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Enhancing Multi-Robot Perception via Learned Data Association

arXiv 2107.00769 ↗First posted 1 Jul 2021cs.RO cs.CV cs.MAcited by 0

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Abstract (arXiv)

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.

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References · 18

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  11. 2017Convolutional neural network architecture for geometric matchingcited by 138
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6 more
  1. 2020Who2com: Collaborative Perception via Learnable Handshake Communicationcited by 127
  2. 2017Occlusion Aware Unsupervised Learning of Optical Flowcited by 101
  3. 2018DGC-Net: Dense Geometric Correspondence Networkcited by 67
  4. 2017Accurate Optical Flow via Direct Cost Volume Processingcited by 66
  5. 2017Toward Geometric Deep SLAMcited by 38
  6. 2019EMPNet: Neural Localisation and Mapping Using Embedded Memory Pointscited by 9

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