# LIMO: Lidar-Monocular Visual Odometry

arXiv: https://arxiv.org/abs/1807.07524 · HTML: https://mlsanity.com/paper/1807.07524/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2018.

## Built on by (most cited first, 21 of 21)

- [Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis](https://mlsanity.com/paper/2312.08782/) (2023)
- [FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry](https://mlsanity.com/paper/2408.14035/) (2024)
- [Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision](https://mlsanity.com/paper/2504.02477/) (2025)
- [MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square](https://mlsanity.com/paper/2102.03771/) (2021)
- [DVLO: Deep Visual-LiDAR Odometry with Local-to-Global Feature Fusion and Bi-Directional Structure Alignment](https://mlsanity.com/paper/2403.18274/) (2024)
- [Fast and Robust Registration of Partially Overlapping Point Clouds](https://mlsanity.com/paper/2112.09922/) (2021)
- [Situational Graphs for Robot Navigation in Structured Indoor Environments](https://mlsanity.com/paper/2202.12197/) (2022)
- [From SLAM to Situational Awareness: Challenges and Survey](https://mlsanity.com/paper/2110.00273/) (2021)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [4DRVO-Net: Deep 4D Radar-Visual Odometry Using Multi-Modal and Multi-Scale Adaptive Fusion](https://mlsanity.com/paper/2308.06573/) (2023)
- [Self-supervised Visual-LiDAR Odometry with Flip Consistency](https://mlsanity.com/paper/2101.01322/) (2021)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning](https://mlsanity.com/paper/2306.01980/) (2023)
- [Colmap-PCD: An Open-source Tool for Fine Image-to-point cloud Registration](https://mlsanity.com/paper/2310.05504/) (2023)
- [DVLO4D: Deep Visual-Lidar Odometry with Sparse Spatial-temporal Fusion](https://mlsanity.com/paper/2509.06023/) (2025)
- [Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Features for Mobile Agents](https://mlsanity.com/paper/2307.07763/) (2023)
- [EdgeCalib: Multi-Frame Weighted Edge Features for Automatic Targetless LiDAR-Camera Calibration](https://mlsanity.com/paper/2310.16629/) (2023)
- [Lidar-Monocular Surface Reconstruction Using Line Segments](https://mlsanity.com/paper/2104.02761/) (2021)
- [CoFi: Coarse-to-Fine ICP for LiDAR Localization in an Efficient Long-lasting Point Cloud Map](https://mlsanity.com/paper/2110.10194/) (2021)
- [JVLDLoc: a Joint Optimization of Visual-LiDAR Constraints and Direction Priors for Localization in Driving Scenario](https://mlsanity.com/paper/2208.09777/) (2022)
- [Visual-LiDAR Odometry and Mapping with Monocular Scale Correction and Visual Bootstrapping](https://mlsanity.com/paper/2304.08978/) (2023)
- [DSP-SLAM++: A Unified Framework for Multi-Class, High-Fidelity Object SLAM in the Wild](https://mlsanity.com/paper/2606.25953/) (2026)

## Abstract (arXiv)

Higher level functionality in autonomous driving depends strongly on a precise motion estimate of the vehicle. Powerful algorithms have been developed. However, their great majority focuses on either binocular imagery or pure LIDAR measurements. The promising combination of camera and LIDAR for visual localization has mostly been unattended. In this work we fill this gap, by proposing a depth extraction algorithm from LIDAR measurements for camera feature tracks and estimating motion by robustified keyframe based Bundle Adjustment. Semantic labeling is used for outlier rejection and weighting of vegetation landmarks. The capability of this sensor combination is demonstrated on the competitive KITTI dataset, achieving a placement among the top 15. The code is released to the community.