Can I read Deep Network Uncertainty Maps for Indoor Navigation on EtoBox?

Deep Network Uncertainty Maps for Indoor Navigation by Verdoja, Francesco; Lundell, Jens; Kyrki, Ville is a scholarly article available to read on EtoBox.

What is Deep Network Uncertainty Maps for Indoor Navigation about?

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

Author

Verdoja, Francesco; Lundell, Jens; Kyrki, Ville

Published

2018

Language

EN