RGB-D画像を用いた深層学習による階段検出と自律階段昇降
RGB-D-based Stair Detection using Deep Learning for Autonomous Stair Climbing
RGB画像と深度画像を入力とするニューラルネットワークを提案し、両者の相補的な情報を選択的に統合することで、夜間や視覚的手がかりが乏しい環境でも高精度に階段を検出する手法を開発した。
著者: Chen Wang, Zhongcai Pei, Shuang Qiu, Zhiyong Tang
分類: cs.CV, cs.RO
原文アブストラクト
Stairs are common building structures in urban environments, and stair detection is an important part of environment perception for autonomous mobile robots. Most existing algorithms have difficulty combining the visual information from binocular sensors effectively and ensuring reliable detection at night and in the case of extremely fuzzy visual clues. To solve these problems, we propose a neural network architecture with RGB and depth map inputs. Specifically, we design a selective module, which can make the network learn the complementary relationship between the RGB map and the depth map and effectively combine the information from the RGB map and the depth map in different scenes. In addition, we design a line clustering algorithm for the postprocessing of detection results, which can make full use of the detection results to obtain the geometric stair parameters. Experiments on our dataset show that our method can achieve better accuracy and recall compared with existing state-of-the-art deep learning methods, which are 5.64% and 7.97%, respectively, and our method also has extremely fast detection speed. A lightweight version can achieve 300 + frames per second with the same resolution, which can meet the needs of most real-time detection scenes.
