Time image sequence self-encoding statistics to improve visual odometer

2nd International Symposium on Resource Exploration and Environmental Science

生态环境科学

Zeng, Chuang^1 ; Yu, Hongyang^1

UESTC, Chengdu, China^1

关键词: Construction sites;    Dimension reduction;    Extracting features;    Extraction parameters;    Feature extraction algorithms;    Feature extraction and matching;    Real-time corrections;    Relevant features;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/170/2/022019/pdf
DOI  :  10.1088/1755-1315/170/2/022019

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Visual odometers are essential in SLAM applications is very important in the application of SLAM, and it is a test for visual odometer of plastering robot. The chaos of the construction site and the difficulty of extracting feature points on the wall have always been a bottleneck restricting the application of SLAM robots. In this paper, based on time series images, a neural network is trained. According to the real-time sequence scene prediction feature extraction algorithm parameters, the feature operator is extracted according to the predicted value. Then the feature operator is subjected to self-encoding dimension reduction and denoising, and finally the feature point is performed. Match. The experiment verifies that in the process of real-time visual feature detection, real-time correction of relevant feature extraction parameters by time series self-encoding statistics can improve the accuracy of feature extraction and matching.