  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
