# V2I-Calib: A Novel Calibration Approach for Collaborative Vehicle and Infrastructure LiDAR Systems

arXiv: https://arxiv.org/abs/2407.10195 · HTML: https://mlsanity.com/paper/2407.10195/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 14 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [TEASER: Fast and Certifiable Point Cloud Registration](https://mlsanity.com/paper/2001.07715/) (2020)
- [PointNetLK: Robust & Efficient Point Cloud Registration using PointNet](https://mlsanity.com/paper/1903.05711/) (2019)
- [Robust Collaborative 3D Object Detection in Presence of Pose Errors](https://mlsanity.com/paper/2211.07214/) (2022)
- [V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting](https://mlsanity.com/paper/2305.05938/) (2023)
- [Latency-Aware Collaborative Perception](https://mlsanity.com/paper/2207.08560/) (2022)
- [A comprehensive survey on point cloud registration](https://mlsanity.com/paper/2103.02690/) (2021)
- [GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer](https://mlsanity.com/paper/2308.03768/) (2023)
- [Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception](https://mlsanity.com/paper/2307.13929/) (2023)
- [PointGMM: a Neural GMM Network for Point Clouds](https://mlsanity.com/paper/2003.13326/) (2020)
- [Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection](https://mlsanity.com/paper/2311.01682/) (2023)
- [A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments](https://mlsanity.com/paper/2203.06612/) (2022)
- [Cross-source Point Cloud Registration: Challenges, Progress and Prospects](https://mlsanity.com/paper/2305.13570/) (2023)
- [A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces](https://mlsanity.com/paper/1904.12116/) (2019)
- [Verifiable Goal Recognition for Autonomous Driving with Occlusions](https://mlsanity.com/paper/2206.14163/) (2022)
- [Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap](https://mlsanity.com/paper/2307.12116/) (2023)

## Abstract (arXiv)

Cooperative LiDAR systems integrating vehicles and road infrastructure, termed V2I calibration, exhibit substantial potential, yet their deployment encounters numerous challenges. A pivotal aspect of ensuring data accuracy and consistency across such systems involves the calibration of LiDAR units across heterogeneous vehicular and infrastructural endpoints. This necessitates the development of calibration methods that are both real-time and robust, particularly those that can ensure robust performance in urban canyon scenarios without relying on initial positioning values. Accordingly, this paper introduces a novel approach to V2I calibration, leveraging spatial association information among perceived objects. Central to this method is the innovative Overall Intersection over Union (oIoU) metric, which quantifies the correlation between targets identified by vehicle and infrastructure systems, thereby facilitating the real-time monitoring of calibration results. Our approach involves identifying common targets within the perception results of vehicle and infrastructure LiDAR systems through the construction of an affinity matrix. These common targets then form the basis for the calculation and optimization of extrinsic parameters. Comparative and ablation studies conducted using the DAIR-V2X dataset substantiate the superiority of our approach. For further insights and resources, our project repository is accessible at https://github.com/MassimoQu/v2i-calib.