UniKF, a Kalman-filter-based late fusion for BEV detections, achieves lower errors than IoU-based baselines on synthetic noise, but only marginal gains over the authors' own WLS method.
When2com: Multi-Agent Perception via Communication Graph Grouping
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abstract
While significant advances have been made for single-agent perception, many applications require multiple sensing agents and cross-agent communication due to benefits such as coverage and robustness. It is therefore critical to develop frameworks which support multi-agent collaborative perception in a distributed and bandwidth-efficient manner. In this paper, we address the collaborative perception problem, where one agent is required to perform a perception task and can communicate and share information with other agents on the same task. Specifically, we propose a communication framework by learning both to construct communication groups and decide when to communicate. We demonstrate the generalizability of our framework on two different perception tasks and show that it significantly reduces communication bandwidth while maintaining superior performance.
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Evaluation of an Uncertainty-Aware Late Fusion Algorithm for Multi-Source Bird's Eye View Detections Under Controlled Noise
UniKF, a Kalman-filter-based late fusion for BEV detections, achieves lower errors than IoU-based baselines on synthetic noise, but only marginal gains over the authors' own WLS method.