Research on Pose Estimation of Mobile Robot Based on Convolutional Neural Network

Abstract

In this paper, we propose a new learning scheme for generating camera pose to be used for visual odometry. Pose estimation of mobile robot in unknown environment is formulated as a learning problem. We train a convolutional neural network end to end to compute the 6-dof pose of the robot from a series of images. The architecture is a kind of residual network with multiple attention resblocks, which can give features different weights in order to improve the accuracy of pose prediction. The network estimates ego-motion taking monocular image sequences as input instead of separate images. We call this novel network architecture Posenet. Compared to traditional methods, results are more accurate and in which we can see the potential of pose estimation methods with convolutional neural network.