Semantic map construction method based on depth camera and sweeping robot

Abstract

The invention provides a semantic map construction method based on a depth camera and a sweeping robot, and is applied to the technical field of robots. The method comprises the following steps: obtaining images of the sweeping robot at multiple positions in the located environment space through the depth camera, wherein the image comprises an RGB image and a depth image; constructing a three-dimensional map of the environment space through a simultaneous localization and mapping (SLAM) algorithm based on each depth map; carrying out semantic recognition on each RGB image through a pre-trainedneural network recognition model; obtaining semantic information of each obstacle in the environment space; fusing the three-dimensional map and the obtained semantic information of each obstacle toobtain a three-dimensional semantic map, that is, the three-dimensional semantic map of the environment space constructed by the application, compared with a two-dimensional map of the environment space constructed according to a laser radar, the richness of information contained in the constructed map of the environment space and the accuracy of the constructed map are improved, and the effectiveworking space range of the sweeping robot is expanded.

Description

technical field [0001] The present application relates to the field of robot technology, in particular, the present application relates to a depth camera-based semantic map construction method and a sweeping robot. Background technique [0002] As a smart appliance that can automatically clean the area to be cleaned, the sweeping robot can replace people to clean the ground, reducing the burden of people's housework, and is more and more recognized by people. The map construction of the application environment space of the sweeping robot is the basis for the sweeping robot to perform the cleaning work. How to construct the map of the application environment space of the sweeping robot has become a key issue. [0003] The problem to be solved by Simultaneous Localization and Mapping (SLAM) technology is: if a robot is placed in an unknown position in an unknown environment, is there a way for the robot to gradually draw a map that is completely consistent with the environment...

AI Technical Summary

Problems solved by technology

However, in the existing SLAM mapping method based only on lidar, lidar can only detect obstacle information on a 2D plane, and cannot detect information on the vertical direction of obstacles. The map constructed is a two-dimensional map, and the environment provided Space information is limited, and for some special obstacles (such as tables and chairs with hollow structures, etc.), it cannot be effectively detected and processed by lidar. In addition, since lidar must be configured at a position with a certain height to work effectively , so that the sweeping robot cannot be ultra-thin, making it impossible for the sweeping robot to enter a space with a small vertical distance to perform work

Therefore, the existing SLAM mapping method based only on lidar has the problem that the constructed map provides little information and low mapping accuracy, and there are problems that the sweeping robot cannot be ultra-thin and the working space is limited.

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