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Graph-based vision SLAM (simultaneous localization and mapping) method

A technology of visual and visual words, applied in directions such as road network navigators

Inactive Publication Date: 2015-02-25
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0005] The present invention will use the corresponding relationship between continuous image frames to solve the problem of local data association

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  • Graph-based vision SLAM (simultaneous localization and mapping) method

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Embodiment Construction

[0053] Preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0054] Graph-based SLAM method, Grisetti proposed that the graph-based SLAM method is divided into two parts: graph construction and graph optimization. The construction of the graph is established through data association, that is, the constraint relationship between graph nodes. Data association includes local data association and global data association. Local data association refers to the matching between consecutive image frames to solve the problem of relative pose estimation. Global data association Obtained by closed-loop detection. Graph-based SLAM can be understood as a sparse graph of nodes and constraints between nodes. The node of the graph is the position x of the robot 0 , x 1 ,...,x T n features m in the map 0 , m 1 ,...,m n-1 . Constraints are successive robot poses x i , x i-1 and the relative positions between th...

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Abstract

The invention discloses a graph-based vision SLAM (simultaneous localization and mapping) method. According to the method, the matching relation between an image and visual feature can be obtained based on the natural feature probability vector representation of the image, and the relative pose between two interframes can be calculated by utilizing the space geometry relation of images. Data association of visual odometry is obtained by utilizing the corresponding relation of continuous images, so that all constraints in an image sequence can be obtained. The camera relative pose is taken as a node in a map, the space constrained relation of image interframes is taken as an edge, so that an estimated track map based on the camera relative pose is constructed. Finally, a maximum likelihood method is employed for optimizing the map, and optimized pose estimation is obtained through a random gradient descent method. Related experiments are performed in the laboratory environment based on the provided method, also the moving track of a robot is displayed, and the validity of the algorithm is confirmed.

Description

technical field [0001] The invention relates to a graph-based visual SLAM method. Background technique [0002] Most visual SLAM algorithms use the method of feature tracking, using visual features as natural landmarks to construct an environmental feature map, and at the same time, by matching with the landmarks in the previously created environmental map (natural landmark library), the current pose of the robot is estimated. Realize robot positioning. Since the two processes of map construction and pose estimation are noisy, the entire SLAM process is uncertain. [0003] At present, the two most popular algorithms, EKF-SLAM and PF-SLAM, describe the uncertainty of information with probability. Siegwart et al. proposed a single-camera-based EKF-SLAM algorithm, which uses multi-feature matching to locate and reduce positioning errors. The algorithm The disadvantage is that it needs to establish a motion model and an observation model, the calculation complexity is high, an...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/32
CPCG01C21/32
Inventor 梁志伟徐小根
Owner NANJING UNIV OF POSTS & TELECOMM
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