SLAM-based visual perception mapping algorithm and mobile robot

Abstract

The invention discloses an SLAM-based visual perception mapping algorithm and a mobile robot. The algorithm comprises the steps of obtaining image data of a to-be-mapped area environment and rotatingspeed data of a driving wheel; performing feature extraction on a color image in the image data; after the rotating speed data is calculated and processed according to a kinematics equation, fusing and optimizing the rotating speed data and a depth image in the image data by using a method of minimizing an encoder and a re-projection error to obtain a current frame pose of the robot; screening current frame image data acquired when a robot walks in a to-be-mapped area to obtain a key frame, identifying and classifying objects in an environment by utilizing a color image of the key frame, deleting the classified dynamic objects from the key frame, carrying out sparse processing on static objects, and carrying out local loopback for correction; taking pixel points of the color image and thecorresponding depth image in the key frame as map points, storing the map points through an octree, and carrying out mapping.

Description

technical field [0001] The invention relates to the fields of visual perception and mobile robots, in particular to a SLAM-based visual perception mapping algorithm and a mobile robot. Background technique [0002] As an important branch of artificial intelligence, computer vision has gradually moved from theory to practical production application, and is widely used in high-tech products such as face recognition systems, driverless cars, and drones. The SLAM technology has greatly promoted the development of robot visual perception technology, enabling robots to use their eyes to explore the surrounding environment and capture information like humans, thereby "knowing" their own environment. [0003] Some improved algorithms or positioning algorithms for SLAM have been proposed in the prior art, but the functions are not perfect. For example, in the actual robot and space environment, the scheme cannot make a large-scale map construction, and there is a large drift. The es...

AI Technical Summary

Problems solved by technology

[0003] Some improved algorithms or positioning algorithms for SLAM have been proposed in the prior art, but the functions are not perfect. For example, in the actual robot and space environment, the scheme cannot make a large-scale map construction, and there is a large drift. The estimation is not accurate enough. At the same time, the existing scheme needs to complete the map construction in an environment that is completely static objects

Images