Uncertainty-aware 3D Object-Level Mapping with Deep Shape Priors
Ziwei Liao Jun Yang Jingxing Qian Angela P. Schoellig Steven L. Waslander
3D object-level mapping is a fundamental problem in robotics, which is
especially challenging when object CAD models are unavailable during inference.
In this work, we propose a framework that can reconstruct high-quality
object-level maps for unknown objects. Our approach takes multiple RGB-D images
as input and outputs dense 3D shapes and 9-DoF poses (including 3 scale
parameters) for detected objects. The core idea of our approach is to leverage
a learnt generative model for shape categories as a prior and to formulate a
probabilistic, uncertainty-aware optimization framework for 3D reconstruction.
We derive a probabilistic formulation that propagates shape and pose
uncertainty through two novel loss functions. Unlike current state-of-the-art
approaches, we explicitly model the uncertainty of the object shapes and poses
during our optimization, resulting in a high-quality object-level mapping
system. Moreover, the resulting shape and pose uncertainties, which we
demonstrate can accurately reflect the true errors of our object maps, can also
be useful for downstream robotics tasks such as active vision. We perform
extensive evaluations on indoor and outdoor real-world datasets, achieving
achieves substantial improvements over state-of-the-art methods. Our code will
be available at https://github.com/TRAILab/UncertainShapePose.