A New Floor Region Estimation Algorithm Based on Deep Learning Networks with Improved Fuzzy Integrals for UGV Robots

Abstract

In this article, a new floor estimation algorithm based on multiple deep learning image segmentation and conventional texture segmentations using fuzzy integrals theory is proposed. The proposed algorithm combines an FCN-8s, a DeepLabv2, and Canny Edge Detection with superpixel segmentation, two deep learning networks, and one texture classifier to recognize a walkable floor area for UGV robots. The authors intersect three results with an Improved Fuzzy Integrals (IFI) method. The experimental results show that the combination algorithm accuracy can reach up to 97.63% on average without any other sensor assistance. In order to achieve real-time performance, the proposed algorithm has been implemented on an NVIDIA Jetson TX2 embedded platform with ROS compatible environment supporting.