Visual Global Localization Based on Deep Neural Netwoks for Self-Driving Cars

In this work, we present a visual global localization system based on Deep Neural Networks (DNNs) for self-driving cars, named DeepVGL (Deep Visual Global Localization). In training mode, DeepVGL is trained with images and associated poses from datasets built during the mapping process; and, in operating mode, DeepVGL receives images captured online and infers the global poses of the self-driving car. To assess the performance of DeepVGL, we carried out experiments using datasets collected by experimental self-driving cars on trips made over long periods of time, thus including significant changes in the environment, traffic volume and weather conditions, as well as different times of the day and seasons of the year. Experimental results show that DeepVGL is able to correctly locate the self-driving car up to 75% of the time for 0.2 m of accuracy and 96% of the time for 5 m of accuracy.