Local Image Descriptors for Full 6 Degree-of-Freedom Object Pose Estimation and Recognition
2010 (English)Article in journal (Refereed) Submitted
Recent years have seen advances in the estimation of full 6 degree-of-freedom object pose from a single 2D image. These advances have often been presented as a result of, or together with, a new local image feature type. This paper examines how the pose accuracy and recognition robustness for such a system varies with choice of feature type. This is done by evaluating a full 6 degree-of-freedom pose estimation system for 17 different combinations of local descriptors and detectors. The evaluation is done on data sets with photos of challenging 3D objects with simple and complex backgrounds and varying illumination conditions. We examine the performance of the system under varying levels of object occlusion and we find that many features allow considerable object occlusion. From the experiments we can conclude that duplet features, that use pairs of interest points, improve pose estimation accuracy, compared to single point features. Interestingly, we can also show that many features previously used for recognition and wide-baseline stereo are unsuitable for pose estimation, one notable example are the affine covariant features that have been proven quite successful in other applications. The data sets and their ground truths are available on the web to allow future comparison with novel algorithms.
Place, publisher, year, edition, pages
bin picking, pose estimation, local features
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:liu:diva-57330OAI: oai:DiVA.org:liu-57330DiVA: diva2:325003
This is an extension of http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-448942010-06-162010-06-162015-12-10