Abstract
A method for estimating object pose and position is proposed. This method is based on integration of data obtained using several kinds of sensors, such as spotting rangefinders, scanning rangefinders, and cameras. Integration of data obtained by such different kinds of sensors is generally difficult because there are various measurement methods and they cause difference of the accuracies.
Defined here are local shape models for each of the sensors and equations that express the distance between the sensed data and models. Based on the accuracy of each sensor, these equations are integrated as a cost function that is minimized to estimate the optimal object pose and position. The configuration of the sensors is modified according to the environment and the degree of accuracy required, allowing flexible and robust object location. The feasibility of the proposed method is shown by simulation and experiments.
