3,188 research outputs found
Deep Network Uncertainty Maps for Indoor Navigation
Most mobile robots for indoor use rely on 2D laser scanners for localization,
mapping and navigation. These sensors, however, cannot detect transparent
surfaces or measure the full occupancy of complex objects such as tables. Deep
Neural Networks have recently been proposed to overcome this limitation by
learning to estimate object occupancy. These estimates are nevertheless subject
to uncertainty, making the evaluation of their confidence an important issue
for these measures to be useful for autonomous navigation and mapping. In this
work we approach the problem from two sides. First we discuss uncertainty
estimation in deep models, proposing a solution based on a fully convolutional
neural network. The proposed architecture is not restricted by the assumption
that the uncertainty follows a Gaussian model, as in the case of many popular
solutions for deep model uncertainty estimation, such as Monte-Carlo Dropout.
We present results showing that uncertainty over obstacle distances is actually
better modeled with a Laplace distribution. Then, we propose a novel approach
to build maps based on Deep Neural Network uncertainty models. In particular,
we present an algorithm to build a map that includes information over obstacle
distance estimates while taking into account the level of uncertainty in each
estimate. We show how the constructed map can be used to increase global
navigation safety by planning trajectories which avoid areas of high
uncertainty, enabling higher autonomy for mobile robots in indoor settings.Comment: Accepted for publication in "2019 IEEE-RAS International Conference
on Humanoid Robots (Humanoids)
LocPoseNet: Robust Location Prior for Unseen Object Pose Estimation
Object location priors have been shown to be critical for the standard 6D
object pose estimation setting, where the training and testing objects are the
same. Specifically, they can be used to initialize the 3D object translation
and facilitate 3D object rotation estimation. Unfortunately, the object
detectors that are used for this purpose do not generalize to unseen objects,
i.e., objects from new categories at test time. Therefore, existing 6D pose
estimation methods for previously-unseen objects either assume the ground-truth
object location to be known, or yield inaccurate results when it is
unavailable. In this paper, we address this problem by developing a method,
LocPoseNet, able to robustly learn location prior for unseen objects. Our
method builds upon a template matching strategy, where we propose to distribute
the reference kernels and convolve them with a query to efficiently compute
multi-scale correlations. We then introduce a novel translation estimator,
which decouples scale-aware and scale-robust features to predict different
object location parameters. Our method outperforms existing works by a large
margin on LINEMOD and GenMOP. We further construct a challenging synthetic
dataset, which allows us to highlight the better robustness of our method to
various noise sources
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