A Semantic Slam Object Association Method Based on Hierarchical Topic Model

A technology of hierarchical themes and objects, applied in character and pattern recognition, image analysis, image enhancement, etc., can solve unreliable problems, achieve accurate object association, and promote the effect of camera pose estimation

Active Publication Date: 2021-08-03
ZHEJIANG UNIV OF TECH
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Problems solved by technology

But in practice, with the movement of the robot, the information captured by the sensor is noisy, and it is unreliable to estimate the movement of the robot only by the information of the sensor, so it needs the assistance of various optimization algorithms

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  • A Semantic Slam Object Association Method Based on Hierarchical Topic Model
  • A Semantic Slam Object Association Method Based on Hierarchical Topic Model
  • A Semantic Slam Object Association Method Based on Hierarchical Topic Model

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Embodiment Construction

[0030] The present invention will be further described below in conjunction with the accompanying drawings.

[0031] refer to Figure 1 ~ Figure 3 , a semantic SLAM object association method based on a hierarchical topic model, comprising the following steps:

[0032] 1) Perform internal reference calibration on the camera to obtain the camera's distortion parameters and internal reference matrix

[0033]

[0034] Among them, [x, y] are the coordinates of the normalized plane point, [x distorted ,y distorted ] is the distorted coordinate, k 1 , k 2 , k 3 ,p 1 ,p 2 is the distortion term;

[0035]

[0036] P is the internal reference matrix of the camera, where f is the focal length of the camera, [O x , O y ] is the main optical axis point;

[0037] 2) Use Single Shot MultiBox Detector (SSD) and Convolutional NeuralNetwork (CNN) to build a deep learning network, train a deep learning model, and complete object recognition and object pose prediction tasks;

[...

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Abstract

A semantic SLAM object association method based on a hierarchical topic model, using a deep learning model to detect objects in key frames and predict their poses, when processing objects in each frame, use the Gibbs sampling method to sample according to the principle of overlapping views A set of real environment objects with potentially associated objects is calculated for each object in the current frame according to the object association method, and whether it is associated is judged according to the maximum posterior probability. Construct a factor graph for objects, cameras, and map points, and use observations between them as edges to optimize object poses, camera poses, and map point locations. Finally, a complete semantic map containing object information and camera trajectory is constructed. The invention can realize object association with high precision and avoid redundant object association; it can promote the camera pose estimation of semantic SLAM, and the optimized object pose can make object association more accurate, thereby constructing a more accurate semantic map.

Description

technical field [0001] The invention relates to technical fields such as robot vision, deep learning, and statistics, and specifically relates to a semantic SLAM object association method based on a hierarchical topic model. Background technique [0002] Simultaneous localization and mapping (SLAM) is an important issue in robot applications, such as autonomous driving, autonomous navigation and other fields. Constructing an accurate environmental map is a specific form of SLAM. Traditional SLAM technology relies on low-level geometric features, such as points, lines, and surfaces. This technology is prone to failure in open or repetitive texture environments. Semantic SLAM utilizes the advanced semantic information in the environment, which can effectively make up for the shortcomings of traditional SLAM, and can create a readable and more application-worthy semantic map. [0003] Object association and object pose optimization are two crucial components in semantic SLAM. ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/73G06T7/80G06K9/00
CPCG06T7/73G06T7/80G06T2207/10004G06T2207/20081G06T2207/20084G06T2207/30244G06V20/10
Inventor 张剑华贵梦萍王其超刘儒瑜徐浚哲陈胜勇
Owner ZHEJIANG UNIV OF TECH
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