Distance-Aware Attentive Framework for Multi-Agent Collaborative Perception in Presence of Pose Error

Multi-agent collaborative perception exchanges information to promote holistic perception, especially for remote and invisible areas that are limited by detection range and occlusion. Due to imperfect localization in practice, it usually suffers from pose estimation error, which can cause spatial message misalignment and performance degradation. Unlike most existing methods using additional module or procedure to correct pose error, we propose a novel framework, DistAtt, to suppress pose error and mine useful information simultaneously. It mainly consists of distance-aware feature sampling and cross-agent feature aggregation. The former utilizes diverse pooling kernels to downsample the intermediate features to different multiple granularities, and the latter utilizes specially designed attention mechanism to learn the most critical information. Furthermore, it adopts compensation strategy for more stable optimization. Experimental results show that DistAtt significantly suppresses the effect of localization noise and achieves outperformed performance when pose error exists.