Scaling Up Real-time Object Pose Tracking to Multiple Objects and Active Cameras
2015 (English)In: IEEE International Conference on Robotics and Automation: Workshop on Scaling Up Active Perception, 2015Conference paper, Presentation (Refereed)
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
IdentifiersURN: urn:nbn:se:kth:diva-165634OAI: oai:DiVA.org:kth-165634DiVA: diva2:808736
IEEE International Conference on Robotics and Automation
NQC 20152015-04-292015-04-292015-04-30Bibliographically approved