A fast pipeline for textured object recognition in clutter using an RGB-D sensor

Publication Type:
Conference Proceeding
2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, 2014, pp. 1650 - 1655
Issue Date:
Filename Description Size
ThumbnailFr37.1-P0193.pdf Published version290.82 kB
Adobe PDF
Full metadata record
© 2014 IEEE. This paper presents a modular algorithm pipeline for recognizing textured household objects in cluttered environment and estimating 6 DOF poses using an RGB-D sensor. The method draws from recent advances in this area and introduces a number of innovations that enable improved performances and faster operational speed in comparison with the state-of-the-art. The pipeline consists of (i) support plane subtraction (ii) SIFT feature extraction and approximate nearest neighbour based matching (iii) feature clustering using 3D Eculidean distances (iv) SVD based pose estimation in combination with a outlier rejection strategy named SORSAC (Spatially ORdered RAndom Consensus) and (v) a pose combination and refinement step to combine overlapping identical instances and to refine the pose estimation result by removing incorrect hypothesis. Quantitative comparisons with the MOPED [1] system on self-constructed dataset are presented to demonstrate the effectiveness of the pipeline.
Please use this identifier to cite or link to this item: