Improved closed-loop detection algorithm-based mobile robot vision SLAM (Simultaneous Location and Mapping) method

Abstract

The present invention provides an improved closed-loop detection algorithm-based mobile robot vision SLAM (Simultaneous Location and Mapping) method. The method includes the following steps that: S1,Kinect is calibrated through a using the Zhang Dingyou calibration method; S2, ORB feature extraction is performed on acquired RGB images, and feature matching is performed by using the FLANN (Fast Library for Approximate Nearest network); S3, mismatches are deleted, the space coordinates of matching points are obtained, and inter-frame pose transformation (R, t) is estimated through adopting thePnP algorithm; S4, structureless iterative optimization is performed on the pose transformation solved by the PnP; and S5, the image frames are preprocessed, the images are described by using the bagof visual words, and an improved similarity score matching method is used to perform image matching so as to obtain closed-loop candidates, and correct closed-loops are selected; and S6, an image optimization method centering cluster adjustment is used to optimize poses and road signs, and more accurate camera poses and road signs are obtained through continuous iterative optimization. With the method of the invention adopted, more accurate pose estimations and better three-dimensional reconstruction effects under indoor environments can be obtained.

Description

technical field [0001] The invention belongs to the field of mobile robot navigation, in particular to a vision-based synchronous positioning and map construction method. Background technique [0002] Simultaneous Location and Mapping (SLAM for short) is to use the sensors carried by the robot to obtain the information of the environment in which it is located to estimate the pose of the robot and build an environmental map. It is the key technology for the robot to move completely autonomously. At the same time, visual SALM is still the key to the current VR technology, and has broad application prospects in the future virtual reality industry. [0003] Vision sensors can obtain rich image information similar to the information perceived by the human eye. These rich environmental information provide more details for the construction of environmental maps, which can achieve better map construction effects. At present, great progress has been made in visual SLAM. In 2007, K...

AI Technical Summary

Problems solved by technology

However, the traditional closed loop detection algorithm is not efficient and has low accuracy in detecting closed loops.

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