Vision-based Path Planning and Control of a Mobile Robot Based on DNN Object Recognition and ORB-SLAM2
碩士 === 國立交通大學 === 電控工程研究所 === 107 === This thesis proposes a path planning and control of a mobile robot based on Deep Neural Networks (DNN) object recognition and ORB-SLAM2. For the application of robotic vacuum cleaner, an embedded system with small size and low power consumption is required. An R...
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| Format: | Others |
| Language: | zh-TW |
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2019
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| Online Access: | http://ndltd.ncl.edu.tw/handle/fu76vv |
| Summary: | 碩士 === 國立交通大學 === 電控工程研究所 === 107 === This thesis proposes a path planning and control of a mobile robot based on Deep Neural Networks (DNN) object recognition and ORB-SLAM2. For the application of robotic vacuum cleaner, an embedded system with small size and low power consumption is required. An RGB-D camera is utilized to realize ORB-SLAM2 algorithm. The advantage is that it can be executed on a CPU-only embedded computing platform. This thesis proposes a ORB feature extraction algorithm with the SSE technology to reduce the computation time. Further, a feature matching method based on DNN object recognition is developed to reduce the computation time. By creating a 2D grid map based on the feature map from ORB-SLAM2 algorithm, the path planner can plan a zig zag path for a robot vacuum cleaner to complete the task more efficiently. This thesis developed the path following controller for the robot to navigate on the desired trajectory. Several interesting experiments validate the proposed method of vision-based path planning and control of a iRobot create mobile robot. The DNN-based object recognition algorithm was implemented in a Movidius Neural Compute Stick. It is shown that the computation time for feature matching is reduced to 45.16% of the original ORB-SLAM2. Positioning experimentation shows that the accuracy of robot localization for a square trajectory about 19.8m path is within 20mm error.
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