FeaKM: Robust Collaborative Perception under Noisy Pose Conditions

TL;DR

FeaKM introduces a feature-level keypoints matching method that corrects pose discrepancies in collaborative perception, significantly improving robustness under noisy conditions.

Contribution

The paper presents FeaKM, a novel approach that employs feature-level keypoints matching to enhance collaborative perception accuracy despite localization noise.

Findings

Outperforms existing methods on DAIR-V2X dataset

Maintains robustness under severe noise conditions

Effective correction of pose discrepancies

Abstract

Collaborative perception is essential for networks of agents with limited sensing capabilities, enabling them to work together by exchanging information to achieve a robust and comprehensive understanding of their environment. However, localization inaccuracies often lead to significant spatial message displacement, which undermines the effectiveness of these collaborative efforts. To tackle this challenge, we introduce FeaKM, a novel method that employs Feature-level Keypoints Matching to effectively correct pose discrepancies among collaborating agents. Our approach begins by utilizing a confidence map to identify and extract salient points from intermediate feature representations, allowing for the computation of their descriptors. This step ensures that the system can focus on the most relevant information, enhancing the matching process. We then implement a target-matching strategy that generates an assignment matrix, correlating the keypoints identified by different agents. This is critical for establishing accurate correspondences, which are essential for effective collaboration. Finally, we employ a fine-grained transformation matrix to synchronize the features of all agents and ascertain their relative statuses, ensuring coherent communication among them. Our experimental results demonstrate that FeaKM significantly outperforms existing methods on the DAIR-V2X dataset, confirming its robustness even under severe noise conditions. The code and implementation details are available at https://github.com/uestchjw/FeaKM.