4,309 research outputs found

    Deep Network Uncertainty Maps for Indoor Navigation

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    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)

    Trends in aerosol abundances and distributions

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    The properties of aerosols that reside in the upper atmosphere are described. Special emphasis is given to the influence these aerosols have on ozone observation systems, mainly through radiative effects, and on ambient ozone concentrations, mainly through chemical effects. It has long been appreciated that stratospheric particles can interfere with the remote sensing of ozone distribution. The mechanism and magnitude of this interference are evaluated. Separate sections deal with the optical properties of upper atmospheric aerosols, long-term trends in stratospheric aerosols, perturbations of the stratospheric aerosol layer by volcanic eruptions, and estimates of the impacts that such particles have on remotely measured ozone concentrations. Another section is devoted to a discussion of the polar stratospheric clouds (PSC's). These unique clouds, recently discovered by satellite observation, are now thought to be intimately connected with the Antarctic ozone hole. Accordingly, interest in PSC's has grown considerably in recent years. This chapter describes what we know about the morphology, physical chemistry, and microphysics of PSC's

    Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes

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    International audienceSatellite and airborne optical sensors are increasingly used by scientists, and policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a specific experimental configuration (surface and atmosphere conditions, sun direction, etc.) are commonly translated into qualitative and quantitative Earth surface parameters. However, atmosphere properties and Earth surface 3D architecture often confound their interpretation. Radiative transfer models capable of simulating the Earth and atmosphere complexity are, therefore, ideal tools for linking remotely sensed data to the surface parameters. Still, many existing models are oversimplifying the Earth-atmosphere system interactions and their parameterization of sensor specifications is often neglected or poorly considered. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models simulating the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It has been developed since 1992. It models optical signals at the entrance of imaging radiometers and laser scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental configuration and instrumental specification. It is freely distributed for research and teaching activities. This paper presents DART physical bases and its latest functionality for simulating imaging spectroscopy of natural and urban landscapes with atmosphere, including the perspective projection of airborne acquisitions and LIght Detection And Ranging (LIDAR) waveform and photon counting signals

    Multiple scattering effects in the lidar pulse stretching problem

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    November, 1997.Bibliography: pages 155-156.Sponsored by DOD Center for Geosciences-Phase II at CIRA/CSU DAAH04-94-G-0420.Sponsored by NASA NAG1-1702

    Time-Gated Topographic LIDAR Scene Simulation

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    The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model has been developed at the RochesterInstitute of Technology (RIT) for over a decade. The model is an established, first-principles based scene simulationtool that has been focused on passive multi- and hyper-spectral sensing from the visible to long wave infrared (0.4 to 14 µm). Leveraging photon mapping techniques utilized by the computer graphics community, a first-principles based elastic Light Detection and Ranging (LIDAR) model was incorporated into the passive radiometry framework so that the model calculates arbitrary, time-gated radiances reaching the sensor for both the atmospheric and topographicreturns. The active LIDAR module handles a wide variety of complicated scene geometries, a diverse set of surface and participating media optical characteristics, multiple bounce and multiple scattering effects, and a flexible suite of sensormodels. This paper will present the numerical approaches employed to predict sensor reaching radiances andcomparisons with analytically predicted results. Representative data sets generated by the DIRSIG model for a topographical LIDAR will be shown. Additionally, the results from phenomenological case studies including standard terrain topography, forest canopy penetration, and camouflaged hard targets will be presented

    Performance Analysis of Constant Speed Local Abstacle Avoidance Controller Using a MPC Algorithym on Granular Terrain

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    A Model Predictive Control (MPC) LIDAR-based constant speed local obstacle avoidance algorithm has been implemented on rigid terrain and granular terrain in Chrono to examine the robustness of this control method. Provided LIDAR data as well as a target location, a vehicle can route itself around obstacles as it encounters them and arrive at an end goal via an optimal route. This research is one important step towards eventual implementation of autonomous vehicles capable of navigating on all terrains. Using Chrono, a multibody physics API, this controller has been tested on a complex multibody physics HMMWV model representing the plant in this study. A penalty-based DEM approach is used to model contacts on both rigid ground and granular terrain. Conclusions are drawn regarding the MPC algorithm performance based on its ability to navigate the Chrono HMMWV on rigid and granular terrain. A novel simulation framework has been developed to efficiently simulate granular terrain for this application. Two experiments were conducted to analyze the performance of the MPC LIDAR-based constant speed local obstacle avoidance controller. In the first, two separate controllers were developed, one using a 2-DOF analytical model to predict the HMMWV behavior, and the second using a higher fidelity 14-DOF vehicle model. In this first experiment, two controllers were compared as they controlled the HMMWV on two obstacle fields on rigid ground and granular terrain to understand the influence of model fidelity and terrain on controller performance. From these results, an improved lateral force model was developed for use in the 2-DOF vehicle model to better model the tire ground interaction using terramechanics relations. A second experiment was performed to compare two developed controllers. One used the 2-DOF vehicle model using the Pacejka Magic Formula to estimate tire forces while the second used a 2-DOF vehicle model with the newly developed force model to estimate lateral tire forces. As a result of this research, a smarter controller was developed that uses friction angle, cohesion, and interparticle friction coefficient to more accurately predict vehicle trajectories on granular terrain and allow a vehicle to navigate autonomously on granular terrain
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