学習されたデータ関連付けによるマルチロボット知覚の強化
Enhancing Multi-Robot Perception via Learned Data Association
未登録の多視点RGB画像から分散セマンティックセグメンテーションを行うため、各ロボットが局所的に特徴を符号化・復号し、不確実性を考慮した特徴交換を行う拡張可能なニューラルネットワークを提案した。
著者: Nathaniel Glaser, Yen-Cheng Liu, Junjiao Tian, Zsolt Kira
分類: cs.RO, cs.CV, cs.MA
原文アブストラクト
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.
関連論文
- 協調マルチロボット知覚のための視点と特徴量の同時選択マルチロボット知覚
