ロボット探索における地図完成度の推定
Estimating Map Completeness in Robot Exploration
部分的なグリッド地図から探索済み領域の割合と未探索領域の探索価値を深層畳み込みニューラルネットワークで推定し、探索を早期終了する基準を提案した。
著者: Matteo Luperto, Marco Maria Ferrara, Giacomo Boracchi, Francesco Amigoni
分類: cs.RO
原文アブストラクト
In this paper, we propose a method that, given a partial grid map of an indoor environment built by an autonomous mobile robot, estimates the amount of the explored area represented in the map, as well as whether the uncovered part is still worth being explored or not. Our method is based on a deep convolutional neural network trained on data from partially explored environments with annotations derived from the knowledge of the entire map (which is not available when the network is used for inference). We show how such a network can be used to define a stopping criterion to terminate the exploration process when it is no longer adding relevant details about the environment to the map, saving, on average, 40% of the total exploration time with respect to covering all the area of the environment.
