Probabilistic view planner for 3D modelling indoor environments

These days researchers are looking for design and development of autonomous robots. It is desirable that robots be capable to acquire the information they need to perform actions and make decisions. Any mobile or humanoid robot will need to construct a spatial representation or model of the surrounding environment that allows it to move and execute tasks with success. The reconstruction of an environment is an important and useful capability for these kind of robots. In order to construct a model, the robot needs to obtain information through a series of acquisitions from its sensors by solving occlusions. Therefore, an important issue is how to plan these robot placements (views) optimally, according to certain criteria for the purpose of reconstructing a complete model automatically. In this work we present a view planning algorithm to solve the problem of 3D modelling for indoor environments; the algorithm uses a volumetric representation as a reasoning domain. In this paper we propose the use of probability distribution functions as a model for the desirable behavior of the system, considering perception range data. The method uses a maximum a posteriori estimator to find the perception system parameters that defines the next best view position. We present results in simulation for a five degrees of freedom robot with a 3D range camera mounted on it to validate our approach.