# A Spatial Calibration Method for Robust Cooperative Perception

arXiv: https://arxiv.org/abs/2304.12033 · HTML: https://mlsanity.com/paper/2304.12033/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2023.

## Built on by (most cited first, 13 of 13)

- [BEVHeight++: Toward Robust Visual Centric 3D Object Detection](https://mlsanity.com/paper/2309.16179/) (2023)
- [V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception](https://mlsanity.com/paper/2411.10962/) (2024)
- [Vehicle-to-Everything Cooperative Perception for Autonomous Driving](https://mlsanity.com/paper/2310.03525/) (2023)
- [TraF-Align: Trajectory-aware Feature Alignment for Asynchronous Multi-agent Perception](https://mlsanity.com/paper/2503.19391/) (2025)
- [Collaborative Perception Datasets for Autonomous Driving: A Review](https://mlsanity.com/paper/2504.12696/) (2025)
- [InScope: A New Real-world 3D Infrastructure-side Collaborative Perception Dataset for Open Traffic Scenarios](https://mlsanity.com/paper/2407.21581/) (2024)
- [Cooperative Visual-LiDAR Extrinsic Calibration Technology for Intersection Vehicle-Infrastructure: A review](https://mlsanity.com/paper/2405.10132/) (2024)
- [RG-Attn: Radian Glue Attention for Multi-modality Multi-agent Cooperative Perception](https://mlsanity.com/paper/2501.16803/) (2025)
- [SGV3D:Towards Scenario Generalization for Vision-based Roadside 3D Object Detection](https://mlsanity.com/paper/2401.16110/) (2024)
- [Wireless Communication as an Information Sensor for Multi-agent Cooperative Perception: A Survey](https://mlsanity.com/paper/2505.00747/) (2025)
- [DRCP: Diffusion on Reinforced Cooperative Perception for Perceiving Beyond Limits](https://mlsanity.com/paper/2509.24903/) (2025)
- [An Analytical Framework to Enhance Autonomous Vehicle Perception for Smart Cities](https://mlsanity.com/paper/2510.13230/) (2025)
- [PEARL: A Lightweight Prompt-based Feature Interpreter Framework for Real-Time, Anonymous, and Heterogeneous Collaborative Perception](https://mlsanity.com/paper/2609.27123/) (2026)

## Abstract (arXiv)

Cooperative perception is a promising technique for intelligent and connected vehicles through vehicle-to-everything (V2X) cooperation, provided that accurate pose information and relative pose transforms are available. Nevertheless, obtaining precise positioning information often entails high costs associated with navigation systems. {Hence, it is required to calibrate relative pose information for multi-agent cooperative perception.} This paper proposes a simple but effective object association approach named context-based matching (CBM), which identifies inter-agent object correspondences using intra-agent geometrical context. In detail, this method constructs contexts using the relative position of the detected bounding boxes, followed by local context matching and global consensus maximization. The optimal relative pose transform is estimated based on the matched correspondences, followed by cooperative perception fusion. Extensive experiments are conducted on both the simulated and real-world datasets. Even with larger inter-agent localization errors, high object association precision and decimeter-level relative pose calibration accuracy are achieved among the cooperating agents.