Convolutional Neural Network Based Sensors for Mobile Robot Relocalization

Abstract

Recently many deep Convolutional Neural Networks (CNN) based architectures have been used for predicting camera pose, though most of these have been deep and require quite a lot of computing capabilities for accurate prediction. For these reasons their incorporation in mobile robotics, where there is a limit on the amount of power and computation capabilities, has been slow. With these in mind, we propose a real-time CNN based architecture which combines low-cost sensors of a mobile robot with information from images of a single monocular camera using an Extended Kalman Filter to perform accurate robot relocalization. The proposed method first trains a CNN that takes RGB images from a monocular camera as input and performs regression for robot pose. It then incorporates the relocalization output of the trained CNN in an Extended Kalman Filter (EKF) for robot localization. The proposed algorithm is demonstrated using mobile robots in GPS-denied indoor and outdoor environments.