29 research outputs found
Graph Laplacian for Image Anomaly Detection
Reed-Xiaoli detector (RXD) is recognized as the benchmark algorithm for image
anomaly detection; however, it presents known limitations, namely the
dependence over the image following a multivariate Gaussian model, the
estimation and inversion of a high-dimensional covariance matrix, and the
inability to effectively include spatial awareness in its evaluation. In this
work, a novel graph-based solution to the image anomaly detection problem is
proposed; leveraging the graph Fourier transform, we are able to overcome some
of RXD's limitations while reducing computational cost at the same time. Tests
over both hyperspectral and medical images, using both synthetic and real
anomalies, prove the proposed technique is able to obtain significant gains
over performance by other algorithms in the state of the art.Comment: Published in Machine Vision and Applications (Springer
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)
Hallucinating robots: Inferring Obstacle Distances from Partial Laser Measurements
Many mobile robots rely on 2D laser scanners for localization, mapping, and
navigation. However, those sensors are unable to correctly provide distance to
obstacles such as glass panels and tables whose actual occupancy is invisible
at the height the sensor is measuring. In this work, instead of estimating the
distance to obstacles from richer sensor readings such as 3D lasers or RGBD
sensors, we present a method to estimate the distance directly from raw 2D
laser data. To learn a mapping from raw 2D laser distances to obstacle
distances we frame the problem as a learning task and train a neural network
formed as an autoencoder. A novel configuration of network hyperparameters is
proposed for the task at hand and is quantitatively validated on a test set.
Finally, we qualitatively demonstrate in real time on a Care-O-bot 4 that the
trained network can successfully infer obstacle distances from partial 2D laser
readings.Comment: In 2018 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS