Benchmark Dataset Based on Category Maps with Indoor–Outdoor Mixed Features for Positional Scene Recognition by a Mobile Robot

Abstract

This study was conducted to develop original benchmark datasets that simultaneously include indoor–outdoor visual features. Indoor visual information related to images includes outdoor features to a degree that varies extremely by time, weather, and season. We obtained time-series scene images using a wide field of view (FOV) camera mounted on a mobile robot moving along a 392-m route in an indoor environment surrounded by transparent glass walls and windows for two directions in three seasons. For this study, we propose a unified method for extracting, characterizing, and recognizing visual landmarks that are robust to human occlusion in a real environment in which robots coexist with people. Using our method, we conducted an evaluation experiment to recognize scenes divided up to 64 zones with fixed intervals. The experimentally obtained results using the datasets revealed the performance and characteristics of meta-parameter optimization, mapping characteristics to category maps, and recognition accuracy. Moreover, we visualized similarities between scene images using category maps. We also identified cluster boundaries obtained from mapping weights.