Adaptive sensor models
J.W.M. van Dam, Ben Kröse, F.C.A. Groen · 2002
In this paper we consider the conversion of sensor data to a probabilistic representation of the environment (occupancy grid). We introduce a neural network which learns these conversions. The conversion of sensor data remains adaptive to changes in either the sensor or its environment. To place this work in a broader context we describe the architecture of our sensor data fusion system in which these conversions are applied. We also introduce the PDOP: a rule for fusing occupancy grids in this system.