Deep-Learning-based Relocalization in Large-Scale outdoor Environment

Abstract For the issue of relocalization, this paper proposed a deep-learning-based method for outdoor large-scale environment. In the first step, we projected a 3D Light Detection and Ranging(LiDAR) scan onto three 2D images from top to bottom. Then a densenet-based neural network structure was designed to regress a 4-DOF robot pose. These images are then stacked together, fed into the proposed DCNN architecture, and the output is the predicted robot pose. Extensive experiments have been conducted in practice with a real mobile robot, verifying the effectiveness of the proposed strategy. Our network can obtain approximately 3.5m and 4◦ accuracy outdoors.