Bag of visual words-based closed-loop detection method for mobile robot maps

Abstract

The invention discloses a bag of visual words-based closed-loop detection method for mobile robot maps. The method comprises the following steps of: providing a visual dictionary-based image similarity detection algorithm, and taking the algorithm as a front end of closed-loop detection, namely, judging candidate closed-loop nodes through image similarity detection; and further determining the closed-loop nodes by adoption of a time constraint and space position verification method. A large number of experiments prove that the closed-loop detection method is capable of correctly detecting various different closed loops, is high in algorithm calculation speed, and is adaptive to relatively high instantaneity requirements, for the closed-loop detection part, of SLAM.

Description

technical field [0001] The invention belongs to the field of map creation, in particular to a method for detecting a closed loop of a mobile robot map based on a bag of visual words. Background technique [0002] In synchronous positioning and map creation SLAM, closed-loop detection refers to judging whether the robot is in a certain area visited before according to the information obtained by the sensor, or whether the current location of the robot is in the created map. corresponding description. In the SLAM method based on graph optimization, closed-loop detection is a very critical link. Correct closed-loop detection helps to correct the odometer error, so as to obtain a map with small error and consistent global information, but wrong closed-loop detection will increase Errors can even destroy the entire map. [0003] Loop closure detection is not only a focus of map creation in an unknown environment, but also a difficulty, mainly reflected in the following aspects:...

AI Technical Summary

Problems solved by technology

[0004] (1) SLAM generally operates in similar scenes, and similar scenes do not necessarily come from the same scene, which creates perceptual ambiguity, which will eventually lead to wrong closed-loop detection results

[0005] (2) When performing closed-loop detection, it is necessary to compare the current observation data with the processed and stored information to determine whether they are in the same scene, and the data that needs to be processed and stored increases with the increase in the running time of the SLAM system. Therefore, the large data scale is also a difficult problem for closed-loop detection

[0006] (3) The premise of graph optimization is to establish a complete closed-loop constraint. After the closed-loop is optimized, the cumulative error can be reduced, but the wrong closed-loop result will have a great impact on the final calculation accuracy, and even cause serious distortion of the entire map. It can be seen that it is particularly important to judge the accuracy of closed-loop detection.

Images