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Scaling Up Real-time Object Pose Tracking to Multiple Objects and Active Cameras
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-3731-0582
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-2965-2953
2015 (English)In: IEEE International Conference on Robotics and Automation: Workshop on Scaling Up Active Perception, 2015Conference paper, Oral presentation only (Refereed)
Abstract [en]

We present an overview of our recent work on real-time model-based object pose estimation. We have developed an approach that can simultaneously track the pose of a large number of objects using multiple active cameras. It combines dense motion and depth cues with proprioceptive information to maintain a 3D simulated model of the objects in the scene and the robot operating on them. A constrained optimization method allows for an efficient fusion of the multiple dense cues obtained from each camera into this scene representation. This work is publicly available as a ROS software module for real-time object pose estimation called SimTrack.

Place, publisher, year, edition, pages
2015.
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-165634OAI: oai:DiVA.org:kth-165634DiVA: diva2:808736
Conference
IEEE International Conference on Robotics and Automation
Note

NQC 2015

Available from: 2015-04-29 Created: 2015-04-29 Last updated: 2015-04-30Bibliographically approved

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Pauwels, KarlKragic, Danica

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