藉由雷射測距儀和疊代最近點演算法在未知靜態環境中對行動機器人的姿態估測

Robot Pose Estimation Using Laser Range Finder with Iterative Closest Point Algorithm in an Unknown Stationary Environment

鄞銘佐(Ming-Tzuoo Yin)

指導教授 ： 連豊力

國立臺灣大學/電機資訊學院/電機工程學研究所/碩士(2010年)

https://doi.org/10.6342/NTU.2010.01540

摘要

機器人在未知的靜態環境中進行導航，定位是一個非常重要的議題。運用最小方差法概念的疊代最近點(ICP)演算法是一個用來達成曲線疊合的利器。所以對於圖形疊合、定位以及地圖的建立來說，ICP演算法是一個非常實用的工具。原因為在利用圖形疊合解決相關的問題時，其將由車輛本身硬體架構以及外在環境所導致的誤差都一併予以考慮。
然而兩個比對圖形的差異與雷射測距儀本身存在的硬體條件，使得在現實環境中進行完善的圖形比對是完全不可能達成的事。本篇論文在將問題簡化的前提下做了三個簡單的演算法測試，而後針對常態運動提出了一個演算法。其為在圖形利用ICP演算法進行比對之前，先將進行比對之兩圖形找出在現實環境中可能的共同區塊。後續又提出了三個補強的方法，分別為增強整體演算法的準確度與健全度。而於實驗過程中所遇到的困難亦會在本篇論文討論。
論文中所採用的方法，都是希望能在室內靜態環境中存在各種障礙物時，使得ICP演算法在進行圖形比對時更加地順利與成功。

關鍵字

定位 ； 疊代最近點演算法 ； 外在環境誤差 ； 雷射測距儀 ； 姿態估測

並列摘要

Localization is an important issue for robot navigation in an unknown stationary environment. ICP (Iterative Closest Point) algorithm can be used to solve problem of curve registration by the concept of least square errors. It is a useful tool for mapping matching, localization, and map building because of its capability to eliminate systematic and nonsystematic errors of robot simultaneously.
However, differences in two mappings and restrictions from laser range finder make complete mapping matching impossible in actual situation. In this thesis, a method is proposed to find common parts of the original mappings called hybrid potential common parts before using ICP algorithm. Then, there are three methods to promote accuracy and robustness of overall algorithm. The experimental difficulties in reality are mentioned in addition.
The goal of above discussed methods is to assist ICP algorithm to match maps more successfully and overcome different types of obstacles in an indoor stationary environment.

並列關鍵字

Localization ； ICP algorithm ； nonsystematic errors ； laser range finder ； pose estimation

參考文獻

[1: Chatila & Laumond 1985]

H. F. Durrant-Whyte, “Consistent integration and propagation of disparate sensor observations,” Proceedings of IEEE International Conference on Robotics and Automation, San Francisco, USA, Vol. 3, pp. 1464-1469, Apr. 1986.

[3: Chen & Medioni 1991]

[4: Besl & McKay 1992]

P. J. Besl and N. D. McKay, “A Method for Registration of 3-D Shapes,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 2 pp. 239-256, Feb. 1992.