Imagining worse than reality: comparing beliefs and intentions between disaster evacuees and survey respondents

We often credit disasters, and their coverage in the media, with changes in the public perception of risk associated with low-probability, high-consequence events (LPHCs). With a change in perceptions, we also expect changes in beliefs, preferences, and behaviors. Do beliefs and behaviors change in different ways for people who live through these LPHC critical events, as opposed to people who observe them? This study compares hypothetical hurricanes with actual hurricane effects in a survey quasi-experiment. Findings indicate that hypothetical disasters induce stronger reactions than those experienced in the natural world, as Hurricane Katrina bystanders imagine themselves incurring much higher damages, and being much less likely to return to live in their hurricane-damaged homes, than actual Hurricane Katrina evacuees. Ultimately, respondents considering a hypothetical low-probability, high-consequence event exhibit exaggerated beliefs and opposite decisions of those who actually lived through one of these events. Results underline the importance of examining the differences between public perceptions and experiential reality

3DSCAN: Online Ego-Localization and Environment Mapping for Micro Aerial Vehicles

International audienceWe present 3DSCAN (3D Scene Characterization for Autonomous Navigation), a software application for state estimation and environment modeling using lowcost 3D sensors, such as a stereorig and RGBD cameras. For state estimation, we describe an original keyframe-based stereoscopic visual odometry technique, which can run at more than 20Hz on a lightweight computer. This so-called ‘efficient Visual Odometry’ (eVO) has been evaluated on several datasets and provides accurate results and limited drift, even for indoor/outdoor trajectories. Environment modeling aggregates instantaneous depthmaps in a volumetric Octomap [15] representation. Stereoscopic depthmaps are computed by a very fast dense matching algorithm derived from eFolki, an optical flow code implemented on GPU. These developments are combined in the 3DSCAN software, which is successfully demonstrated on our MAV (Micro Aerial Vehicle) system, following indoor, outdoor or mixed trajectories

Real-Time Mobile Object Detection Using Stereo

International audienceThis paper considers passive vision for robotics and focuses on devising a real-time process for moving object detection using a stereo rig. As several previous works, our method relies on the use of dense stereo and of optical flow. Observing that the main computational load of existing methods is related to the estimation of the optical flow, we propose to use a fast algorithm based on Lucas-Kanade's paradigm. We derive a new uncertainty model which explicitly takes into account all errors originating from each estimation step of the process. In contrast with most previous works, we describe a rigorous expansion of the error related to vision based ego-motion estimation. Finally, we present a comparative study of performance on the KITTI dataset, which demonstrates the effectiveness of the proposed approach

Accuracy assessment of a Lucas-Kanade based correlation method for 3D PIV

International audienceWe introduce and characterize a new 3D cross-correlation algorithm, which relies on gradient-based iterative volume deformation. The algorithm, FOLKI3D, is the extension to 3D PIV of the approach introduced by Champagnat et al. 2011. It has a highly parallel structure and is implemented on GPU. Additionally to the gradient approach for displacement estimation, we implemented a high-order interpolation scheme (with cubic B-Splines) in the volume deformation step, at a reasonable computational cost. Performance tests on synthetic volumic distributions first allow to characterize the spatial transfer function of the algorithm, and to confirm the efficiency of this interpolator, comparable to that of standard image deformation methods in planar PIV. A second series of synthetic tests then investigates the response of FOLKI3D to sources of noise specific to the tomographic PIV context, i.e. ghost particles. Depending on the tests, the algorithm is found as efficient or more robust than the state-of-the-art. The gain brought by the high-order interpolation is also confirmed in a situation with a large number of ghosts, and different reconstructed particle shapes

EXTRACTING RELEVANCE FROM SAR TEMPORAL PROFILES ON A GLACIER AND AN ALPINE WATERSHED BY A DEEP AUTOENCODER

International audienceAbstract. This paper proposes to use methods for compressing the temporal profiles of Sentinel-1 images, in order to be able to evaluate and analyze the richness of the temporal dynamics, both on a glacier and on a watershed. We propose to use unsupervised deep learning to auto-encode the temporal information in 3 dimensions, allowing to use the three descriptors as three RGB components to produce a colored composition synthesizing the information. We compare this Convolutional AutoEncoder (CAE) approach with a dimensionality reduction based on a Principal Component Analysis (PCA) of the temporal profiles. The two methods, CAE and PCA, are applied to a time series over the Kyagar Glacier before and after a surge event, and on an alpine watershed to compare the differences in dynamic evolution associated with different terrain classes with and without snow. On the one hand, on the glacier, the stacks of 10 images used are too short for CAE to extract more than two really significant axes. On the other hand, with longer profiles available over the alpine watershed, the CAE is interesting to improve the clustering results obtained from the decomposition