Three-dimensional (3D) object reconstruction is the process of building a 3D model of a real object. This task is performed by taking several scans of an object from different locations (views). Due to the limited field of view of the sensor and the object's self-occlusions, it is a difficult problem to solve. In addition, sensor positioning by robots is not perfect, making the actual view different from the expected one. We propose a next best view (NBV) algorithm that determines each view to reconstruct an arbitrary object. Furthermore, we propose a method to deal with the uncertainty in sensor positioning. The algorithm fulfills all the constraints of a reconstruction process, such as new information, positioning constraints, sensing constraints and registration constraints. Moreover, it improves the scan's quality and reduces the navigation distance. The algorithm is based on a search-based paradigm where a set of candidate views is generated and then each candidate view is evaluated to determine which one is the best. To deal with positioning uncertainty, we propose a second stage which re-evaluates the views according to their neighbours, such that the best view is that which is within a region of the good views. The results of simulation and comparisons with previous approaches are presented.

For manipulating an unknown object a robot needs a 3D model of it. Given the limited field of view of a camera and self occlusions, a set of views is required to build a complete 3D model, so an important problem is how to select these views optimally according to certain criteria. We propose a novel algorithm to select the next-best-view (NBV) for a range camera to model 3D arbitrary objects. We use a volumetric representation and voxel labeling. We propose a new utility function based on factors considering voxel, quality and navigation information. We also propose two novel strategies to make faster the search of the NBV, one based on a hierarchical decomposition of the search space and other based on a multi-resolution of ray tracing. We have tested our planner in simulation with 7 different 3D objects, showing good results in terms of quality of the models and computation time required, and at the same time reducing the distance that the sensor has to travel to obtain the set of views.

The future personal robot must be capable to work and take decisions in a dynamic human environment. One important capability of such a robot is manipulation. Grasp, as the beginning of any manipulation task is a key point. In this paper we present a grasp planner for manipulating real objects modeled by polyhedra. The algorithm is based on a random generation of grasp oriented by mass and inertial properties of the object. Recent techniques to filter and evaluate the quality of grasp are discussed.

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.

The aim of this paper is to tune and compare different optimization algorithms on the Next Best View (NBV) problem, which consists in finding the next position that the sensor or camera needs to take to scan an object or scenery in its totality. A simulated 5 Degree-of-Freedom (DOF) mobile robot with a mounted simulated range sensor was used on a Virtual Reality Modeling Language (VRML) environment, and the space discretization was made using a voxel map. For the objective function, two main factors were included: an area factor to make sure that the image taken by the sensor provides the best possible information, and a motion factor made up of distance and energy sub-factors to reduce the resources used by the robot. Tasks such as a backstepping technique to escape local minima and a dynamic change in the objective function were implemented. The retrievement of the scene was made on an iterative process, and three different optimization methods were tuned and tested: Nelder-Mead, an Evolution Strategy, and Simulated Annealing. A set of experiments comparing the three methods in computational time and retrievement efficiency were made on three different environments with increasing difficulty to test their repeatability, with them being a laboratory model, a room with a cube and a pyramid inside it, and a study room with multiple furniture and windows.