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Improving object orientation estimates by considering multiple viewpoints

Orientation histograms of symmetries and measurement models for view selection

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Abstract

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.

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Notes

  1. http://docs.pointclouds.org/1.7.0/group__visualization.html.

  2. In our experiments the prior was always uniformly distributed, but it can be easily modified to any distribution the discretization allows to represent (which is much more flexible than a parametric model).

  3. We do not apply an additional uncertainty to the estimates after a viewpoint change, as we were using very precise sensor movements to obtain ground truth.

  4. https://github.com/PointCloudLibrary/pcl/tree/master/simulation.

  5. Please note that the box plot drawing library detects outliers based on interquartile range distance, marks them as dots, and excludes them from percentile calculations.

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Acknowledgements

The authors would like to thank Anas Al-Nuaimi for helpful discussions, and the help of Laura Beckmann with the real-world ground truth for the evaluation.

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Authors and Affiliations

  1. Institute of Robotics and Mechatronics (RM), DLR (German Aerospace Center), Muenchner Strasse 20, 82234, Oberpfaffenhofen-Wessling, Germany

    Zoltán Csaba Márton, Christian Rink, Manuel Brucker, Simon Kriegel, Tim Bodenmüller & Sebastian Riedel

  2. NavVis GmbH, Blutenburgstr. 18, 80636, Munich, Germany

    Serkan Türker

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  1. Zoltán Csaba Márton
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Correspondence to Zoltán Csaba Márton.

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This is one of several papers published in Autonomous Robots comprising the Special Issue on Active Perception.

This work has partly been supported by the EC, under contract number H2020-ICT-645403-ROBDREAM.

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Márton, Z.C., Türker, S., Rink, C. et al. Improving object orientation estimates by considering multiple viewpoints. Auton Robot 42, 423–442 (2018). https://doi.org/10.1007/s10514-017-9633-1

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