Title :
Sensor-based self-localization for wheeled mobile robots
Author :
Curran, A. ; Kyriakopoulos, K.J.
Author_Institution :
Rensselaer Polytech. Inst. Troy, NY, USA
Abstract :
A reliable and robust algorithm for localizing a mobile robot in an indoor environment that is relatively consistent with an a priori map is demonstrated. The algorithm uses an extended Kalman filter that combines dead-reckoning, ultrasonic, and infrared sensor data to estimate current position and orientation. Through a thresholding approach, unexpected obstacles can be detected. Experimental results from implementation in a mobile robot, Nomad-200, are presented
Keywords :
Kalman filters; filtering and prediction theory; mobile robots; sensor fusion; Nomad-200; a priori map; dead-reckoning; extended Kalman filter; indoor environment; infrared sensor data; obstacle detection; orientation estimation; position estimation; sensor-based self-localization; thresholding; ultrasonic data; wheeled mobile robots; Costs; Indoor environments; Infrared sensors; Mobile robots; Optical filters; Optical sensors; Recursive estimation; Robot sensing systems; Robotics and automation; Robustness;
Conference_Titel :
Robotics and Automation, 1993. Proceedings., 1993 IEEE International Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
0-8186-3450-2
DOI :
10.1109/ROBOT.1993.291954
