應用 SLAM 自走機器人自動建置場域 Google 街景地圖

陳世軒

doi:10.6342/NTU201704016

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應用 SLAM 自走機器人自動建置場域 Google 街景地圖

Application of SLAM-based Autonomous Robot for Building Custom Field Street View in Google Maps

陳世軒 , Masters　　Advisor：林達德　　

繁體中文 DOI： 10.6342/NTU201704016

即時定位與地圖建構；最近疊代點；場景重建；場景接合；立體視覺；雷達感測器；感測器融合；障礙物偵測；避障系統； Simultaneous localization and mapping (SLAM) ； iterative closest point (ICP) ； scene reconstruction ； stereo vision ； radar ； sensor fusion ； obstacle detection ； obstacle avoidance

Abstract │ Reference (43) │ Altmetrics

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This study is dedicated to the development of an automated street shooting system. In order to achieve this purpose, the system is equipped with a number of different sensors with different usage. In the field of robot vision, SLAM (Simultaneous localization and mapping) is a technology be used to explore the unknown environment, can estimate the information of the environment and the robot's location through repetitive observation and controlled information. Stereo vision system, which is composed of two cameras, can calculate the spatial information of the image through image processing and stereo vision theory. The disadvantage is that the accuracy of the depth of the distance calculation is easily affected by the environment such as light source, image quality, and distance. Therefore, we add the radar, which measure the distance between the sensor and the object and other related information through the Doppler effect principle. To improve the overall reliability of the measurement through the stereo vision and radar sensor using the sensor fusion approach. The results obtained by the sensor fusion can provide obstacle detection, obstacle tracking, and even provide the system to do A * path planning algorithm to dodge them. The robot will have different degrees of error because of the different quality of the road or the tire slip caused during the movement. In order to fix this, this study use SLAM to provide the location of robot to gain PID control feedback signal, which can give the robot the ability to correct the route in the process of moving indoor and outdoor. The results of the experiment get the average offset of 0.084 m and 0.125m.GPS can locate the current position through satellite, with an error from a few meters to tens of meters due to the environment. This study combines SLAM with the selection of feature points to calculate the actual location of the shooting, and the accuracy compared with the information recorded by GPS is greatly improved. This study can automatically navigate and capture the scene information on the general road or in a specific field. After the GPS information of the image is corrected by SLAM, the 360 ° panoramic image can be uploaded to the open cloud platform to let the worldwide users view local scene information or store them in a private back-end database, which make it easier for users to view and record environmental information at the time.

Reference ( 43 ) 〈TOP〉

劉昶志。2011。基於擴展式卡爾曼濾波器織田間機器人即時同步定位與地圖建構演算法。碩士論文。臺北：國立臺灣大學生物產業機電工程學研究所。
連結：
賴宗誠。2012。應用多組雙眼攝影機系統進行車前三維環境模型重建。碩士論文。臺北：國立臺灣大學生物產業機電工程學研究所。
連結：
莊凱強。2013。基於立體視覺之即時障礙物追蹤與避障方法。碩士論文。臺北：國立臺灣大學生物產業機電工程學研究所。
連結：
李榕修。2015。基於SLAM自動導航及色彩點雲演算法之大尺度場景重建方法。碩士論文。臺北：國立臺灣大學生物產業機電工程學研究所。
連結：
翁立剛。2015。立體視覺與雷達感測器融合系統於車輛避障之應用。碩士論文。臺北：國立臺灣大學生物產業機電工程學研究所。
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