Robot positioning and mapping method and device based on depth image

Abstract

The invention relates to a robot positioning and mapping method and device based on a depth image, a computer device and a storage medium. The method comprises the following steps: using an RGB-D camera for carrying out surrounding environment detection; acquiring an RGB image and a depth image; based on an RGB image and a depth image, determining continuous image frames, calculating continuous image frames by adopting a sparse direct method; obtaining an initial pose of the current frame; few calculations are carried out; determining initial pose, increasing the pose acquisition speed; meanwhile, calculating and optimizing the initial pose of the current frame by adopting a feature point method; obtaining an accurate pose of the current frame; the illumination change or the fast movementis ensured; according to the accuracy of pose estimation, performing key frame selection according to the accurate pose of the current frame to obtain a key frame sequence, performing local mapping and optimization based on the key frame sequence to generate an environment map. The environment map is generated efficiently and accurately, and the efficiency and accuracy of robot positioning and mapping are improved.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular, to a robot positioning and mapping method, device, computer equipment and storage medium. Background technique [0002] In recent years, technologies such as unmanned vehicles, robots, drones, and AR / VR have developed rapidly. At the same time, positioning and map construction have also become hot research issues and are considered to be key basic technologies in these fields. This is because in an unknown environment, the accurate positioning of the robot requires an accurate environment map, and to construct an accurate environment map, the robot also needs to know its exact location in the environment. The SLAM (Simultaneous Localization and Mapping) technology enables robots and other carriers to start from unknown locations in an unknown environment, and use a series of sensors (lidar, GPS, IMU, cameras, etc.) they carry to observe the characteristics of the en...

AI Technical Summary

Problems solved by technology

PTAM meets the real-time requirements of visual SLAM, but in the process of implementing this application, the inventors found that there are at least the following problems in the prior art: in larger scenarios, the use of PTAM to achieve visual SLAM relocation is prone to failure. In the scene, how to accurately locate and map the robot has become an urgent problem to be solved

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