Self localization of an autonomous robot: using an EKF to merge odometry and vision based landmarks

Localization is essential to modern autonomous robots in order to enable effective completion of complex tasks over possibly large distances in low structured environments. In this paper, a extended Kalman filter is used in order to implement self-localization. This is done by merging odometry and localization information, when available. The used landmarks are colored poles that can be recognized while the robot moves around performing normal tasks. This paper models measurements with very different characteristics in distance and angle to markers and shows results of the self-localization method. Results of simulations and real robot tests are shown

[1]  Gaetano Borriello,et al.  SpotON: An Indoor 3D Location Sensing Technology Based on RF Signal Strength , 2000 .

[2]  Patric Jensfelt,et al.  Approaches to Mobile Robot Localization in Indoor Environments , 2001 .

[3]  Sinisa Segvic,et al.  Determining the absolute orientation in a corridor using projective geometry and active vision , 2001, IEEE Trans. Ind. Electron..

[4]  Armando Sousa,et al.  5dpo Team Description , 1999, RoboCup.

[5]  Fernando Figueroa,et al.  A Robust Navigation System for Autonomous Vehicles using Ultrasonics , 1993 .

[6]  Andy Hopper,et al.  The active badge location system , 1992, TOIS.

[7]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[8]  Michael Drumheller,et al.  Mobile Robot Localization Using Sonar , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Henrik I. Christensen,et al.  Localization and navigation of a mobile robot using natural point landmarks extracted from sonar data , 2000, Robotics Auton. Syst..

[10]  G. Meijer,et al.  Low-Cost Ultrasonic Fusion Sensor For Angular Position , 2000 .

[11]  Artur Arsenio,et al.  Absolute localization of mobile robots using natural landmarks , 1998, 1998 IEEE International Conference on Electronics, Circuits and Systems. Surfing the Waves of Science and Technology (Cat. No.98EX196).

[12]  M. I. Ribeiro,et al.  Natural landmark based localisation of mobile robots using laser range data , 1996, Proceedings of the First Euromicro Workshop on Advanced Mobile Robots (EUROBOT '96).

[13]  P. Costa,et al.  Localização em tempo real de múltiplos robots num ambiente dinâmico , 1999 .

[14]  Shin'ichi Yuta,et al.  A corridors lights based navigation system including path definition using a topologically corrected map for indoor mobile robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[15]  M. Isabel Ribeiro,et al.  Mobile robot localisation on reconstructed 3D models , 2000, Robotics Auton. Syst..

[16]  Henrik I. Christensen,et al.  Triangulation based fusion of ultrasonic sensor data , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[17]  Gregory D. Hager,et al.  Tracker fusion for robustness in visual feature tracking , 1995, Other Conferences.

[18]  Greg Welch,et al.  An Introduction to Kalman Filter , 1995, SIGGRAPH 2001.

[19]  Ching-Chih Tsai.  A localization system of a mobile robot by fusing dead-reckoning and ultrasonic measurements , 1998, IEEE Trans. Instrum. Meas..

[20]  Deborah Estrin,et al.  Robust range estimation using acoustic and multimodal sensing , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).