Scaling Up Real-time Object Pose Tracking to Multiple Objects and Active Cameras

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.