変化する環境でのテキスト検出を活用したロバストなオンボード自己位置推定
Robust Onboard Localization in Changing Environments Exploiting Text Spotting
屋内環境の構造変化に対応するため、看板などの文字情報をテキスト検出で認識し、LiDARとカメラを用いたパーティクフィルタによる自己位置推定に統合した。オフィス環境での実験で構造変化や人混みに対してロバストで、オンボード計算機で動作することを示した。
著者: Nicky Zimmerman, Louis Wiesmann, Tiziano Guadagnino, Thomas Läbe, Jens Behley, Cyrill Stachniss
分類: cs.RO
原文アブストラクト
Robust localization in a given map is a crucial component of most autonomous robots. In this paper, we address the problem of localizing in an indoor environment that changes and where prominent structures have no correspondence in the map built at a different point in time. To overcome the discrepancy between the map and the observed environment caused by such changes, we exploit human-readable localization cues to assist localization. These cues are readily available in most facilities and can be detected using RGB camera images by utilizing text spotting. We integrate these cues into a Monte Carlo localization framework using a particle filter that operates on 2D LiDAR scans and camera data. By this, we provide a robust localization solution for environments with structural changes and dynamics by humans walking. We evaluate our localization framework on multiple challenging indoor scenarios in an office environment. The experiments suggest that our approach is robust to structural changes and can run on an onboard computer. We release an open source implementation of our approach (upon paper acceptance), which uses off-the-shelf text spotting, written in C++ with a ROS wrapper.
