Enhancing Multi-Robot Perception via Learned Data Association
Abstract
In this paper, we address the multi-robot collaborative perception problem, specifically in the context of multi-view infilling for distributed semantic segmentation. This setting entails several real-world challenges, especially those relating to unregistered multi-agent image data. Solutions must effectively leverage multiple, non-static, and intermittently-overlapping RGB perspectives. To this end, we propose the Multi-Agent Infilling Network: an extensible neural architecture that can be deployed (in a distributed manner) to each agent in a robotic swarm. Specifically, each robot is in charge of locally encoding and decoding visual information, and an extensible neural mechanism allows for an uncertainty-aware and context-based exchange of intermediate features. We demonstrate improved performance on a realistic multi-robot AirSim dataset.
Cite
@article{arxiv.2107.00769,
title = {Enhancing Multi-Robot Perception via Learned Data Association},
author = {Nathaniel Glaser and Yen-Cheng Liu and Junjiao Tian and Zsolt Kira},
journal= {arXiv preprint arXiv:2107.00769},
year = {2021}
}
Comments
Accepted to ICRA 2020 Workshop on "Emerging Learning and Algorithmic Methods for Data Association in Robotics"; associated spotlight talk available at https://www.youtube.com/watch?v=-lEVvtsfz0I&t=16743s