Open AccessResearch on Pose Estimation of Mobile Robot Based on Convolutional Neural Network
Author(s) -
Min Yue,
Guohong Fu,
Ming Wu
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/565/1/012013
Subject(s) - artificial intelligence , convolutional neural network , computer science , pose , visual odometry , computer vision , mobile robot , monocular , monocular vision , articulated body pose estimation , residual , 3d pose estimation , robot , artificial neural network , algorithm
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.