Browse > Article
http://dx.doi.org/10.3745/KIPSTA.2010.17A.2.053

Autonomous Cooperative Localization of Mobile Sensors  

Song, Ha-Yoon (홍익대학교 컴퓨터공학과)
Publication Information
The KIPS Transactions:PartA / v.17A, no.2, 2010 , pp. 53-62 More about this Journal
Abstract
Mobile Sensor Vehicles, nodes of Mobile Sensor Network, are navigating for a specific, maybe unknown, region. For the precise usage of MSN, MSV has to be able to do localization by integrating information through communication by each other. In addition, MSV should be localized with various sensors equipped. In this research, we propose a set of techniques that improve accuracy using human mimic by combining and exploiting the existing techniques such as Dead-Reckoning, Computer Vision and Received Signal Strength Identification.
Keywords
Localization; Computer Vision; Mobile Sensor Network; Dead-Reckoning; RSSI;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J. Borenstein and L. Feng, “A Method for Measuring, Comparing, and Correcting Dead-Reckoning Errors in MobileRobots,” December, 1994.
2 Sina-park, “odometry Error Correction with a gyro sensor for the mobile robot localization,” December, 2005.
3 Jae Young Park, Ha Yoon Song, “Multilevel Localization for Mobile Sensor Network Platforms,” First International Symposium on Multimedia-Real Time Software(RTS'08), October, 2008.
4 Jae Young Park, Ha Yoon Song, “모바일 센서 차량의 하드웨어 설계”, KISS Korea Computer Congress, 2007.
5 J.-S. Gutmann, C. Schlege, AMOS, “Comparison of Scan Matching approaches for self-Localization in indoor Environments,” EUROBOT, 1996.
6 M. A. Fischler, R. C. Bolles. “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Comm. of the ACM, Vol.24, pp.381-395, 1981.   DOI   ScienceOn
7 Kwan Sik Cho, Ha Yoon Song, Jun Park, “Accurate Localization in Short Distance based on Computer Vision for Mobile Sensors,” IMCSIT, 2008.
8 J. Borenstein, L. Feng, Professors D. K. Wehe, Y. Koren, Grad Students,Z. Fan, B. Holt, B. Costanza, “UMBmark - A Method for Measuring, Comparing, and Correcting Dead-reckoning Errors in Mobile Robots,” 1995.
9 Haralick, R.M and Lee, D and Ottenburg, K and Nolle, M, “Analysis and Solutions of The Three Point Perspective Pose Estimation Problem,” IEEE Computer Vision and Pattern Recognition Computer Society Conference on, 592-598, June, 1991.
10 조관식, 송하윤, 박준, “컴퓨터 비전 기반 근거리 지역화의 최적조건”, 정보과학회, 2008.
11 S. Thrun, “Learning Maps for Indoor Mobile Robot Navigation,” Artificial Intelligence, 1999.
12 B. Kuipers and Y.-T.Byun, “A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations,” Journal of Robotics and Autonomous Systems, 1991.
13 J.-S. Gutmann, W. Burgard, D. Fox, K. Konolige, “An Experimental Comparion of Localization Method,” IROS, 1998.
14 Xiao Shan Gao and Xiao‐Roing Hou and Jianliang Tang and Hang‐Fei Cheng, “Complete Solution Classification for the Perspective Three Point Problem,” IEEE TRANSACTIONS ONPATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol.25, August, 2003.   DOI   ScienceOn