CNN based enhanced perception for mobile robots in rainy environments - Lan, Xi (Nanyang Technological University, 2021)
Image enhancement and robot perception are hot research areas in recent years. With the state-of-art algorithms and technologies employed, the unmanned ground vehicles (UGVs) can cope with daily tasks in normal environments. For example, many latest cars are carrying some half-unmanned driving techniques implemented by Li-DAR or other distance detecting devices. However, most perception tasks may fail facing the challenging situations such as rainy or foggy weather. Therefore, focusing on binocular images under heavy rain and foggy circumstances, an end-to-end disparity estimation network is proposed in this paper.