Improving object orientation estimates by considering multiple viewpoints

This article describes a probabilistic approach for improving the accuracy of general object pose estimation algorithms. We propose a histogram filter variant that uses the exploration capabilities of robots, and supports active perception through a next-best-view proposal algorithm. For the histogram-based fusion method we focus on the orientation of the 6 degrees of freedom (DoF) pose, since the position can be processed with common filtering techniques. The detected orientations of the object, estimated with a pose estimator, are used to update the hypothesis of its actual orientation. We discuss the design of experiments to estimate the error model of a detection method, and describe a suitable representation of the orientation histograms. This allows us to consider priors about likely object poses or symmetries, and use information gain measures for view selection. The method is validated and compared to alternatives, based on the outputs of different 6 DoF pose estimators, using real-world depth images acquired using different sensors, and on a large synthetic dataset