Laser SLAM (simultaneous localization and mapping) closed loop detection method based on deep learning
A closed-loop detection and deep learning technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve the problems of inability to meet high-precision laser SLAM positioning and composition requirements, low computing efficiency, and adaptability limitations. Achieve the effect of SLAM closed-loop detection, improve efficiency, and improve accuracy
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[0035] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments:
[0036] A laser SLAM closed-loop detection method based on deep learning, including the following steps:
[0037] Step 1: Construct a point cloud data sample pair data set: collect laser point cloud data, divide the laser point cloud data into single samples according to the time sequence of collection and a certain interval, and extract a certain number of sample pairs from the point cloud data samples, According to the similarity between the two samples in the sample pair, the sample pair is divided into a positive sample pair and a negative sample pair; a certain number of positive sample and negative sample pairs are constructed, wherein the positive sample pair and the negative sample pair constitute a point cloud data sample Set, each sample pair is composed of two samples, and the label of the sample pair is determined according to the similarity...
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