9,506 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)
Assembly via disassembly: A case in machine perceptual development
First results in the effort of learning about representations of objects is presented. The questions attempted to be answered are: What is innate and what must be derived from the environment. The problem is casted in the framework of disassembly of an object into two parts
Robust Techniques for Bearing Estimation in Contaminated Gaussian Noise
The problem of estimating directions-of-arrival (DOA) of radiating sources from measurements provided by a passive array of sensors is frequently encountered in radar, sonar, radio astronomy and seismology. In this study various robust methods for the DOA estimation problem are developed, where the term robustness refers to insensitivity against small deviation in the underlying Gaussian noise assumption. The first method utilizes an eigenvector method and robust reconstruction of the correlation matrix by time series modeling of the array data; Secondly, a decentralized processing scheme is considered for geographically distributed array sites. The method provides reliable estimates even when a few of the subarray sites are malfunctioning. The above two techniques are useful for narrow band and incoherent sources. The third robust method, which utilizes Radon Transform, is capable of handling both the narrow band and wide band sources as well as the incoherent or coherent sources. The technique is also Useful in situations of very low SNR and colored noise with unknown correlation structure. The fourth method is an efficient narrow band robust maximum likelihood DOA estimation algorithm which is capable of handling coherent signals as well as the single snapshot cases. Furthermore, relationships between eigenvector methods and a ML DOA estimation, where the source signals are treated as sample functions of Gaussian random processes, are investigate
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