Method for creating semi-dense cognitive map for binocular SLAM (simultaneous localization and mapping)

Abstract

The invention discloses a method for creating a semi-dense cognitive map for binocular SLAM (simultaneous localization and mapping). The method comprises the following steps of (1) localizing and identifying an interested object in a key frame; (2) dividing the key frames by meshes, and further finely dividing the meshes of where the interested object is located; (3) extracting a mesh key point ofeach mesh; (4) using a Gaussian-distributed depth filter to representing the depth evaluation of the mesh key point by using a Gaussian-distributed depth filter; (5) matching the mesh key point of the key frame in the existing frame; (6) calculating and updating the depth filter of the mesh key point; (7) when the binocular SLAM selects the new key frame, using the depth filter of the existing key frame to initializinge the new key frame by using the depth filter of the existing key frame; (8) after the binocular SLAM is finished, converting the depth estimation into the specific map representation. The method has the advantages that the autonomous navigation and obstacle avoidance of a mobile robot, and the three-dimensional environment reconstruction are realized; by creating the cognitive map for the environment, the interaction between human and the mobile robot as well as between the human and the environment is realized.

Description

technical field [0001] The present invention relates to the field of mobile robot, visual SLAM and map creation, in particular to a semi-dense cognitive map creation method for binocular SLAM. Background technique [0002] Simultaneous Localization and Mapping (SLAM) refers to the fact that a mobile robot obtains environmental information through its own sensors in an unknown environment, and continuously estimates its own pose during the movement process, and at the same time creates a map of its surrounding environment. map. SLAM is mainly used to solve the "positioning" and "map creation" of mobile robots. On the one hand, mobile robots need to know where they are currently on the map (positioning), and on the other hand, they need to create a map of the surrounding environment (map creation). Since it was proposed, SLAM has quickly attracted the attention and research of many scholars, and has always been a research hotspot in the field of mobile robots. [0003] In re...

AI Technical Summary

Problems solved by technology

Sparse maps can only be used for the positioning of mobile robots, but not for other app

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