With the development and progress of science and technology, mobile robots gradually play a more important role in industrial production and daily life. Choosing an appropriate map model according to the characteristics of the environment and realizing autonomous positioning are the basis for a mobile robot to conduct reliable navigation in a large-scale environment, and it is more convenient for a robot to observe and measure the surrounding environment through an active vision system, which is significant in both theoretical research and real-world applications. This thesis focuses on map construction and navigation of mobile robots with large-scale environments, and studies the key techniques of active vision measurement and map building of environments. A series of studies have been carried out on the calibration and 3D measurement method of active vision system with symmetric yawing cameras, simultaneous localization and mapping of monocular cameras with points and lines, and hierarchical map for the outdoor environments based on HOG features. The main works and contributions of this thesis are summarized as follows:
(2) It is difficult for a monocular vision system to obtain the depth information of objects, which results in the failure of 3D measurement. A motion-based measurement method is presented for a monocular vision system consisting of a yawing camera, based on the proposed active vision system with symmetric yawing cameras and the moving platform. The depth of the object is estimated with the motion increment and the image features’ variation when the camera translates with the moving platform. Then the object's X and Y coordinates are computed with the depth and its imaging coordinates. In the two typical cases of the front view and the side view, the errors caused by different variables are analyzed. The experimental results verify the accuracy and effectiveness of the proposed measurement method.
(3) A visual SLAM method based on point and line features is proposed to solve the low robustness of the visual SLAM method based on point features in the environment with low texture and changing illumination. The proposed VSLAM method is on the basis of ORB-SLAM2 and line features are combined with it. Plücker coordinates are used for spatial line’s coordinate transformation and line feature projection, and orthonormal representation is used for optimization. The degenerate motions of the camera during line triangulation are analyzed using Plücker coordinates, and a novel residual term using vanishing points is proposed for optimization to make the line’s direction vector of Plücker coordinates corrected sufficiently and reduce the influence of the degeneracy problem on mapping accuracy. Experimental results show that the proposed method enhances the robustness of the system, and improves the accuracy of pose estimation and 3D map.
(4) In order to make full use of the pre-known knowledge of the environment and to enable mobile robots to realize autonomous navigation and localization in large-scale outdoor environments, a novel hierarchical outdoor map constructed offline based on the pre-known knowledge of the environment is proposed. It consists of a topological map, a simple global metric map, a semantic map, and a local metric map, which can be constructed offline using the proposed active vision system with symmetric yawing cameras. A node recognition method based on the common-viewing relationship of the node’s surrounding objects, a line-segment-based road perception method, and a vanishing point-based navigation control method are proposed. Using the different part of the map can realize path planning, node recognition, and relative pose estimation, enabling mobile robots to achieve autonomous navigation and positioning in large-scale outdoor environments. In the outdoor environment experiment, the robot uses the above method to achieve navigation and move to the destination, which verifies the effectiveness of the method.
Keywords: Mobile robot, Active vision, 3D measurement, VSLAM, Hierarchical map
|Keyword||移动机器人 主动视觉 三维测量 VSLAM 多层次地图|
|Sub direction classification||智能机器人|
|planning direction of the national heavy laboratory||视觉信息处理|
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