# Lidar with Velocity: Correcting Moving Objects Point Cloud Distortion from Oscillating Scanning Lidars by Fusion with Camera

arXiv: https://arxiv.org/abs/2111.09497 · HTML: https://mlsanity.com/paper/2111.09497/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 18 Nov 2021.

## Builds on (most cited first, 11 of 11)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics](https://mlsanity.com/paper/2008.08063/) (2020)
- [Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments](https://mlsanity.com/paper/2103.01627/) (2021)
- [VDO-SLAM: A Visual Dynamic Object-aware SLAM System](https://mlsanity.com/paper/2005.11052/) (2020)
- [CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System](https://mlsanity.com/paper/2011.11357/) (2020)
- [Low-cost Retina-like Robotic Lidars Based on Incommensurable Scanning](https://mlsanity.com/paper/2006.11034/) (2020)
- [Camera-based vehicle velocity estimation from monocular video](https://mlsanity.com/paper/1802.07094/) (2018)
- [Extended Object Tracking with Random Hypersurface Models](https://mlsanity.com/paper/1304.5084/) (2013)

## Built on by (most cited first, 3 of 3)

- [STD: Stable Triangle Descriptor for 3D place recognition](https://mlsanity.com/paper/2209.12435/) (2022)
- [FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentation](https://mlsanity.com/paper/2502.09274/) (2025)
- [Skeleton-based Robust Registration Framework for Corrupted 3D Point Clouds](https://mlsanity.com/paper/2509.24273/) (2025)

## Abstract (arXiv)

Lidar point cloud distortion from moving object is an important problem in autonomous driving, and recently becomes even more demanding with the emerging of newer lidars, which feature back-and-forth scanning patterns. Accurately estimating moving object velocity would not only provide a tracking capability but also correct the point cloud distortion with more accurate description of the moving object. Since lidar measures the time-of-flight distance but with a sparse angular resolution, the measurement is precise in the radial measurement but lacks angularly. Camera on the other hand provides a dense angular resolution. In this paper, Gaussian-based lidar and camera fusion is proposed to estimate the full velocity and correct the lidar distortion. A probabilistic Kalman-filter framework is provided to track the moving objects, estimate their velocities and simultaneously correct the point clouds distortions. The framework is evaluated on real road data and the fusion method outperforms the traditional ICP-based and point-cloud only method. The complete working framework is open-sourced (https://github.com/ISEE-Technology/lidar-with-velocity) to accelerate the adoption of the emerging lidars.