Image appearance based loop closure detecting method in monocular vision SLAM (simultaneous localization and mapping)

Abstract

The invention discloses an image appearance based loop closure detecting method in monocular vision SLAM (simultaneous localization and mapping). The image appearance based loop closure detecting method includes acquiring images of the current scene by a monocular camera carried by a mobile robot during advancing, and extracting characteristics of bag of visual words of the images of the current scene; preprocessing the images by details of measuring similarities of the images according to inner products of image weight vectors and rejecting the current image highly similar to a previous history image; updating posterior probability in a loop closure hypothetical state by a Bayesian filter process to carry out loop closure detection so as to judge whether the current image is subjected to loop closure or not; and verifying loop closure detection results obtained in the previous step by an image reverse retrieval process. Further, in a process of establishing a visual dictionary, the quantity of clustering categories is regulated dynamically according to TSC (tightness and separation criterion) values which serve as an evaluation criterion for clustering results. Compared with the prior art, the loop closure detecting method has the advantages of high instantaneity and detection precision.

Description

technical field [0001] The invention aims at the closed-loop detection problem in the monocular vision synchronous positioning and map construction (simultaneous localization and mapping, SLAM) of mobile robots, and provides a closed-loop detection method based on image appearance in monocular vision SLAM. The invention belongs to mobile robot navigation technology field. Background technique [0002] Synchronous positioning and map construction are basic issues and research hotspots in the field of mobile robot navigation. Whether the ability to synchronize positioning and map construction is considered by many people to be the key prerequisite for a robot to achieve autonomous navigation. In the SLAM process, the robot realizes self-localization and builds an environmental map at the same time. Due to the lack of prior knowledge and the uncertainty of the environment, the robot needs to judge whether the current position is in the environment area that has been visited dur...

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Problems solved by technology

[0004] In terms of visual SLAM closed-loop detection, Angeli et al. proposed a topological closed-loop detection method based on enhanced vision, and Cummins et al. proposed a probabilistic closed-loop detection method based on topological appearance. Although these two methods are effective in large-scale environments, detection, but cannot meet the high-efficiency and real-time closed-loop detection requirements in SLAM problems

RTAB-MAP is a real-time closed-loop detection method based on the appearance of the scene. Its powerful memory management function enables the robot to process each frame of image online for a long time, but the detection accuracy is low and error-prone closed-loop detection

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