2019/08/30 by Minjie Hua, Hua, Minjie, Yibing Nan +3 · 1 citation
Computer Science · Engineering · #Advanced Neural Network Applications #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1908.11675
Accepted by CVRSUAD 2019 (ICCV Workshop)
arxiv created 2019/08/30 · openalex publication_date 2019/08/30 · arxiv updated 2019/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents a novel obstacle avoidance system for road robots equipped with RGB-D sensor that captures scenes of its way forward. The purpose of the system is to have road robots move around autonomously and constantly without any collision even with small obstacles, which are often missed by existing solutions. For each input RGB-D image, the system uses a new two-stage semantic segmentation network followed by the morphological processing to generate the accurate semantic map containing road and obstacles. Based on the map, the local path planning is applied to avoid possible collision. Additionally, optical flow supervision and motion blurring augmented training scheme is applied to improve temporal consistency between adjacent frames and overcome the disturbance caused by camera shake. Various experiments are conducted to show that the proposed architecture obtains high performance both in indoor and outdoor scenarios.