Bayesian Structure Learning for the Locating of an Unknown Number of Static Objects
M. Verschure (TU Delft - Mechanical Engineering)
Martijn Wisse – Mentor (TU Delft - Robot Dynamics)
R. Sabzevari – Graduation committee member (TU Delft - Group Sabzevari)
A. Zgonnikov – Graduation committee member (TU Delft - Human-Robot Interaction)
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Abstract
This paper presents a method which is capable of creating an object centered world description based upon consecutive measurements about an unknown number of static objects using Bayesian inference. The objects are represented by a two dimensional position, with the aim of adding more attributes in future works. This objective is reformulated into a clustering problem which is then solved using a structure learning method. It is implemented using RxInfer which uses the message passing algorithm in combination with factor graphs to perform Bayesian inference. The results indicate a promising performance of the structure learning model, but also show signs that the object representation has been over simplified. The future works section provides guidance on how the model complexity can be increased by adding additional attributes in order to improve performance.