[1]ZHU Juntao,CHEN Qiang.Improvement of kinect performance in RGB-D visual odometer[J].CAAI Transactions on Intelligent Systems,2020,15(5):943-948.[doi:10.11992/tis.201903007]
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Improvement of kinect performance in RGB-D visual odometer

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 5 Page number: 943-948 Column: 学术论文—机器感知与模式识别 Public date: 2020-09-05
Title:
Improvement of kinect performance in RGB-D visual odometer
Author(s):
ZHU Juntao;  CHEN Qiang
Electrical and Electronic Engineering College, Shanghai University of Engineering and Technology, Shanghai 201600, China
Keywords:
kinect; lack of depth; fusion algorithm; feature points; iterative closest point; perspective-n-point; depth value; pose estimation; BA optimization model; g2o
CLC:
TP242.6
DOI:
10.11992/tis.201903007
Abstract:
Kinect is a 3D camera that gives you the depth values associated with every pixel. It uses structured infrared light to determine depth values. Apart from these, you also have access to raw RGB-D data, and even the raw infrared data. Aiming to solve the problem of insufficient depth values for the images captured by Kinect camera in RGB-D visual odometer, we propose a fusion optimization algorithm based on Perspective-n-Point and iterative closest point (ICP). Because of the lack of depth values, traditional ICP algorithm often loses feature points when iterating the camera pose; this results in excessive error, or we can say that the algorithm is unable to converge. This algorithm establishes bat algorithm optimization model by judging the depth of feature points and optimizes the feature point of poses and camera using g2o solver. Experiments show that the method is effective and improves the accuracy of camera pose estimation and the convergence success rate of the algorithm, thus improving the accuracy and robustness of RGB-D visual odometer.
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