3 accepted papers
For robots to operate robustly in the real world, they should be aware of their uncertainty. However, most methods for object pose estimation return a single point estimate of the object's pose. In this work, we propose two learned methods for estimating a distribution over an object's orientation.…
We describe a surgical system that autonomously searches for tumors and dynamically displays a computer graphic model of them super-imposed on the organ (or in our case, phantom). Once localized, the phantom is tracked in real time and augmented with overlaid stiffness information in 3D. We believe…
Pose estimation is central to several robotics applications such as registration, hand-eye calibration, SLAM, etc. Online pose estimation methods typically use Gaussian distributions to describe the uncertainty in the pose parameters. Such a description can be inadequate when using parameters s…