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Robustifying correspondence based 6D object pose estimation

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
Number of pages7
ISBN (Electronic)9781509046331
Publication statusPublished - 21 Jul 2017
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Robotics and Automation -
Duration: 1 Jan 19001 Jan 2000


ConferenceIEEE International Conference on Robotics and Automation


We propose two methods to robustify point correspondence based 6D object pose estimation. The first method, curvature filtering, is based on the assumption that low curvature regions provide false matches, and removing points in these regions improves robustness. The second method, region pruning, is more general by making no assumptions about local surface properties. Our region pruning segments a model point cloud into cluster regions and searches good region combinations using a validation set. The robustifying methods are general and can be used with any correspondence based method. For the experiments, we evaluated three correspondence selection methods, Geometric Consistency (GC) [1], Hough Grouping (HG) [2] and Search of Inliers (SI) [3] and report systematic improvements for their robustified versions with two distinct datasets.

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