Open AccessA SLAM Algorithm Based on Multi-Constraint Learning Model
Author(s) -
Haibin Shi,
Minghao Guo,
Yuanbin Zou,
Zhi Xu
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1267/1/012098
Subject(s) - robustness (evolution) , unsupervised learning , artificial intelligence , computer science , constraint (computer aided design) , dynamic time warping , image warping , consistency (knowledge bases) , machine learning , pattern recognition (psychology) , algorithm , mathematics , biochemistry , chemistry , geometry , gene
Unsupervised Learning based SLAM algorithm has lately drawn significant attention for its potential in label-free leaning ability and robustness to camera parameters and environmental variations. In order to achieve better robustness and accuracy, a multi-constraint learning model is proposed. In contrast to traditional geometry-based methods, multi-constraint unsupervised learning models optimize the photometric consistency over image sequences by warping one view into another; make the Network learning more geometrically information. A lot of experiments on the KITTI data set show that our model is superior to previous unsupervised methods and has comparable results with the supervised method.