本研究是利用遠端遙控履帶車進行定位精確度的分析與討論，履帶車上整合霍爾感測器、ZED雙目相機、慣性量測元件以及RTK-GPS。因此履帶車共有四個定位系統: 馬達里程計、影像里程計、感測器融合以及RTK-GPS。感測器融合是利用擴展型卡爾曼濾波將馬達里程計、影像里程計與慣性量測元件進行融合。由於RTK-GPS具備穩定的公分級定位能力，因此在本研究中被視為定位之參考值。本研究有兩個討論主題: 一為可否經由履帶車運動學得到較佳的馬達里程計性能表現，二為感測器融合能否解決履帶車定位精確度不佳的問題。本研究是透過整合一台以Nvidia Jetson TX2與Arduino Mega2560作為控制與運算核心的履帶車，實驗場地設置在校園大樓之屋頂，透過所設計之實驗對履帶車之移動進行定位精確度的研究與討論。

This research investigate the problem of vehicle positioning accuracy based on a remote-control tracked vehicle. A Hall sensor, a ZED stereo camera, an inertial measurement unit and RTK-GPS are integrated on the tracked vehicle. The Nvidia Jetson TX2 and Arduino Mega 2560 are integrated as the control kernel of the tracked vehicle. Extended Kalman Filter was used to fuse the signals of motor odometry, visual odometry and inertial measurement unit. Since RTK-GPS has stable and centimeter-grade positioning capabilities, it is used as the reference for positioning system. Two main topics are discussed in this research. One is whether the motor odometry can get better performance by using tracked vehicle kinematics. The other is whether the sensor fusion can solve the problem of poor positioning accuracy for tracked vehicle system. Due to the GPS signal sheltering problem, the experiment site was chosen on the roof of the campus building. The motion positioning accuracy of tracked vehicle system is investigated based on designed experiments. The experiment result shows that the EKF can improve the positioning accuracy from 4.52m to 0.57m, respect to the motor odometry.

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