Learning Orientation Distributions for Object Pose Estimation
Brian Okorn,Mengyun Xu,Martial Hebert,David Held,Brian Okorn,Mengyun Xu,Martial Hebert,David Held
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. Our methods take into account both the inaccuracies in the pose estimation as well as the object sym...
