in Distribution-to-Distribution Scan Matching](https://mlsanity.com/paper/2211.04047/) (2022)
- [Deep Planar Parallax for Monocular Depth Estimation](https://mlsanity.com/paper/2301.03178/) (2023)
- [LoRCoN-LO: Long-term Recurrent Convolutional Network-based LiDAR Odometry](https://mlsanity.com/paper/2303.11853/) (2023)
- [Efficient Active Training for Deep LiDAR Odometry](https://mlsanity.com/paper/2509.03211/) (2025)
- [An Adaptive ICP LiDAR Odometry Based on Reliable Initial Pose](https://mlsanity.com/paper/2509.22058/) (2025)

## Abstract (arXiv)

LiDAR odometry is a fundamental task for various areas such as robotics, autonomous driving. This problem is difficult since it requires the systems to be highly robust running in noisy real-world data. Existing methods are mostly local iterative methods. Feature-based global registration methods are not preferred since extracting accurate matching pairs in the nonuniform and sparse LiDAR data remains challenging. In this paper, we present Deep Matching LiDAR Odometry (DMLO), a novel learning-based framework which makes the feature matching method applicable to LiDAR odometry task. Unlike many recent learning-based methods, DMLO explicitly enforces geometry constraints in the framework. Specifically, DMLO decomposes the 6-DoF pose estimation into two parts, a learning-based matching network which provides accurate correspondences between two scans and rigid transformation estimation with a close-formed solution by Singular Value Decomposition (SVD). Comprehensive experimental results on real-world datasets KITTI and Argoverse demonstrate that our DMLO dramatically outperforms existing learning-based methods and comparable with the state-of-the-art geometry based approaches.