A Probabilistic Framework for Multi-Sensor, Multi-Detector Localization Systems: Application to Vehicle Guidance

One of the major current developments in outdoor robotic aims at providing vehicles with automatic guidance capabilities. Such systems need a localization module to work. However, indoor localization methods are not directly usable in outdoor due to noise and the dynamic aspect of these environments. In this paper, we propose an original active localization system relying on sensors fusion able to supply an accurate position with a high confidence level. The main contributions of this work are: (1) The introduction of the "perceptive triplet" notion that associates landmarks, sensors and detectors to supervise the detections. (2) The use of a supervisor that determines at each time which landmark, with which sensor and detector, should be used to detect this landmark in order to improve the localization. The supervisor constitutes the intelligent part of this localization system. It decides when it\´s necessary to detect a landmark. (3) The integration of a confidence level over the vehicle\´s pose estimation that permits to take wrong matching hypothesis into account. Our system was tested in an outdoor environment, where it succeeded in accurately localizing the vehicle during automatic guidance.