We propose a the use of a consistent Bayesian methodology for the analysis of the uncertainty associated with a pose estimation procedure. A novel model-based technique to estimate the pose of rigid 3D objects from laser range finder images is studied, and various sources of uncertainty are carried through the process using a Bayesian MAP treatment, yielding local, point-by-point estimates of position and predicted error. Promising experimental results on complex objects are presented and discussed.

Describes a system for automatically digitizing the surfaces of a completely unknown object to a prescribed sampling density. Unlike many available commercial systems (e.g. Cyberware), our system analyzes actively the data and computes sensor trajectories to achieve complete surface coverage taking into account the limitations of the sensor/manipulator. This represents in fact an active approach to the problem of complete object digitization, working directly at the measurement level, as opposed to existing off-line methods that perform post-processing on a fixed data set. We propose a theoretical framework to achieve this goal and we present preliminary results of a laboratory implementation.

Recognition ambiguity, due to noisy measurements and uncertain object models, can be quantified and actively used by an autonomous agent to efficiently gather new data and improve its information about the environment. In this work an information-based utility measure is used to derive from a learned classification of shape models an efficient data collection strategy, specifically aimed at increasing classification confidence when recognizing uncertain shapes. Promising simulation results are presented and discussed.

Scene ambiguity, due to noisy measurements and uncertain object models, can be quantified and actively used by an autonomous agent to efficiently gather new data and improve its information about the environment. In this work an information-based utility measure is used to derive from a learned classification of shape models an efficient data collection strategy, specifically aimed at increasing classification confidence when recognizing uncertain shapes.

New robust techniques for the analysis of the first and second order discrete variational structure of images are introduced. Their application to parametric surface recovery from shading is presented. The theoretical groundings are described along with promising preliminary experimental results.