Michael Krainin, Peter Henry, Xiaofeng Ren, Dieter Fox, and Brian Curless
We address the problem of active object investigation using robotic
manipulators and Kinect-style RGB-D depth sensors. To do so, we jointly tackle the issues
of sensor to robot calibration, manipulator tracking, and 3D object model construction.
We additionally consider the problem of motion and grasp planning to maximize coverage
of the object.
Due to the large end-effector errors experienced by our robot, we must perform articulated
pose estimation in conjunction with object modeling. We combine a novel variant of the
Articulated ICP algorithm with an elegant, Kalman filter-based framework for state estimation.
Shown in the video below are the tracking system on the left (model in red, sensor data
in true color), and the surfel-based reconstruction on the right.
Next Best View Planning
We developed a novel information-based variant of the next best view algorithm. We use
this for trajectory and grasp planning to maximize the coverage of the object when modeling.
The video below demonstrates the next best view procedure for a single grasp.
These first results are with a single grasp only and without next best view motion planning.
They have been converted to meshes using the Poisson Reconstruction algorithm.
(Click for videos of the meshed models)
Next are examples of object models before and after next best view planning. Red areas indicate low confidence regions, having few to no observations (Click for 3D ply models).
Shown below are 3 grasps of a box (with corresponding confidence models) as well
as the resulting final model.