Abstract
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.
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