TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Robustifying correspondence based 6D object pose estimation

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoICRA 2017 - IEEE International Conference on Robotics and Automation
KustantajaIEEE
Sivut739-745
Sivumäärä7
ISBN (elektroninen)9781509046331
DOI - pysyväislinkit
TilaJulkaistu - 21 heinäkuuta 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION -
Kesto: 1 tammikuuta 19001 tammikuuta 2000

Conference

ConferenceIEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
Ajanjakso1/01/001/01/00

Tiivistelmä

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.