Laser point cloud scene similarity evaluation method

A similarity evaluation and laser point cloud technology, applied in the field of computer vision, to achieve accurate evaluation results, precise capture, and improve coding efficiency

CN113963184APending Publication Date: 2022-01-21杭州大数云智科技有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2022-01-21

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Abstract

The invention relates to a laser point cloud scene similarity evaluation method, which comprises the following steps of: A1, respectively carrying out semantic segmentation, instance segmentation, node feature coding and node feature aggregation on two frames of laser point cloud scene data Pi and Pj which need to be subjected to similarity evaluation to obtain graph features Ei and Ej of the two frames of laser point cloud scene data; A2, calculating a similarity vector for the graph features Ei and Ej by using a pre-trained neural tensor network V, carrying out dimensionality reduction on the similarity vector through a pre-trained full connection layer to obtain a similarity score Y of the graph features Ei and Ej, and evaluating the similarity of the graph features Ei and Ej according to the similarity score Y. The laser point cloud scene similarity evaluation method has the advantages that the method is more robust and stable for scene changes such as shielding and rotation, the calculation method is simple, the calculation amount is small, the precision is high, and the speed is high.
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Description

technical field

[0001] The invention relates to the technical field of computer vision, in particular to a laser point cloud scene similarity evaluation method. Background technique

[0002] In recent years, simultaneous localization and mapping (SLAM) technology has developed rapidly and plays a key role in robotic applications and autonomous driving. Loop closure detection is an important problem in SLAM, referring to the ability of a robot or moving vehicle to recognize whether it has ever been to a place. This is the most effective way to eliminate the accumulated drift error of the odometry, which can help to build a more accurate map and achieve more accurate positioning. The research of vision-based position recognition has been studied for a long time, and many successful methods have emerged. Most image-based methods work by extracting feature descriptors and then encoding them using methods such as bag-of-words. Related scenes are retrieved by comparing global d...

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Embodiment Construction

[0091] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention, and the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0092] A laser point cloud scene similarity evaluation method, comprising the following steps:

[0093] Step A1: Two frames of laser point cloud scene data P that need to be evaluated for similarity i ,P j Perform step S1.1-step S1.3 respectively to obtain the graph feature E of two frames of laser point cloud scene data i ,E j ; In the actual application of the robot, the current scene and a certain frame in the historical scene are generally used a...