Night-time indoor relocalization using depth image with Convolutional Neural Networks

In this work, we present a Convolutional Neural Network(CNN) with depth images as its inputs to solve the relocalization problem of a moving platform in night-time indoor environment. The developed algorithm can estimate the camera pose in an end-to-end manner with 0.40m and 7.49° errors in real time during night. It does not require any geometric computation as it directly uses a CNN for 6 DOFs pose regression. The architecture and its encoding methods of depth images are discussed. The proposed method is also evaluated on benchmark datasets collected from a motion capture system in our lab.