Multi-agent Collaborative Perception for Robotic Fleet: A Systematic Review

TL;DR

This paper systematically reviews collaborative perception in multi-robot fleets, highlighting its benefits, evaluation frameworks, and demonstrating over 200% improvement in perception accuracy through experiments.

Contribution

It provides a comprehensive summary of research, testing frameworks, and experimental validation of collaborative perception in robotic fleets.

Findings

Over 200% improvement in perception accuracy with collaboration

Summarized findings from 20+ research papers

Discussed evaluation frameworks for autonomous systems

Abstract

Collaborative perception in multi-robot fleets is a way to incorporate the power of unity in robotic fleets. Collaborative perception refers to the collective ability of multiple entities or agents to share and integrate their sensory information for a more comprehensive understanding of their environment. In other words, it involves the collaboration and fusion of data from various sensors or sources to enhance perception and decision-making capabilities. By combining data from diverse sources, such as cameras, lidar, radar, or other sensors, the system can create a more accurate and robust representation of the environment. In this review paper, we have summarized findings from 20+ research papers on collaborative perception. Moreover, we discuss testing and evaluation frameworks commonly accepted in academia and industry for autonomous vehicles and autonomous mobile robots. Our experiments with the trivial perception module show an improvement of over 200% with collaborative perception compared to individual robot perception. Here's our GitHub repository that shows the benefits of collaborative perception: https://github.com/synapsemobility/synapseBEV