Prior Map Research Articles

C${}^{*}$: Cross-Modal Simultaneous Tracking and Rendering for 6-DoF Monocular Camera Localization Beyond Modalities

We present a monocular camera localization technique for a three-dimensional prior map. Visual localization has been attracting considerable attention as a lightweight and widely available localization technique for any mobilities; however, it still suffers from appearance changes and a high computational cost. With a view to achieving robust and real-time visual localization, we first reduce the localization problem to alternate local tracking and occasional keyframe rendering by following a simultaneous tracking and rendering algorithm. At the same time, by using an information-theoretic metric denoted normalized information distance in the local tracking, we developed a 6-DoF localization method robust to intensity variations between modalities and varying sensor properties. We quantitatively evaluated the accuracy and robustness of our method using both synthetic and real datasets and achieved reliable and practical localization even in the case of extreme appearance changes.

Gaussian Mixture Model and Self-Organizing Map Neural-Network-Based Coverage for Target Search in Curve-Shape Area.

This article focuses on the target search problem in a curve-shape area using multiple unmanned aerial vehicles (UAVs), with the demand for obtaining the maximum cumulative detection reward, as well as the constraint of maneuverability and obstacle avoidance. First, the prior target probability map of the curve-shape area, generated by Parzen windows with Gaussian kernels, is approximated by the 1-D Gaussian mixture model (GMM) in order to extract some high-value curve segments corresponding to Gaussian components. Based on the parameterized curve segments from GMM, the self-organizing map (SOM) neural network is then established to achieve the coverage search. The step of winner neuron selection in SOM will prioritize and allocate the curve segments to UAVs, with the comprehensive consideration of multiple evaluation factors and allocation balance. The following step of neuron weight update will plan the UAV paths under the constraint of maneuverability and obstacle avoidance, using the modified Dubins guidance vector field. Finally, the good performance of GMM-SOM is evaluated on a coastline map.

Intelligent Vehicle Self-Localization Based on Double-Layer Features and Multilayer LIDAR

High-precision self-localization is one of the basic functions of intelligent vehicles. Moreover, location information is necessary prior information for tasks such as behavioural decision-making and path planning. Recently, the technology of map-based localization has been widely concerned due to its accuracy. A prior map for localization stores one or more environmental features extracted by on-board sensors. These features represent fundamental information about the ground environment such as curbs and lane markings, or the vertical environment such as building outlines. In order to enhance the robustness and positioning accuracy, this paper uses two layers of features to express the environment: one is a bottom layer composed of ground and curb features, and the other is an upper layer composed of a 2D point cloud with vertical features. Firstly, a novel collision-based detection method is proposed to extract the curb features, and a vertical-projection-based detection algorithm is used to detect vertical feature points from multilayer LIDAR. Then, the fusion of a multi-frame feature point cloud is used to represent the rich and complete information on the environment of the intelligent vehicle. Finally, the Monte Carlo localization algorithm is used to obtain an optimal estimate of the vehicle position. The experimental results indicate that the proposed localization algorithm can realize localization accuracy.

FloorVLoc: A Modular Approach to Floorplan Monocular Localization

Intelligent vehicles for search and rescue, whose mission is assisting emergency personnel by visually exploring an unfamiliar building, require accurate localization. With GPS not available, and approaches relying on new infrastructure installation, artificial landmarks, or pre-constructed dense 3D maps not feasible, the question is whether there is an approach which can combine ubiquitous prior map information with a monocular camera for accurate positioning. Enter FloorVLoc—Floorplan Vision Vehicle Localization. We provide a means to integrate a monocular camera with a floorplan in a unified and modular fashion so that any monocular visual Simultaneous Localization and Mapping (SLAM) system can be seamlessly incorporated for global positioning. Using a floorplan is especially beneficial since walls are geometrically stable, the memory footprint is low, and prior map information is kept at a minimum. Furthermore, our theoretical analysis of the visual features associated with the walls shows how drift is corrected. To see this approach in action, we developed two full global positioning systems based on the core methodology introduced, operating in both Monte Carlo Localization and linear optimization frameworks. Experimental evaluation of the systems in simulation and a challenging real-world environment demonstrates that FloorVLoc performs with an average error of 0.06 m across 80 m in real-time.

Adding Measurement Error to Location Data to Protect Subject Confidentiality While Allowing for Consistent Estimation of Exposure Effects

Summary In public use data sets, it is desirable not to report a respondent's location precisely to protect subject confidentiality. However, the direct use of perturbed location data to construct explanatory exposure variables for regression models will generally make naive estimates of all parameters biased and inconsistent. We propose an approach where a perturbation vector, consisting of a random distance at a random angle, is added to a respondent's reported geographic co-ordinates. We show that, as long as the distribution of the perturbation is public and there is an underlying prior population density map, external researchers can construct unbiased and consistent estimates of location-dependent exposure effects by using numerical integration techniques over all possible actual locations, although coefficient confidence intervals are wider than if the true location data were known. We examine our method by using a Monte Carlo simulation exercise and apply it to a real world example using data on perceived and actual distance to a health facility in Tanzania.

" Improving Knowledge Structure through Extended Scratch-Build Concept Mapping"

Extended concept mapping is a potential technique to elicit missing ideas and relationships. This study explored an Extended Scratch-Build (ESB) concept mapping in improving learners' knowledge structure. ESB is an expansion of an open-ended approach that asks students to connect the prior-existing original concept map with the new additional map on related material topics. The practical use of ESB has been conducted and proves positive effects on learning achievements. However, no information has been provided regarding the performance of the concept map components that describe the broad overview of students' attainments. This observational study focused on improving learners' knowledge using quantity measurements based on concept map features, consists of a number of concepts, number of links, and a number of propositions. University students (N = 27) with a major in Informatics Engineering participated in the study. The results reported that students' knowledge structure on additional maps significantly increased compared to the prior original map.

Bagging-based saliency distribution learning for visual saliency detection

Saliency detection is still very challenging in computer vision and image processing. In this paper, we propose a novel visual saliency detection framework via bagging-based saliency distribution learning (BSDL). Given an input image, we firstly segment it into superpixels as basic units. Then two prior knowledge containing background prior and center prior are integrated to generate an initial prior map, which is used to select training samples from all superpixels to train the BSDL model. Specifically, the BSDL contains two stages: In the first stage, we use bagging-based sampling method to train K saliency classifiers from all training samples. K saliency classifiers are used to predict each superpixel saliency value. In the second stage, we aim to learn a saliency distribution model, whose goal is to infer the relationship between each classifier and each superpixel. i.e., for each superpixel, the BSDL not only trains K saliency classifiers to predict its saliency value, but also infers the reliability of using each saliency classifier to predict its saliency value. As a result, each superpixel’s saliency value is determined by its K prediction saliency values and saliency distribution. After the BSDL, we propose a so called foreground consistency saliency optimization framework (FCSO) to further refine saliency map obtained by BSDL. To improve computation efficiency, a prejudgment rule is proposed to evaluate the quality of saliency map obtained by BSDL, which is used to decide whether the FCSO is needed for input image. Experimental results on four public datasets demonstrate the superiority of the proposed method than other state-of-the-art methods.

Search for Static Target in Nonwide Area by AUV: A Prior Data-Driven Strategy

This article presents a prior data-driven strategy to solve the motion planning problem of autonomous underwater vehicle (AUV) for the target search mission. Compared with the widely studied cases over wide marine area, however, this article focuses on searching static target that exists in nonwide area. First, under the assumption that the nonwide area could be regarded as a combination of lines or curves, we utilize the Parzen windows theory with one-dimensional Gaussian kernels to generate the prior probability map of target based on the previous qualitative data. Then, we use the “global warming” method to extract some valuable subregions quantitatively, which will be visited by AUV in the optimal sequence. Finally, we verify the good performance of our method by an example of underwater pipeline inspection.

Functional Magnetic Resonance Imaging Connectivity Accurately Distinguishes Cases With Psychotic Disorders From Healthy Controls, Based on Cortical Features Associated With Brain Network Development

BackgroundMachine learning (ML) can distinguish cases with psychotic disorder from healthy controls based on magnetic resonance imaging (MRI) data, but it is not yet clear which MRI metrics are the most informative for case-control ML, or how ML algorithms relate to the underlying biology. MethodsWe analyzed multimodal MRI data from 2 independent case-control studies of psychotic disorders (cases, n = 65, 28; controls, n = 59, 80) and compared ML accuracy across 5 selected MRI metrics from 3 modalities. Cortical thickness, mean diffusivity, and fractional anisotropy were estimated at each of 308 cortical regions, as well as functional and structural connectivity between each pair of regions. Functional connectivity data were also used to classify nonpsychotic siblings of cases (n = 64) and to distinguish cases from controls in a third independent study (cases, n = 67; controls, n = 81). ResultsIn both principal studies, the most informative metric was functional MRI connectivity: The areas under the receiver operating characteristic curve were 88% and 76%, respectively. The cortical map of diagnostic connectivity features (ML weights) was replicable between studies (r = 0.27, p < .001); correlated with replicable case-control differences in functional MRI degree centrality and with a prior cortical map of adolescent development of functional connectivity; predicted intermediate probabilities of psychosis in siblings; and was replicated in the third case-control study. ConclusionsML most accurately distinguished cases from controls by a replicable pattern of functional MRI connectivity features, highlighting abnormal hubness of cortical nodes in an anatomical pattern consistent with the concept of psychosis as a disorder of network development.

Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy

Small target detection is a crucial technique that restricts the performance of many infrared imaging systems. In this paper, a novel detection model of infrared small target via non-convex tensor rank surrogate joint local contrast energy (NTRS) is proposed. To improve the latest infrared patch-tensor (IPT) model, a non-convex tensor rank surrogate merging tensor nuclear norm (TNN) and the Laplace function, is utilized for low rank background patch-tensor constraint, which has a useful property of adaptively allocating weight for every singular value and can better approximate l 0 -norm. Considering that the local prior map can be equivalent to the saliency map, we introduce a local contrast energy feature into IPT detection framework to weight target tensor, which can efficiently suppress the background and preserve the target simultaneously. Besides, to remove the structured edges more thoroughly, we suggest an additional structured sparse regularization term using the l 1 , 1 , 2 -norm of third-order tensor. To solve the proposed model, a high-efficiency optimization way based on alternating direction method of multipliers with the fast computing of tensor singular value decomposition is designed. Finally, an adaptive threshold is utilized to extract real targets of the reconstructed target image. A series of experimental results show that the proposed method has robust detection performance and outperforms the other advanced methods.

Anthropogenic Biomes: 10,000 BCE to 2015 CE

Human populations and their use of land have reshaped landscapes for thousands of years, creating the anthropogenic biomes (anthromes) that now cover most of the terrestrial biosphere. Here we introduce the first global reconstruction and mapping of anthromes and their changes across the 12,000-year interval from 10,000 BCE to 2015 CE; the Anthromes 12K dataset. Anthromes were mapped using gridded global estimates of human population density and land use from the History of the Global Environment database (HYDE version 3.2) by a classification procedure similar to that used for prior anthrome maps. Anthromes 12K maps generally agreed with prior anthrome maps for the same time periods, though significant differences were observed, including a substantial reduction in Rangelands anthromes in 2000 CE but with increases before that time. Differences between maps resulted largely from improvements in HYDE’s representation of land use, including pastures and rangelands, compared with the HYDE 3.1 input data used in prior anthromes maps. The larger extent of early land use in Anthromes 12K also agrees more closely with empirical assessments than prior anthrome maps; the result of an evidence-based paradigm shift in characterizing the history of Earth’s transformation through land use, from a mostly recent large-scale conversion of uninhabited wildlands, to a long-term trend of increasingly intensive transformation and use of already inhabited and used landscapes. The spatial history of anthropogenic changes depicted in Anthromes 12K remain to be validated, especially for earlier time periods. Nevertheless, Anthromes 12K is a major advance over all prior anthrome datasets and provides a new platform for assessing the long-term environmental consequences of human transformation of the terrestrial biosphere.

MagSLAM: Aerial simultaneous localization and mapping using Earth's magnetic anomaly field

Instances of spoofing and jamming of global navigation satellite systems (GNSSs) have emphasized the need for alternative navigation methods. Aerial navigation by magnetic map matching has been demonstrated as a viable GNSS-alternative navigation technique. Flight test demonstrations have achieved accuracies of tens of meters over hour-long flights, but these flights required accurate magnetic maps which are not always available.Magneticmap availability and resolution vary widely around the globe. Removing the dependency on prior survey maps extends the benefits of aerial magnetic navigation methods to small unmanned aerial systems (sUAS) at lower altitudes where magnetic maps are especially undersampled or unavailable. In this paper, a simultaneous localization andmapping (SLAM) algorithm known as FastSLAM was modified to use scalar magnetic measurements to constrain a drifting inertial navigation system (INS). The algorithm was then demonstrated on real magnetic navigation flight test data. Similar in performance to the map-based approach, MagSLAM achieved tens of meters accuracy in a 100-minute flight without the use of a prior magnetic map. Aerial SLAM using Earth's magnetic anomaly field provides a GNSS-alternative navigation method that is globally persistent, impervious to jamming or spoofing, stealthy, and locally accurate to tens of meters without the need for a magnetic map.

Conservative and disruptive modes of adolescent change in human brain functional connectivity.

Adolescent changes in human brain function are not entirely understood. Here, we used multiecho functional MRI (fMRI) to measure developmental change in functional connectivity (FC) of resting-state oscillations between pairs of 330 cortical regions and 16 subcortical regions in 298 healthy adolescents scanned 520 times. Participants were aged 14 to 26 y and were scanned on 1 to 3 occasions at least 6 mo apart. We found 2 distinct modes of age-related change in FC: "conservative" and "disruptive." Conservative development was characteristic of primary cortex, which was strongly connected at 14 y and became even more connected in the period from 14 to 26 y. Disruptive development was characteristic of association cortex and subcortical regions, where connectivity was remodeled: connections that were weak at 14 y became stronger during adolescence, and connections that were strong at 14 y became weaker. These modes of development were quantified using the maturational index (MI), estimated as Spearman's correlation between edgewise baseline FC (at 14 y, [Formula: see text]) and adolescent change in FC ([Formula: see text]), at each region. Disruptive systems (with negative MI) were activated by social cognition and autobiographical memory tasks in prior fMRI data and significantly colocated with prior maps of aerobic glycolysis (AG), AG-related gene expression, postnatal cortical surface expansion, and adolescent shrinkage of cortical thickness. The presence of these 2 modes of development was robust to numerous sensitivity analyses. We conclude that human brain organization is disrupted during adolescence by remodeling of FC between association cortical and subcortical areas.

Multimodal localization: Stereo over LiDAR map

AbstractIn this paper, we present a real‐time high‐precision visual localization system for an autonomous vehicle which employs only low‐cost stereo cameras to localize the vehicle with a priori map built using a more expensive 3D LiDAR sensor. To this end, we construct two different visual maps: a sparse feature visual map for visual odometry (VO) based motion tracking, and a semidense visual map for registration with the prior LiDAR map. To register two point clouds sourced from different modalities (i.e., cameras and LiDAR), we leverage probabilistic weighted normal distributions transformation (ProW‐NDT), by particularly taking into account the uncertainty of source point clouds. The registration results are then fused via pose graph optimization to correct the VO drift. Moreover, surfels extracted from the prior LiDAR map are used to refine the sparse 3D visual features that will further improve VO‐based motion estimation. The proposed system has been tested extensively in both simulated and real‐world experiments, showing that robust, high‐precision, real‐time localization can be achieved.

Managing Localization Uncertainty to Handle Semantic Lane Information from Geo-Referenced Maps in Evidential Occupancy Grids

Occupancy grid is a popular environment model that is widely applied for autonomous navigation of mobile robots. This model encodes obstacle information into the grid cells as a reference of the space state. However, when navigating on roads, the planning module of an autonomous vehicle needs to have semantic understanding of the scene, especially concerning the accessibility of the driving space. This paper presents a grid-based evidential approach for modeling semantic road space by taking advantage of a prior map that contains lane-level information. Road rules are encoded in the grid for semantic understanding. Our approach focuses on dealing with the localization uncertainty, which is a key issue, while parsing information from the prior map. Readings from an exteroceptive sensor are as well integrated in the grid to provide real-time obstacle information. All the information is managed in an evidential framework based on Dempster–Shafer theory. Real road results are reported with qualitative evaluation and quantitative analysis of the constructed grids to show the performance and the behavior of the method for real-time application.

Autonomous aerial cinematography in unstructured environments with learned artistic decision‐making

AbstractAerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely taxing, often requiring multiple expert human operators. Hence, there is a demand for an autonomous cinematographer that can reason about both geometry and scene context in real‐time. Existing approaches do not address all aspects of this problem; they either require high‐precision motion‐capture systems or global positioning system tags to localize targets, rely on prior maps of the environment, plan for short time horizons, or only follow fixed artistic guidelines specified before the flight. In this study, we address the problem in its entirety and propose a complete system for real‐time aerial cinematography that for the first time combines: (a) vision‐based target estimation; (b) 3D signed‐distance mapping for occlusion estimation; (c) efficient trajectory optimization for long time‐horizon camera motion; and (d) learning‐based artistic shot selection. We extensively evaluate our system both in simulation and in field experiments by filming dynamic targets moving through unstructured environments. Our results indicate that our system can operate reliably in the real world without restrictive assumptions. We also provide in‐depth analysis and discussions for each module, with the hope that our design tradeoffs can generalize to other related applications. Videos of the complete system can be found at https://youtu.be/ookhHnqmlaU.

MapLite: Autonomous Intersection Navigation Without a Detailed Prior Map

In this work, we present MapLite: a one-click autonomous navigation system capable of piloting a vehicle to an arbitrary desired destination point given only a sparse publicly available topometric map (from OpenStreetMap). The onboard sensors are used to segment the road region and register the topometric map in order to fuse the high-level navigation goals with a variational path planner in the vehicle frame. This enables the system to plan trajectories that correctly navigate road intersections without the use of an external localization system such as GPS or a detailed prior map. Since the topometric maps already exist for the vast majority of roads, this solution greatly increases the geographical scope for autonomous mobility solutions. We implement MapLite on a full-scale autonomous vehicle and exhaustively test it on over 15 km of road including over 100 autonomous intersection traversals. We further extend these results through simulated testing to validate the system on complex road junction topologies such as traffic circles.

Classification of Anomalous Pixels in the Focal Plane Arrays of Orbiting Carbon Observatory-2 and -3 via Machine Learning

A machine learning approach was developed to improve the bad pixel maps that mask damaged or unusable pixels in the imaging spectrometers of National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) and Orbiting Carbon Observatory-3 (OCO-3). The OCO-2 and OCO-3 instruments use nearly 500,000 pixels to record high resolution spectra in three infrared wavelength ranges. These spectra are analyzed to retrieve estimates of the column-average carbon dioxide (XCO 2) concentration in Earth’s atmosphere. To meet mission requirements, these XCO 2 estimates must have accuracies exceeding 0.25%, and small uncertainties in the bias or gain of even one detector pixel can add significant error to the retrieved XCO 2 estimates. Thus, anomalous pixels are identified and removed from the data stream by applying a bad pixel map prior to further processing. To develop these maps, we first characterize each pixel’s behavior through a collection of interpretable and statistically well-defined metrics. These features and a prior map are then used as inputs in a Random Forest classifier to assign a likelihood that a given pixel is bad. Consequently, the likelihoods are analyzed and thresholds are chosen to produce a new bad pixel map. The machine learning approach adopted here has improved data quality by identifying hundreds of new bad pixels in each detector. Such an approach can be generalized to other instruments that require independent calibration of many individual elements.

Convolutional neural network-based coarse initial position estimation of a monocular camera in large-scale 3D light detection and ranging maps

Initial position estimation in global maps, which is a prerequisite for accurate localization, plays a critical role in mobile robot navigation tasks. Global positioning system signals often become unreliable in disaster sites or indoor areas, which require other localization methods to help the robot in searching and rescuing. Many visual-based approaches focus on estimating a robot’s position within prior maps acquired with cameras. In contrast to conventional methods that need a coarse estimation of initial position to precisely localize a camera in a given map, we propose a novel approach that estimates the initial position of a monocular camera within a given 3D light detection and ranging map using a convolutional neural network with no retraining is required. It enables a mobile robot to estimate a coarse position of itself in 3D maps with only a monocular camera. The key idea of our work is to use depth information as intermediate data to retrieve a camera image in immense point clouds. We employ an unsupervised learning framework to predict the depth from a single image. Then we use a pretrained convolutional neural network model to generate depth image descriptors to construct representations of the places. We retrieve the position by computing similarity scores between the current depth image and the depth images projected from the 3D maps. Experiments on the publicly available KITTI data sets have demonstrated the efficiency and feasibility of the presented algorithm.

Integrating manifold ranking with boundary expansion and corners clustering for saliency detection of home scene

In this paper, we propose a novel framework for saliency detection of home scene by exploiting manifold ranking, boundary expansion, and corners clustering. Our proposed method firstly combines color cues in RGB and CIELab to select image boundary seeds, and exclude the ones which might be located at salient objects as much as possible. Then, we utilize the boundary seeds on each image boundary as the queries of manifold ranking to compute saliency and integrate them for a background-based saliency map. For the foreground-based saliency detection. Boundary expansion combined with background-based saliency map highlights foreground regions, which are regarded as queries for a foreground-based saliency map. Moreover, we achieve center prior saliency map through multi-scale Harris corner detection and corners clustering to further highlight salient regions and suppress background regions. Finally, we integrate the three saliency maps via the proposed unified framework for a more accurate and smooth saliency map. Both qualitative and quantitative experimental results indicate that our proposed method can deliver better performance than several state-of-the-art saliency detection methods as a whole.