Pose determination method and device of mobile platform, equipment and storage medium

Abstract

The embodiment of the invention discloses a pose determination method and device of a mobile platform, equipment and a storage medium. The method comprises the following steps: controlling a camera of a mobile platform to shoot an RGB image and a depth image of an environment reference object; extracting point features of the environment reference object in the RGB image, and matching the point features of the front and back two frames of RGB images to obtain a first matching result; extracting plane features of the environment reference object in the depth images, and matching the plane features of the front and rear frames of depth images based on the first matching result to obtain a second matching result; and determining the pose of the mobile platform according to the first matching result and the second matching result. The method is higher in robustness, lower in sensitivity to light change, higher in feature matching accuracy when the framing object point features are deficient or textures are weak, and more accurate in pose estimation.

Description

technical field [0001] The present invention relates to the field of computer vision, in particular to a method, device, device and storage medium for determining the pose of a mobile platform. Background technique [0002] Simultaneous localization and mapping (SLAM, Simultaneous Localization and Mapping) refers to the process of establishing an environmental model and estimating the movement of the mobile platform during the movement process without prior information on the environment. The concept of SLAM first appeared in 1986 , and then the field of SLAM has developed rapidly, and it is also applied to all aspects of life, such as sweeping robots and logistics robots. Generally, according to the sensor, it can be divided into two categories: lidar-based SLAM and vision-based SLAM. [0003] LiDAR-based SLAM started early and has a high degree of maturity in theory and technology. However, the cost of LiDAR is high. In recent years, with the advancement of camera technol...

AI Technical Summary

Problems solved by technology

However, visual SLAM using line features is affected by the uncertain recognition of line segment endpoints, and the matching accuracy of features is not high

[0005] At present, visual SLAM will have problems such as tracking failure, feature mismatch, and feature point loss in scenes with few feature points and weak texture, resulting in inaccurate estimated poses.

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