Bandwidth-Adaptive Spatiotemporal Correspondence Identification for Collaborative Perception

TL;DR

This paper introduces a bandwidth-adaptive method for spatiotemporal correspondence identification in multi-robot collaborative perception, improving data sharing efficiency and robustness under dynamic communication constraints.

Contribution

It presents a novel approach that allows robots to selectively share observations based on bandwidth, enhancing collaborative perception performance.

Findings

Achieves 8%-56% improvement in covisible object retrieval.

Outperforms previous techniques in data sharing efficiency.

Validates effectiveness across autonomous driving scenarios.

Abstract

Correspondence identification (CoID) is an essential capability in multi-robot collaborative perception, which enables a group of robots to consistently refer to the same objects within their respective fields of view. In real-world applications, such as connected autonomous driving, vehicles face challenges in directly sharing raw observations due to limited communication bandwidth. In order to address this challenge, we propose a novel approach for bandwidth-adaptive spatiotemporal CoID in collaborative perception. This approach allows robots to progressively select partial spatiotemporal observations and share with others, while adapting to communication constraints that dynamically change over time. We evaluate our approach across various scenarios in connected autonomous driving simulations. Experimental results validate that our approach enables CoID and adapts to dynamic communication bandwidth changes. In addition, our approach achieves 8%-56% overall improvements in terms of covisible object retrieval for CoID and data sharing efficiency, which outperforms previous techniques and achieves the state-of-the-art performance. More information is available at: https://gaopeng5.github.io/acoid.