RGB-D based Stair Detection using Deep Learning for Autonomous Stair Climbing

• URL: http://arxiv.org/abs/2212.01098v1
• Date: Fri, 2 Dec 2022 11:22:52 GMT
• Title: RGB-D based Stair Detection using Deep Learning for Autonomous Stair Climbing
• Authors: Chen Wang, Zhongcai Pei, Shuang Qiu, Zhiyong Tang
• Abstract summary: We propose a neural network architecture with inputs of both RGB map and depth map. Specifically, we design the selective module which can make the network learn the complementary relationship between RGB map and depth map. Experiments on our dataset show that our method can achieve better accuracy and recall compared with the previous state-of-the-art deep learning method.
• Score: 6.362951673024623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stairs are common building structures in urban environment, 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 inputs of both RGB map and depth map. Specifically, we design the selective module which can make the network learn the complementary relationship between RGB map and depth map and effectively combine the information from RGB map and depth map in different scenes. In addition, we also design a line clustering algorithm for the post-processing of detection results, which can make full use of the detection results to obtain the geometric parameters of stairs. Experiments on our dataset show that our method can achieve better accuracy and recall compared with the previous state-of-the-art deep learning method, which are 5.64% and 7.97%, respectively. Our method also has extremely fast detection speed, and a lightweight version can achieve 300 + frames per second with the same resolution, which can meet the needs of most real-time detection scenes.

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