Recognising and locating objects with local sensors

This paper presents a recognition and location method for objects, composed of primitives with a simple analytical description, using local sensors such as an ultrasonic and an infra-red sensor. As these sensors only return local data, several measurements are needed to obtain global information. To allow effective exploration, on-line estimation is needed. Estimation is based on a constrained Kalman filter, the constraints defining relations between the primitives in the object model. Linearisation errors are reduced with a careful choice of the parametrisation of the primitives and by using a novel version of the constrained Kalman filter. Special features of the presented approach are the easy extendibility to objects composed of a different number and type of primitives, the fast convergence due to the effective use of geometric constraints, the ability to handle incomplete models, and the robustness to linearisation errors. The performance of the estimator has been thoroughly evaluated with simulations and experiments, showing that 10 measurements with an infrared sensor on the edges of a 800 × 550mm² rectangular plate are sufficient to locate a point on the plate with standard deviation σ = 3mm.