标题：Vision-based mobile robot's environment outdoor perception
作者：Jing, Punan ;Zheng, Wanqiang ;Xu, Qingyang
作者机构：[Jing, Punan ;Zheng, Wanqiang ;Xu, Qingyang ] School of Mechanical, Electrica and Information Engineering Shandong University, Weihai, Shandong; 26420 更多
会议名称：3rd International Conference on Computer Science and Application Engineering, CSAE 2019
会议日期：22 October 2019 through 24 October 2019
来源：ACM International Conference Proceeding Series
关键词：Automatic obstacle avoidance; Multi-task learning; Semantic segmentation; Target detection
摘要：Scene perception of mobile robot is that the robot realizes the perception and understanding of the surrounding environment through a series of sensors configured by itself, and the perception technology based on vision has always been a hot and difficult point in research. Therefore, in terms of visual environment perception algorithms, there have been numerous valuable research achievements in recent years. In particular, the object detection and segmentation algorithm based on convolutional neural network has shown good performance in simple scene, but there are still some limitations when these algorithms are directly applied to actual scenes. In this paper, we study the practical application of vision-based environmental perception of mobile robots in complex scenes, presents a unified algorithm architecture of object detection and road segmentation, and build a vision-based mobile robot's environment perception system. Firstly, the image acquisition of the surrounding environment is completed by the computer camera mounted on the robot, and then the obstacle detection and the segmentation of the drivable area are achieved by using the target detection and segmentation algorithm. In order to meet the real-time requirements, the detection and segmentation algorithms share the same feature extraction network, and are jointly trained as one framework. Finally, according to the detection and segmentation results, the robot can automatically avoid obstacles and move in the drivable area. © 2019 Association for Computing Machinery.