Vision Based Navigation System of Autonomous Mobile Robot. Planning of Landmark Sensing Considering Environmental Conditions.

We propose the Planning of Landmark Sensing (PLAS) considering environmental conditions for autonomous mobile robots.When a robot moves, the robot usually resets accumulated position errors by sensing the landmark.In previous works, these errors are estimated based on robot models, and Kalman Filter is used to reset the errors after the landmark sensing.Based on this method, only the relation of the robot and the landmark position is considered to influence the observation noise of Kalman Filter.But in the actual visual landmark sensing, environmental conditions, like position of lighting and brightness of room, are likely to cause the misrecognition of landmark.Then we consider environmental conditions for the observation noise.This helps our system to recognize the possibility of the misrecognition in bad sensing environments.As a result of the consideration of the environmental conditions, our navigation system can navigate robots precisely, even if the environments are not suitable for the landmark sesning.

[1]  Chisato Kanamori,et al.  Self-Locating Measurement System for Mobile Robot Working in the Building Site , 1991, J. Robotics Mechatronics.

[2]  S. Ito,et al.  Navigation system based on ceiling landmark recognition for autonomous mobile robot , 1993, Proceedings of IECON '93 - 19th Annual Conference of IEEE Industrial Electronics.

[3]  Fumihito Arai,et al.  Navigation system based on ceiling landmark recognition for autonomous mobile robot-landmark detection based on fuzzy template matching (FTM) , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[4]  Luis Montano,et al.  Real-time robot navigation in unstructured environments using a 3D laser rangefinder , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.

[5]  Maja J. Mataric,et al.  Integration of representation into goal-driven behavior-based robots , 1992, IEEE Trans. Robotics Autom..

[6]  Kostas J. Kyriakopoulos,et al.  Sensor-based self-localization for wheeled mobile robots , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[7]  Alain Pruski,et al.  Dynamic modeling of an indoor environment , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[8]  I. Masaki.  Three dimensional vision system for intelligent vehicles , 1993, Proceedings of IECON '93 - 19th Annual Conference of IEEE Industrial Electronics.

[9]  Fumihito Arai,et al.  Development of Air Conditioning Equipment Inspection Robot for Automatic Measurement of Outlet Air Volume , 1996 .

[10]  Kazuo Tani,et al.  Planning of Landmark Measurement for the Navigation of a Mobile Robot , 1993 .

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

[12]  Volker Graefe,et al.  An approach to obstacle recognition for autonomous mobile robots , 1990, EEE International Workshop on Intelligent Robots and Systems, Towards a New Frontier of Applications.