A Deep-Learning-based Strategy for Kidnapped Robot Problem in Similar Indoor Environment

Abstract

We present a deep-learning-based strategy that only uses a 2D LiDAR sensor to solve the kidnapped robot problem in similar indoor environments. First, we converted a set of 2D laser data into an RGB-image and an occupancy grid map and stacked them into a multi-channel image. Then, a neural network structure with five convolutional layers and four fully connected layers was designed to regress the 3-DOF robot pose. Finally, the network was trained using multi-channel images as input. We also improved the network structure to identify the scene where the robot is localized. Extensive experiments have been conducted in practice with a real mobile robot, verifying the effectiveness of the proposed strategy. Our network can obtain approximately 2m and 5∘ accuracy indoors, and the scene classification accuracy of our network reaches up to 98%.