Semantic SLAM method and system based on object and plane features

Abstract

The invention discloses a semantic SLAM method and system based on object and plane features, and belongs to the technical field of computer vision, and the method comprises the steps: obtaining the RGB-D image stream of a scene; performing frame-by-frame tracking on the RGB-D image stream to obtain a key frame image; constructing a local map about the scene by utilizing the key frame image; carrying out plane segmentation on the depth map of the key frame image to obtain a current plane, constructing a global plane map by utilizing the current plane, carrying out object detection on the key frame image to obtain a detection frame and confidence, reconstructing point cloud of an object by utilizing the detection frame and the confidence, and merging feature points in the detection frame into the object to obtain a global object map; and performing loop-back detection by using the key frame image to obtain a loop-back frame, and performing loop-back correction and optimization on the plane constraint and the object constraint by using the loop-back frame to obtain a plane map and an object map of the scene. According to the method, the SLAM optimization performance can be improved,and the semantic description of the environment is enhanced.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and more specifically relates to a semantic SLAM method and system based on object and plane features. Background technique [0002] Simultaneous localization and mapping (SLAM) is a popular research field in recent years. This technology is proposed for the positioning and mapping of robots in unknown environments. After a period of development, the main framework of the current visual SLAM technology has matured, mainly including visual odometer, back-end optimization, loop detection and other parts. [0003] The maps constructed by traditional SLAM technology are mostly composed of low-level geometric elements (points, lines, planes, etc.), which lack high-level semantic information while having a certain ability to describe the environment. When the robot needs to interact with objects in the environment during motion, we need to describe the semantic information of the environment. ...

AI Technical Summary

Problems solved by technology

[0006] In view of the above defects or improvement needs of the prior art, the present invention provides a semantic SLAM method and system based on object and plane features, thereby solving the technical problems of lack of semantic information and poor SLAM optimization ability

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