A closed-loop automatic detection method of laser slam 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, adaptability limitations, and low computing efficiency. Achieve the effect of SLAM closed-loop detection, improve accuracy, and improve efficiency

Active Publication Date: 2019-11-08
WUHAN UNIV
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AI Technical Summary

Problems solved by technology

However, the computational efficiency of the ICP algorithm is low, and the feature extraction, coding, and classification of the bag-of-words technology are usually based on the artificially constructed feature space, and its adaptability is limited. Poor reliability, unable to meet high-precision laser SLAM positioning and composition requirements

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  • A closed-loop automatic detection method of laser slam based on deep learning
  • A closed-loop automatic detection method of laser slam based on deep learning
  • A closed-loop automatic detection method of laser slam based on deep learning

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Embodiment Construction

[0036] Below in conjunction with accompanying drawing and specific embodiment the present invention will be described in further detail:

[0037] A laser SLAM closed-loop detection method based on deep learning, comprising the following steps:

[0038] Step 1: Construct a point cloud data sample pair dataset: collect laser point cloud data, divide the laser point cloud data into individual 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. And according to the similarity of two samples in the sample pair, the sample pair is divided into a positive sample pair and a negative sample pair; construct a certain number of positive sample pairs and negative sample pairs, wherein, the positive sample pairs and negative sample pairs constitute point cloud data samples Each sample pair is composed of two samples, and the label of the sample pair is determined according to the simila...

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Abstract

The invention discloses a laser SLAM closed-loop detection method based on deep learning, which converts the problem of SLAM closed-loop detection into the retrieval problem of SALM data samples, and innovatively constructs a deep Hash network to perform Hash on laser point cloud samples Code, and then calculate the sample similarity on the basis of Hash code, to achieve fast retrieval of similar samples and SLAM closed-loop detection. The constructed deep Hash network takes advantage of deep learning to obtain more reliable binary codes than traditional coding techniques, thereby greatly improving the accuracy of retrieval; a new point cloud feature extraction algorithm is designed, which is used for spatial division of point clouds Based on the feature map and projection, the reliable description of the laser point cloud sample data and the effective connection with the deep Hash network are realized. The constructed deep Hash network adopts offline training, and the trained model can meet the real-time encoding requirements.

Description

technical field [0001] The invention relates to the field of mobile mapping and autonomous navigation, in particular to a laser SLAM closed-loop automatic detection method based on deep learning. [0002] technical background [0003] SLAM (simultaneous localization and mapping), real-time positioning and map construction. SLAM was first proposed in 1988. Due to its important theoretical and application value, it is considered by many scholars to be the key to realizing a truly fully autonomous mobile robot. The SLAM problem can be described as: the robot starts to move from an unknown position in an unknown environment, locates itself according to position estimation and maps during the movement process, and builds an incremental map on the basis of its own positioning to realize the autonomous positioning and positioning of the robot. navigation. At present, the sensors used in SLAM are mainly divided into two categories, one is lidar, and the other is camera. The corresp...

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Application Information

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Patent Type & AuthorityPatents(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/10G06N3/045G06F18/22
Inventor邹勤
OwnerWUHAN UNIV