感測器融合運用於移動式機器人之室內同步定位與建地圖

Mobile Robot Indoor SLAM by Using Sensor Fusion

張嘉鑑(Chia-Chien Chang)

指導教授 ： 陸冠群

大同大學/工程學院/機械工程學系所/碩士(2009年)

摘要

自主式移動機器人必須具備認知周遭環境、行動規劃及導航的能力，要具備此種能力，則機器人必須能夠在未知環境中同時定位及建構地圖。本論文使用單眼視覺以及里程計，進行移動機器人同步定位與建立地圖的相關研究，並以實驗驗證成效。
首先處理影像以擷取環境垂直方向物的邊緣特徵，並定義相關座標系，使三維環境降為二維，簡化運算時間，接著運用里程計計算機器人移動軌跡，建立機器人運動模型，本論文採用擴展卡爾曼濾波器消除感測器累計誤差及雜訊影響，對機器人狀態與特徵狀態進行估測以及更新，藉由環境中同一特徵於連續影像畫面中的匹配，以達到定位的目的，此外，本研究以雷射感測器同時繪製未知環境的地圖。

關鍵字

里程計 ； 同步定位與建地圖 ； 機器視覺

並列摘要

An autonomous mobile robot must possess capabilities of cognition, motion planning and navigation. Consequently, a robot has to localize its position and build map simultaneously in unknown environments. In this thesis, we study SLAM (simultaneously localization and mapping) of mobile robot employing monocular vision and odometer. Moreover, experiments were applied to evaluate the performance of the proposed mechanism.
First the vision picture was processed to derive the objects’ vertical edge landmark. The relative coordinates were than defined and reduced from three-dimension to two-dimension to simply computation. Consequently, the robot’s trajectories derived using the data of odometer were applied to build the robot’s dynamic model. In this study, the extended Kalman filter was employed to eliminate the accumulated error from odometer measurement and noise. The states of mobile robot and landmarks were estimated and updated continuously. The localization of mobile robot was achieved through the matching of landmarks. Moreover, laser range finder was utilized to draw the map of unknown environment.

並列關鍵字

SLAM ； single-camera ； odometer

參考文獻

[1] Hugh Durrant-Whyte and Tim Bailey, “Simultaneous Localization and Mapping”, IEEE Robotics & Automation Magazing, pp. 99-117, 2006.

[2] Leonard&Newman, “Consistent, Convergent, and Constant-Time SLAM”, IJCAI, 2003.

[3] Castellanos, J. A., Neira, J., and Tardós, J. D., ”Multisensor fusion for simultaneous localization and map building”, IEEE Trans on Robotics and Automation 17, pp. 908-914, 2001.

[4] L. Kleeman, “Advanced Sonar and Odometry Error Modeling for Simultaneous Localisation and Map Building”, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas 2003, pp. 699-704.

[5] Sheng Fu, Hui-ying Liu, Lu-fang Gao and Yu-xian Gai, ”SLAM for Mobile Robots Using Lasar Range Finder and Monocular Vision”, School of Automobile Engineering Harbin Institute of Technology Weihai, Shandong Province 264209, China.