最近這幾年來，由於無線感測網路及相機的盛行，影響了室內定位的發展。無線感測網路使用無線訊號接收，在室內環境會有許多影響訊號的問題，因為會造成訊號接收的不穩定，導致使用無線信號強度的室內定位的結果變差。數位相機及手機相機是使用了影像裡的資訊去辨識附近環境，以至於定位，效果比接收訊號定位來的好，但影像容易受環境光線狀況的影響，故本論文將其兩種方法做結合，以改善並增加室內定位的精確度。
　　本論文的重點分為兩部份，一部分是使用同時定位與環境地圖建置技術去解出訊號強度定位的問題，另一部分是將訊號強度定位和同時定位與環境地圖建置技術做結合，分別是用遞迴式最小平方法及擴展式卡爾曼濾波器去模擬及觀察其定位效果。
　　經模擬結果之後，發現使用遞迴式最小平方法的定位效果結果不如預期，反而變的不理想，而使用擴展式卡爾曼濾波器的定位效果比較好，於是針對擴展式卡爾曼濾波器的模擬結果再去增加基地台及特徵點，發現增加特徵點之方法有效增加定位的精準度。

Due to wireless sensor network and camera become popular, affecting the development of indoor position in recent years. Wireless sensor network received wireless signal, but it has many problems which can effect of signals when indoor. Because it make unstable when signals receive, the results which use RSSI of indoor position became worse. Digital cameras and mobile cameras used information of images to identify surrounding environment and position, the effect is better than RSSI, but the images susceptible to the impact of ambient light conditions, so this thesis will combine with two ways to improve and increase the accuracy of indoor positioning.
Two important parts of this thesis: one is using SLAM to solve the problems of RSSI and the other is combining with RSSI and SLAM use RLS and EKF respectively, and simulation the result of position.
After simulation, the results of EKF is better than RLS’, so choose the results of EKF and add base stations and features. Finally adding features will increase the accuracy of positioning.

[1] A.H Sayed, A. Tarightat, and N. Khajehnouri, ”Network-based
wireless location,” IEEE Signal Processing Magazine, pp 24-40, July
2005
[2] R.G. Brown and P. Y. C Hwang, “Introduction to Random Signals
and Applied Kalman Filtering,” John Wiley&Sons, New York,
1997
[3] D. Simon, “Optimal State Estimation, Kalman, H∞, and Nonlinear
Approaches,” John Wiley & Sons, 2006
[4] Wei-Wen Kao and Shih-Hong Lin, ”Dead-reckoning Aided RSSI
Based Position System for Dynamic Indoor Environments”,
National Taiwan University of Science and Technology, Taiwan
[5] Javier Civera, Andrew J .Davison, and J.M Martinez
Montiel, ”Inverse Depth Parametrization for Monocular SLAM”,
IEEE transactions on robotics, vol.24, No5, October 2008
[6] H. Durrant-Whyte and T. Bailey, “Simultaneous Localization and
mapping (SLAM): Part I the Essential Algorithms,” Robotics and
Automation Magazine, pp. 99-110, June, 2006.
[7] T. Bailey and H. Durrant-Whyte, “Simultaneous Localisation and
Mapping (SLAM): Part II State of the Art,” Robotics and Automation
Magazine, pp. 108-117, September, 2006.
[8] M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, “FastSLAM: A
Factored Solution to Simultaneous Mapping And Localization
Problem,” Proc. 18th Natl. Conf. on Artificial Intelligence, pp.
593-598,2002
[9] S. Se, D. Lowe, and J. Little, “Mobile Robot Localization and
Mapping With Uncertainty Using Scale-Invariant Visual
Landmarks,” Int. J. of Robotics Research, Vol. 21, No. 8, pp.
735-758, August 2002.
[10] A. J. Davison and D. W. Murray,“ Simultaneous Localisation and
Map-Building Using Active Vision,” IEEE Trans. on Pattern
Analysis and Machine Intelligence, pp. 865-880, July 2002.

[11] A. J. Davison, “Real-time Simultaneous Mapping And Localization
with a Single Camera,” Pro. International Conference on Computer
Vision, Nice, October 2003.
[12] T. Bailey, “Constrained Initialization for Bearing Only SLAM,”
IEEE Int. Conference on Robotics and Automation, ICRA, Taipei,
Taiwan, pp. 1966-1971, 2005.
[13] M. Bryson and S. Sukkarich, “Bearing Only SLAM for an Airborne
Vehicle,” Australian Conference on Robotics and Automation,
ACRA, Sydney, Australia, 2005.
[14] A. J. Davison, I. D. Reid, N. D. Molton and O. Stasse,
“MonoSLAM: Real-Time Single Camera SLAM,” IEEE Trans. on
Pattern Analysis and Machine Intelligence, Vol. 29, No. 6,
pp.1052-1067, June 2007.
[15] J. Sola, A. Monin, M. Devy and T. Lemaire, ”Undelayed
Initialization in Bearing Only SLAM,” Proc. IEEE/RSJ Conference
on Intelligent Robots and Systems, pp. 2499-2504, 2005.
[16] J. M. M Montiel, J. Civera, and A. J. Davison, “Unified Inverse
Depth Parametrization for Monocular SLAM,” Robotics Science
and Systems, RSS, Philadelphia, 2006.
[17] P. Pinies, T. Lupton, S. Sukkarieh, and J. Tardos, “Inertial Aiding of
Inverse Depth SLAM using a Monocular Camera,” IEEE Int.
Conference on Robotics and Automation, ICRA, 2007.
[18] S. Sukkarieh, E. M. Nebot, and H. Durrant-White, “A High Integrity
IMU/GPS Navigation Loop for Autonomous Land Vehicle
Applications,” IEEE Trans. Robot. Automation, vol. 15, pp. 572-578,
September 1999.
[19] 倪育德教授 導航原理與系統 PPT，中國民航大學
[20] 葛定寰，非線性估測器於動態式內定位應用，國立台灣科技大
學機械工程研究所碩士論文，2010
[21] 林師泓，雷射測距儀輔助無線感測器網路於動態室內之應用，
國立台灣科技大學機械工程研究所碩士論文，2010
[22] 李建斌、張復閔，RFID室內定位挑戰重重 結合LBS可望提供
個人化導航服務，新通訊 2006 年 8 月號 66 期
[23] 王衍凱，以擴展型卡爾曼濾波器為基礎之感應馬達無感測器控
制及定子與轉子阻抗估測，國立台灣科技大學機械工程研究所碩
士論文，2010
[24] 邱永昌，相對位移感測器輔助室內無限定位系統，國立台灣科
技大學機械工程研究所碩士論文，2004。
[25] 阮堯輝，使用單一相機全向轮機器人之同時定位建圖與局部觻
徑規劃研究，碩士論文，國立中興大學機械工程研究所，2008。
[26] 張弘毅，整合GPS與MEMS感測器於自行車導航系統之應用國
立台灣科技大學機械工程技術研究所，2010。
[27] Wang Li-li，Liu Rong ，基於圖像的幾何三維重建方法，Vol. 13
Suppl. 2001。
[28] 侯雲，易衛東，建築物三維場景重建方法，Vol.36 No.4 February
2010 Computer Engineering.
[29] 鄧非，張祖勳，張劍清，基於序列影像的城市三維建模， Vol.33
No.8 April 2007 Computer Engineering.