Occupancy Map Research Articles

Comparison of soil use in the infiltration of rainwater: pasture and forest

Los bosques de Itajaí/SC, Brazil han sido intensamente fragmentados en la llanura, reflejando en el predominio de asentamientos urbanos, estructuras portuarias y cultivos de arroz. Al sumarse los eventos provenientes del cambio climático a las condiciones ambientales existentes, la región se torna susceptible a las inundaciones. De esta forma, este trabajo tiene como objetivo analizar la función hídrica de los bosques de llanura en la infiltración del agua de las lluvias en Itajaí. Se utilizó el Sistema de Información Geográfica para la elaboración de un mapa de uso y ocupación del suelo y se comparó con el mapa de suelos del municipio. De estos análisis fueron seleccionadas dos áreas en la llanura en la misma clase de suelos, una con pastoreo y una cubierta por bosque. En los meses de agosto y octubre, estas áreas se muestrearon y clasificaron acorde a los siguientes criterios: granulometría, humedad, conductividad hidráulica y contenido de materia orgánica de los suelos. También en estas unidades de muestra se realizó el ensayo de velocidad de infiltración básica (VIB) mediante el método de anillos concéntricos. Se utilizó el análisis de varianza (ANOVA) para verificar el efecto entre las variables obtenidas. Los valores medios obtenidos de VIB para área de pastoreo fueron 3,45 cm h-1 y 3,60 cm h-1 para los meses de agosto y octubre, respectivamente. En el área con vegetación se obtuvieron valores medios de 19,05 cm h-1 y 8,70 cm h-1 para los meses de agosto y octubre, respectivamente. En este trabajo fue posible verificar que hay diferencias significativas en la infiltración de agua, en la parte superficial del suelo,entre las coberturas con bosque y con pastoreo, donde las condiciones edáficas son iguales. La vegetación forestal denota su papel potencial en la infiltración de agua de lluvia en la planicie de inundación del área.

Monte Carlo Localization on Gaussian Process Occupancy Maps for Urban Environments

Map-aided localization methods have been employed for vehicle localization to overcome the limitations of global navigation satellite system (GNSS) devices. In this solution, sensor information is matched to the environment map to determine the vehicle position. Occupancy grid maps (OGMs) have been adapted for map-aided localization. However, there are known drawbacks of OGM, such as the environment discretization, the assumption of independence between grid cells, and the need for dense measurements. In recent years, Gaussian process occupancy map (GPOM) was developed to suppress some of the OGM limitations. GPOM enables the computation of the likelihood of occupancy at any location, even if not directly observed by the sensor, thus representing the environment in a continuous manner. Taking into account the superiority of GPOM over OGM, we devise a novel vehicle localization technique for urban environments. This solution enables more accurate localization due to the use of a representation that better models the real environment. The development of the proposed method is based on Monte Carlo localization, which is a popular map-aided localization method. Two road features commonly found in urban cities were chosen to build the maps: road curbs and road markings. Specifically, the proposed localization method relies on a GPOM constructed with curb data and an OGM built with road marking data. Experiments were performed in real urban environments. Maps were intentionally generated using sparse light detection and ranging (LIDAR) data to verify the localization in non-observed areas. The localization system was evaluated by comparing the results with a high precision GNSS device.

Learning to reconstruct 3D structures for occupancy mapping from depth and color information

Real-world scenarios contain many structural patterns that, if appropriately extracted and modeled, can be used to reduce problems associated with sensor failure and occlusions while improving planning methods in such tasks as navigation and grasping. This paper devises a novel unsupervised procedure that models 3D structures from unorganized pointclouds as occupancy maps. Our methodology enables the learning of unique and arbitrarily complex features using a variational Bayesian convolutional auto-encoder, which compresses local information into a latent low-dimensional representation and then decodes it back in order to reconstruct the original scene, including color information when available. This reconstructive model is trained on features obtained automatically from a wide variety of scenarios, in order to improve its generalization and interpolative powers. We show that the proposed framework is able to recover partially missing structures and reason over occlusions with high accuracy while maintaining a detailed reconstruction of observed areas. To combine localized feature estimates seamlessly into a single global structure, we employ the Hilbert maps framework, recently proposed as a robust and efficient occupancy mapping technique, and introduce a new kernel for reproducing kernel Hilbert space projection that uses estimates from the reconstructive model. Experimental tests are conducted with large-scale 2D and 3D datasets, using both laser and monocular data, and a study of the impact of various accuracy–speed trade-offs is provided to assess the limits of the proposed methodology.

A expansão da mancha urbana da cidade de Guarapuava-PR, entre 1940 e 2016

O presente trabalho teve por objetivo avaliar a expansão físico-territorial urbana de Guarapuava-PR, entre 1940 e 2016, por meio do mapeamento da ocupação urbana, utilizando-se de diversas fontes cartográficas e técnicas de geoprocessamento. A cidade de Guarapuava é de porte médio e localiza-se na região Centro-Sul do Estado do Paraná, região que apresenta baixa dinâmica econômica e os mais baixos IDH (Índices de Desenvolvimento Humano) do Estado do Paraná. Os dados utilizados para identificação da ocupação urbana em diferentes décadas foram cartas analógicas de loteamentos urbanos registrados, cartas topográficas, fotografias áreas e imagens de satélites. Os procedimentos metodológicos consistiram na integração e processamento dos diferentes dados cartográficos em um SIG (Sistema de Informação Geográfica). A expansão físico-territorial urbana de Guarapuava entre 1940 e 2016 deu-se em três fases distintas: até 1960, de 1960 a 1990, e após 1990. A primeira fase, até 1960 foi marcada por uma urbanização incipiente, com pequena taxa de expansão da área urbana; na fase de 1960 a 1990 por elevada taxa de urbanização na cidade, com significativa expansão das áreas urbanas; enquanto que a fase após 1990 é caracterizada pelo adensamento populacional.

Predicting Occupancy Distributions of Walking Humans With Convolutional Neural Networks

As robots are increasingly entering human environments, many subtleties of socially compliant navigation are still unsolved. To behave in a socially compliant way, robots need to have an understanding of the natural motion paths of humans in the shared environment. Humans intuitively follow social norms, which allows them to navigate smoothly even in crowded environments. For example, when humans enter a previously unseen building, they are still able to infer from their surroundings where humans would typically walk and use this information to obviate interference. In this letter, we propose an approach to learn such a predictive method. A robot could use this information to find nondisturbing waiting positions, avoid crowded areas, or clean heavily frequented areas more often. We propose the use of convolutional neural networks to predict average occupancy maps of walking humans even in environments where no human trajectory data are available. In experiments, we show that our method transfers from simulation to real-world data and performs better than several baseline methods. We demonstrate the applicability on a real robot to find good waiting positions near narrow passages as well as a planner, which avoids areas where human interference is likely.

Towards real-time 3D continuous occupancy mapping using Hilbert maps

The ability to model the surrounding space and determine which areas are occupied is of key importance in many robotic applications, ranging from grasping and manipulation to path planning and obstacle avoidance. Occupancy modeling is often hindered by several factors, such as: real-time constraints, that require quick updates and access to estimates; quality of available data, that may contain gaps and partial occlusions; and memory requirements, especially for large-scale environments. In this work we propose a novel framework that elegantly addresses all these issues, by producing an efficient non-stationary continuous occupancy function that can be efficiently queried at arbitrary resolutions. Furthermore, we introduce techniques that allow the learning of individual features for different areas of the input space, that are better able to model its contained information and promote a higher-level understanding of the observed scene. Experimental tests were conducted on both simulated and real large-scale datasets, showing how the proposed framework rivals current state-of-the-art techniques in terms of computational speed while achieving a substantial decrease (of orders of magnitude) in memory requirements and demonstrating better interpolative powers, that are able to smooth out sparse and noisy information.

Building Continuous Occupancy Maps With Moving Robots

Mapping the occupancy level of an environment is important for a robot to navigate in unknown and unstructured environments. To this end, continuous occupancy mapping techniques which express the probability of a location as a function are used. In this work, we provide a theoretical analysis to compare and contrast the two major branches of Bayesian continuous occupancy mapping techniques---Gaussian process occupancy maps and Bayesian Hilbert maps---considering the fact that both utilize kernel functions to operate in a rich high-dimensional implicit feature space and use variational inference to learn parameters. Then, we extend the recent Bayesian Hilbert maps framework which is so far only used for stationary robots, to map large environments with moving robots. Finally, we propose convolution of kernels as a powerful tool to improve different aspects of continuous occupancy mapping. Our claims are also experimentally validated with both simulated and real-world datasets.

Integrating auxiliary data in optimal spatial design for species distribution modelling

AbstractTraditional surveys used to create species distribution maps and estimate ecological relationships are expensive and time consuming. Citizen science offers a way to collect a massive amount of data at negligible cost and has been shown to be a useful supplement to traditional analyses. However, there remains a need to conduct formal surveys to firmly establish ecological relationships and trends.In this paper, we investigate the use of auxiliary (e.g. citizen science) data as a guide to designing more efficient ecological surveys. Our aim is to explore the use of opportunistic data to inform spatial survey design through a novel objective function that minimizes misclassificaton rate (i.e. false positives and false negatives) of the estimated occupancy maps. We use an initial occupancy estimate from auxiliary data as the prior in a Bayesian spatial occupancy model, and an efficient posterior approximation that accounts for spatial dependence, covariate effects, and imperfect detection in an exchange algorithm to search for the optimal set of sampling locations to minimize misclassification rate.We examine the optimal design as a function of the detection rate and quality of the citizen‐science data, and compare this optimal design with several common ad hoc designs via an extensive simulation study. We then apply our method to eBird data for the brown‐headed nuthatch in the Southeast US.We argue that planning a survey with the use of auxiliary data improves estimation accuracy and may significantly reduce the costs of sampling.

Efficient Octree-Based Volumetric SLAM Supporting Signed-Distance and Occupancy Mapping

We present a dense volumetric simultaneous localisation and mapping (SLAM) framework that uses an octree representation for efficient fusion and rendering of either a truncated signed distance field (TSDF) or an occupancy map. The primary aim of this letter is to use one single representation of the environment that can be used not only for robot pose tracking and high-resolution mapping, but seamlessly for planning. We show that our highly efficient octree representation of space fits SLAM and planning purposes in a real-time control loop. In a comprehensive evaluation, we demonstrate dense SLAM accuracy and runtime performance on-par with flat hashing approaches when using TSDF-based maps, and considerable speed-ups when using occupancy mapping compared to standard occupancy maps frameworks. Our SLAM system can run at 10–40 Hz on a modern quadcore CPU, without the need for massive parallelization on a GPU. We, furthermore, demonstrate a probabilistic occupancy mapping as an alternative to TSDF mapping in dense SLAM and show its direct applicability to online motion planning, using the example of informed rapidly-exploring random trees (RRT $^*$ ).

DCT Maps: Compact Differentiable Lidar Maps Based on the Cosine Transform

Most robot mapping techniques for lidar sensors tessellate the environment into pixels or voxels and assume uniformity of the environment within them. Although intuitive, this representation entails disadvantages: The resulting grid maps exhibit aliasing effects and are not differentiable. In the present paper, we address these drawbacks by introducing a novel mapping technique that does neither rely on tessellation nor on the assumption of piecewise uniformity of the space, without increasing memory requirements. Instead of representing the map in the position domain, we store the map parameters in the discrete frequency domain and leverage the continuous extension of the inverse discrete cosine transform to convert them to a continuously differentiable scalar field in the position domain, which we call DCT map. A DCT map assigns to each point in space a lidar decay rate, which models the local permeability of the space for laser rays. In this way, the map can describe objects of different laser permeabilities, from completely opaque to completely transparent. DCT maps represent lidar measurements significantly more accurate than grid maps, Gaussian process occupancy maps, and Hilbert maps, all with the same memory requirements, as demonstrated in our real-world experiments.

Hybrid Filtering Framework Based Robust Localization for Industrial Vehicles

This paper presents a precise and robust localization framework for autonomous vehicles. In contrast to simultaneous localization and mapping, localization and mapping in our method are separate (i.e., first mapping and then localization within this map). The map used in this paper is a 3-D occupancy map that is generated from a stitched point cloud. For localization, a hybrid filtering framework is proposed to match the live data with the prior map. In the upper layer, odometer data, IMU data, and the map matching result are fused by a cubature Kalman filter, which will limit the predictive pose within a reasonable bound. In the lower layer, the map matching problem is converted into a point set registration problem that is solved using a particle filter, which will make the matching result robust to local minima. This hybrid scheme makes localization more robust to convergence to a local minimum, which is often encountered in the localization task for autonomous vehicles. This method can also guarantee decimeter-level precision in industrial environments. Experiments demonstrate the validity of this method and also show that it outperforms some state-of-the-art methods.

Spatial Interpolation of Cyclostationary Test Statistics in Cognitive Radio Networks: Methods and Field Measurements

The focus of this paper is on evaluating different spatial interpolation methods for the construction of radio environment map using field measurements obtained by cyclostationary-based mobile sensors. Mobile sensing devices employing cyclostationary detectors provide lot of advantages compared to the widely used energy detectors, such as robustness to noise uncertainty and ability to distinguish among different primary user signals. However, mobile sensing results are not available at locations between the sensors making it difficult for a secondary user (possibly without a spectrum sensor) to decide whether to use primary user resources at that location. To overcome this, spatial interpolation of test statistics measured at limited number of locations can be carried out to create a channel occupancy map at unmeasured locations between the sensors. For this purpose, different spatial interpolation techniques for the cyclostationary test statistic have been employed in this paper such as inverse distance weighting, ordinary kriging, and universal kriging. The effectiveness of these methods is demonstrated by applying them on extensive real-world field measurement data obtained by mobile-phone-compliant spectrum sensors. The field measurements were carried out using four mobile spectrum sensors measuring eight digital video broadcasting-terrestrial (DVB-T) channels at more than hundred locations encompassing roughly one-third of the area of the city of Espoo in Finland. The accuracy of the spatial interpolation results based on the field measurements is determined using the cross-validation approach with the widely used root mean square error as the metric. Field measurement results indicate that reliable results with spatial coverage can be achieved using kriging for cyclostationary based test statistics. Comparison of spatial interpolation results of cyclostationary test statistics is also carried out with those of energy values obtained during the measurement campaign in the form of received signal strength indicator. Comparison results clearly show the performance improvement and robustness obtained using cyclostationary based detectors instead of energy detectors.

3D Occupancy Mapping Framework Based on Acoustic Camera in Underwater Environment

Abstract In this paper, we present a novel probabilistic three-dimensional (3D) mapping framework that uses acoustic images captured in an underwater environment. Acoustic camera is a forward-looking imaging sonar that is commonly used in underwater inspection recently; however, the loss of elevation angle information makes it difficult to get a better understanding of underwater environment. To cope with this, we apply a probabilistic occupancy mapping framework with a novel inverse sensor model suitable for the acoustic camera in order to reconstruct the underwater environment in volumetric presentation. The simulations and experimental results demonstrate that our mapping framework for the acoustic camera can reconstruct dense 3D model of underwater targets successfully.

Downscale: An R Package for Downscaling Species Occupancy from Coarse-Grain Data to Predict Occupancy at Fine-Grain Sizes

The geographical area occupied by a species is a valuable measure for assessing its conservation status. Coarse-grained occupancy maps are available for many taxa, e.g., as atlases, but often at spatial resolutions too coarse for conservation use. However, mapping occupancy at fine spatial resolution across the entire extent of the species' distribution is often prohibitively expensive for the majority of species. Occupancy downscaling is a technique to estimate finer scale occupancy from coarse scale maps, by using the occupancyarea relationship (OAR) which reflects how the proportion of area occupied increases with spatial grain size. Models that describe the OAR are fitted to observed occupancies at the available coarse-grain sizes and then extrapolated to predict occupancy at the finer grain sizes required. The downscale package in the R programming environment provides users with easy-to-use functions for downscaling occupancy with ten published models. First, upgrain calculates occupancy for multiple grain sizes larger than the input data. Normal methods for aggregating raster data increase the extent of the focal area as grain size increases which is undesirable, so the function fixes the extent for all grain sizes, assigning unsampled cells as absences. Four suggested methods are provided to enable this and upgrain.threshold provides diagnostic plots that allow the user to explore the inherent trade-off between making assumptions about unsampled locations and discarding information from sampled locations. downscale fits nine possible models to the data generated from upgrain. hui.downscale fits the special case of the Hui model. predict and plot extrapolate the fitted models to predict and plot occupancy at finer grain sizes. Finally, ensemble.downscale simultaneously fits two or more of the downscaling models and calculates mean predicted occupancy across all selected models. Here we describe the package and apply the functions to atlas data of a hypothetical UK species.

Lateral Inhibition Map Updating Method to Prevent Map Deformation for Simultaneous Localization and Mapping in Natural Environment

In this paper, a novel real time map updating algorithm using a concept of lateral inhibition was proposed and it was evaluated by simple 2D mapping experiment. For long time and difficult map creation process of simultaneous localization and mapping (SLAM) algorithm, stable map updating is one of the important topics. By using the lateral inhibition process to the map updating process, it is able to reduce the complexity of the map updating process of the SLAM. It could reduce the noise around the wall and emphasize the border (such a wall). The experimental result of map updating process of 6.0 x 3.5 m 2D room with many obstacles shows the x-y positional and the rotational estimation precision would be measured as x,y= 1.8 ×10-6 m (less than the LIDAR measurement error about 0.04 m) and the = - 1.9×10-2 deg (1 hour measurement), and the map was updated real time when the LIDAR sensor was moving from a position to another room position with low speed. After the 38 min outdoor experiment, the occupancy map was stably maintained, and there is also slight rotation and position estimation errors (x,y = 0.0 m, = 8.3 ×10-2 deg). The proposed method could suppress the wall existence probability distribution creeping induced by the natural environment vibration such as tree or leaves.

Gaussian processes autonomous mapping and exploration for range-sensing mobile robots

Most of the existing robotic exploration schemes use occupancy grid representations and geometric targets known as frontiers. The occupancy grid representation relies on the assumption of independence between grid cells and ignores structural correlations present in the environment. We develop a Gaussian Processes (GPs) occupancy mapping technique that is computationally tractable for online map building due to its incremental formulation and provides a continuous model of uncertainty over the map spatial coordinates. The standard way to represent geometric frontiers extracted from occupancy maps is to assign binary values to each grid cell. We extend this notion to novel probabilistic frontier maps computed efficiently using the gradient of the GP occupancy map. We also propose a mutual information-based greedy exploration technique built on that representation that takes into account all possible future observations. A major advantage of high-dimensional map inference is the fact that such techniques require fewer observations, leading to a faster map entropy reduction during exploration for map building scenarios. Evaluations using the publicly available datasets show the effectiveness of the proposed framework for robotic mapping and exploration tasks.

Grand canonical Monte Carlo simulation study of cyclohexane, oxane, 1,4-dioxane, and 1,3,5-trioxane confined in carbon slit pore

A grand canonical Monte Carlo (GCMC) simulation has been conducted to investigate the adsorption of cyclohexane, oxane, 1,4-dioxane, and 1,3,5-trioxane into a carbon slit pores with widths of 0.8, 1.0, and 1.2nm at 298K. Particular emphasis has been paid to the effect of molecular size and shape on the intermolecular structure of the adsorbate confined in the carbon slit pore. Simulations showed that cyclohexane and oxane form a disordered single layer structure at saturation, while 1,4-dioxane and 1,3,5-trioxane form a bilayer structure in the 0.8nm wide pore. Among the four adsorbates, 1,4-dioxane forms the most regular packing structure in the 0.8nm wide pore at saturation; most 1,4-dioxane molecules are parallel to the pore surface and form a square lattice packing structure, which may be associated with the square molecular shape. In addition, 1,4-dioxane molecules are oriented so that the OO axes in the molecule are alternately arrayed vertically and horizontally to avoid Coulomb repulsion between intermolecular oxygen atoms with negative charges. In addition, this packing structure is formed via two transitions (a gas to a liquid-like state and a liquid-like state to a solid-like state), as determined from analysis of the isothermal compressibility and 2D occupancy maps. These transitions are manifested as a step and a sub-step in the adsorption isotherm. On the other hand, triangular 1,3,5-trioxane forms a hexagonal lattice packing structure in the 0.8nm wide pore at saturation; however, no definite sub-step appeared in the adsorption isotherm. The pore size dependence of the packing structures of these molecules is also discussed.

Dendara at Its Origins: New Evidence for a Pre- and Early Dynastic Settlement Site in Upper Egypt

The archaeology of Pre- and Early Dynastic Egypt has been one of the most active area in Egyptology in the past few decades, but despite the intensification of the fieldwork research in the Nile Delta or Middle Egypt, our knowledge of early Egyptian settlements in Upper Egypt is still limited to several prominent sites, some under excavation since the late nineteenth century. The work undertaken by a team from The Oriental Institute—on the concession of the French Archaeological Institute (IFAO)—in the intramural area of the Hathor and Isis sanctuaries at Dendara has uncovered the earliest archaeological levels discovered to date, with several strata from the Naqada IIC–D and Early Dynastic periods. These discoveries push back the date of a first permanent community at this site by more than five hundred years and add an important point on the map of the predynastic occupation of Upper Egypt, all the more significant since the site became one of the main provincial centers, the capital of the 6th Nome of ...

Online View Planning for Inspecting Unexplored Underwater Structures

In this letter, we propose a method to automate the exploration of unknown underwater structures for autonomous underwater vehicles (AUVs). The proposed algorithm iteratively incorporates exteroceptive sensor data and replans the next-best-view in order to fully map an underwater structure. This approach does not require prior environment information. However, a safe exploration depth and the exploration area (defined by a bounding box, parameterized by its size, location, and resolution) must be provided by the user. The algorithm operates online by iteratively conducting the following three tasks: 1) Profiling sonar data are first incorporated into a 2-D grid map, where voxels are labeled according to their state (a voxel can be labeled as empty, unseen, occluded, occplane, occupied, or viewed). 2) Useful viewpoints to continue exploration are generated according to the map. 3) A safe path is generated to guide the robot toward the next viewpoint location. Two sensors are used in this approach: a scanning profiling sonar, which is used to build an occupancy map of the surroundings, and an optical camera, which acquires optical data of the scene. Finally, in order to demonstrate the feasibility of our approach, we provide real-world results using the Sparus II AUV.

Nano-scale experimental investigation of in-situ wettability and spontaneous imbibition in ultra-tight reservoir rocks

We investigated spontaneous imbibition behavior, three-dimensional fluid occupancy maps, and in-situ wettability at the nano scale in five ultra-tight and shale reservoir rock samples. For this purpose, we developed a novel technique by integrating a custom-built in-situ miniature fluid-injection module with a non-destructive high-resolution X-ray imaging system. Small cylindrical core samples (15–60 µm in diameter) were prepared from reservoir rocks using Focused-Ion Beam (FIB) milling technique. The pore network inside the samples were first characterized using ultra-high resolution three-dimensional images obtained at initial state by X-ray nano-tomography (Nano-CT) and FIB-Scanning Electron Microscopy (FIB-SEM) techniques at the nano scale. The petrophysical parameters, including porosity, permeability, pore-size distribution, and organic content were computed for each sample using image analysis. We then performed series of imbibition experiments using brine, oil, and surfactant solutions on each core sample. We observed that both oil and brine phases spontaneously imbibe into the pore network of the rock samples at various quantities. We also, for the first time, examined fluid distribution in individual pores and found a complex wettability behavior at the pore scale in the reservoir rock samples. Three pore types were identified with water-wet, oil-wet, and fractionally-wet behaviors. This work opens a new path to developing an improved understanding of the pore-level physics involved in multi-phase flow and transport not only in tight rock samples but also in other nanoporous material used in different science and engineering applications.