移動機器人在稀疏環境中之同時定位與建圖

Mobile Robot SLAM in Sparse Environment

陳建華(Jian-Hua Chen)

指導教授 ： 林繼耀

國立暨南國際大學/科技學院/電機工程學系/碩士(2014年)

https://doi.org/10.6837/NCNU.2014.00190

摘要

此論文研究移動式機器人在稀疏環境中同時定位與建造地圖，採用分佈式擴增型卡爾曼濾波之粒子濾波器估測機器人位置及周遭環境特徵並建立地圖。在目前FastSLAM算法是基於把聯合SLAM狀態分成運動部分和地圖部分以縮小採樣空間，首先先估測機器人自己位置，利用估測到的機器人位置估測環境特徵，在每顆粒子中只提供一組估測資訊，每個擴增型卡爾曼濾波器獨自估測一個環境特徵，所以位置估測準確性顯得相當重要，因為如果當機器人處在的環境是稀疏的，機器人將無法獲得足夠的資訊以判斷出自己正確的位置，換言之這代表著位置估測誤差會相當的大，所以導致位置估測部分不如預期，進而影響其地圖之建造。
因此，為確保機器人位置之估測準確性，提出改善之演算法，每顆粒子採樣機器人之樣本位置，並將機器人位置之估測包含在每個擴增型卡爾曼濾波器，通過使用分佈式卡爾曼濾波器的想法，融合位置資訊並將其回授到每個擴增型卡爾曼濾波器，此外，基於線之特徵與數據關聯建構地圖。最後，透過模擬驗證所提出之演算法可行性與其性能。

關鍵字

同時定位與建造地圖 ； 稀疏環境 ； FastSLAM ； 分佈式卡爾曼濾波器

並列摘要

This thesis presents a method for solving simultaneous localization and mapping (SLAM) in sparse-feature environment, by adopting a concept of particle filter with
multiple extended Kalman filters (EKF). Compared with the common FastSLAM where in each particle is a sample of one vehicle path whereas each EKF is solely a feature estimator, the proposed algorithm includes the vehicle-pose estimate in each EKF whereas the particle is a sample of vehicle motion. Particularly, we use the concept of the distributed EKF to realize of fusion feedback information in order to improve vehicle pose estimation. Thus, the proposed algorithm ensures dead reckoning in the absence of features. Map construction is based on line features which are extracted from observation of the environment. Finally, simulation results demonstrate the feasibility and performance of the proposed SLAM algorithm.

並列關鍵字

simultaneous localization and map (SLAM) ； sparse environment ； FastSLAM ； distributed Kalman filter

參考文獻

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