A SLAM Algorithm Based on Multi-Constraint Learning Model

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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.