Map Of Environment Research Articles

An Aerial Robotic Missing-Person Search in Urban Settings—A Probabilistic Approach

Autonomous robotic teams have been proposed for a variety of lost-person searches in wilderness and urban settings. In the latter scenarios, for missing persons, the application of such teams, however, is more challenging than it would be in the wilderness. This paper, specifically, examines the application of an autonomous team of unmanned aerial vehicles (UAVs) to perform a sparse, mobile-target search in an urban setting. A novel multi-UAV search-trajectory planning method, which relies on the prediction of the missing-person’s motion, given a known map of the search environment, is the primary focus. The proposed method incorporates periodic updates of the estimates of where the lost/missing person may be, allowing for intelligent re-coverage of previously searched areas. Additional significant contributions of this work include a behavior-based motion-prediction method for missing persons and a novel non-parametric estimator for iso-probability-based (missing-person-location) curves. Simulated experiments are presented to illustrate the effectiveness of the proposed search-planning method, demonstrating higher rates of missing-person detection and in shorter times compared to other methods.

Optimizing offshore wind export cable routing using GIS-based environmental heat maps

Abstract. In the United States, there are plans to produce up to 30 GW of offshore wind power by the year 2030, resulting in numerous seabed lease areas which are currently going through the leasing or construction and operations phase. A key challenge associated with offshore wind is optimal routing and installation of the subsea power cables, which transmit power from the main offshore wind energy production area to a land-based station, where it connects to the electrical grid. By traversing a vast extent of the seafloor, the installation and operational phases of subsea power cables have the potential to result in a range of environmental impacts, which may negatively affect sensitive biological, physical, human and/or cultural resource receptors. Presented here is a case study from southeastern North Carolina to identify optimal seabed cable routes and coastal landfalls for a recently leased offshore wind farm by using a combination of publicly available data, coupled with standard environmental impact assessment methodologies and geographic information system (GIS)-based heat maps. The study identified a range of high-risk areas, in addition to a number of potential low-risk routes and landfall areas which minimize seabed user conflicts and impacts on environmentally sensitive locations. Although additional high-resolution and site-specific environmental, geological and biological surveys are required to develop a robust cable installation plan, the preliminary steps from this research optimize early-phase marine spatial planning for offshore wind projects and other similar subsea industries.

Sound power level and spectrum of port sources for environmental noise mapping

Ports are noisy areas that are often located near populated centers, leading to disturbances for citizens. Neglecting these issues can result in health problems and committees that can impede the natural development of port economic activity, which is growing rapidly. Attention to the sector has increased in recent years, but mainly with studies devoted to the general impact on the population. There is currently insufficient information available regarding the inputs required to conduct noise mapping and assess its effects. This is due to the high quantity and diversity of sources present in the port area. Basing on measurements conducted following established and standardized procedures, the present work reports a database of sound power levels, third-octave band spectra, average duration, and source height for various types of equipment, including reach stackers, dock tractors, front lifts, forklifts, transtainers on wheels, container cranes, mobile cranes, and dry load handling scrapers, during their movements and cargo handling activities. Acousticians are expected to benefit from this detailed input data for noise mapping and environmental acoustic impact assessments.

Comprehensive Performance Evaluation between Visual SLAM and LiDAR SLAM for Mobile Robots: Theories and Experiments

SLAM (Simultaneous Localization and Mapping), primarily relying on camera or LiDAR (Light Detection and Ranging) sensors, plays a crucial role in robotics for localization and environmental reconstruction. This paper assesses the performance of two leading methods, namely ORB-SLAM3 and SC-LeGO-LOAM, focusing on localization and mapping in both indoor and outdoor environments. The evaluation employs artificial and cost-effective datasets incorporating data from a 3D LiDAR and an RGB-D (color and depth) camera. A practical approach is introduced for calculating ground-truth trajectories and during benchmarking, reconstruction maps based on ground truth are established. To assess the performance, ATE and RPE are utilized to evaluate the accuracy of localization; standard deviation is employed to compare the stability during the localization process for different methods. While both algorithms exhibit satisfactory positioning accuracy, their performance is suboptimal in scenarios with inadequate textures. Furthermore, 3D reconstruction maps established by the two approaches are also provided for direct observation of their differences and the limitations encountered during map construction. Moreover, the research includes a comprehensive comparison of computational performance metrics, encompassing Central Processing Unit (CPU) utilization, memory usage, and an in-depth analysis. This evaluation revealed that Visual SLAM requires more CPU resources than LiDAR SLAM, primarily due to additional data storage requirements, emphasizing the impact of environmental factors on resource requirements. In conclusion, LiDAR SLAM is more suitable for the outdoors due to its comprehensive nature, while Visual SLAM excels indoors, compensating for sparse aspects in LiDAR SLAM. To facilitate further research, a technical guide was also provided for the researchers in related fields.

An indigenous geographic position on producing data in colonial conditions

AbstractIn a historically colonial field, what are the possibilities of a geography informed by Indigenous and Anti‐Colonial ethics and onto‐epistemologies? This article suggests engaging in a critical study of data from an Indigenous geographic standpoint, with a focus on imperialism and colonialism in settler nation‐states. I begin by emphasizing the pervasive and long‐standing imposition of geographical data collection in Indigenous life, naming the binds of engaging with the production of data for and with colonial institutions. I then review prominent spatial analytics within critical Indigenous studies, Indigenous geography, and aligned Anti‐Colonial geography, including Indigenous place‐based knowledge and onto‐epistemologies, (racialized) colonial dispossession, sovereignty and recognition, environmental colonialism, and mapping and cartography. Last, I suggest that studies of colonial data dynamics and Indigenous data production strengthen Indigenous and Anti‐Colonial geographies by emphasizing the co‐constitution of good relations and good data. Future research avenues include the need to push beyond the geo‐historical bounds of the category settler colonial, and to build co‐rejections of racial empire with other fields of study including Black, Queer, and Feminist geographies.

Autonomous Exploration Method for Fast Unknown Environment Mapping by Using UAV Equipped With Limited FOV Sensor

Autonomous exploration is crucial and highly challenging for various intelligent operations of unmanned aerial vehicles (UAVs). However, low-efficiency problems caused by low-quality trajectory or back-and-forth maneuvers (BFM) still exist. To improve the exploration efficiency in unknown environments, a fast autonomous exploration planner (FAEP) is proposed in this paper. We firstly design a novel frontiers exploration sequence generation method to significantly reduce BFM during the exploration by considering multi-level factors in asymmetric traveling salesman problem (ATSP). Then, according to the exploration sequence and the distribution of frontiers, an adaptive yaw planning method is proposed to cover more frontiers by yaw change during an exploration journey. In addition, to increase the fluency of flight, a dynamic replanning strategy is also adopted. The main technical contributions of the proposed approach are to provide a comprehensive global coverage path with less BFM, and use adaptive yaw planning to generate more reasonable yaw trajectory. We present extensive comparison and real-world experiments to validate the effectiveness and correctness of our method. Experimental results show the proposed approach has better performance compared to typical and state-of-the-art methods in these flat scenarios without lots of scattered obstacles. We release our implementation as an open-source package <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{1}$</tex-math></inline-formula> for the community.

Physically‐Based Lighting for 3D Generative Models of Cars

AbstractRecent work has demonstrated that Generative Adversarial Networks (GANs) can be trained to generate 3D content from 2D image collections, by synthesizing features for neural radiance field rendering. However, most such solutions generate radiance, with lighting entangled with materials. This results in unrealistic appearance, since lighting cannot be changed and view‐dependent effects such as reflections do not move correctly with the viewpoint. In addition, many methods have difficulty for full, 360° rotations, since they are often designed for mainly front‐facing scenes such as faces. We introduce a new 3D GAN framework that addresses these shortcomings, allowing multi‐view coherent 360° viewing and at the same time relighting for objects with shiny reflections, which we exemplify using a car dataset. The success of our solution stems from three main contributions. First, we estimate initial camera poses for a dataset of car images, and then learn to refine the distribution of camera parameters while training the GAN. Second, we propose an efficient Image‐Based Lighting model, that we use in a 3D GAN to generate disentangled reflectance, as opposed to the radiance synthesized in most previous work. The material is used for physically‐based rendering with a dataset of environment maps. Third, we improve the 3D GAN architecture compared to previous work and design a careful training strategy that allows effective disentanglement. Our model is the first that generate a variety of 3D cars that are multi‐view consistent and that can be relit interactively with any environment map.

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India.

INTEGRATION OF STUMPF'S RATIO MODEL AND RANDOM FOREST FOR SATELLITE-DERIVED BATHYMETRY ESTIMATION

Abstract. The development of remote sensing for coastal and marine environment mapping has significantly enhanced our understanding of these ecosystems, enabling improved mitigation strategies against the impacts of human activities. However, remote sensing must consider the complex interplay of the atmosphere and water column. Ongoing research focuses on refining water column correction techniques, including Depth Invariant Indices (DII), Radiative Transfer models, and bathymetry models. This study specifically aims to enhance the Stumpf's Ratio model (SRM) for bathymetry by employing the Random Forest (RF) machine learning regression algorithm. The resulting bathymetry model, which incorporates visible bands from a Sentinel-2 MSI image, and their Stumpf's ratios, outperforms other methods, yielding the highest accuracy with RMSE and R2 values of 1.25 m and 0.854, respectively. This was followed by the multivariate SRM with RMSE and R2 values of 2.196 m and 0.554 respectively These findings demonstrate the promising potential of using RF machine learning regression with SRM for bathymetry modelling.

Conjunctive encoding of exploratory intentions and spatial information in the hippocampus

The hippocampus creates a cognitive map of the external environment by encoding spatial and self-motion-related information. However, it is unclear whether hippocampal neurons could also incorporate internal cognitive states reflecting an animal’s exploratory intention, which is not driven by rewards or unexpected sensory stimuli. In this study, a subgroup of CA1 neurons was found to encode both spatial information and animals’ investigatory intentions in male mice. These neurons became active before the initiation of exploration behaviors at specific locations and were nearly silent when the same fields were traversed without exploration. Interestingly, this neuronal activity could not be explained by object features, rewards, or mismatches in environmental cues. Inhibition of the lateral entorhinal cortex decreased the activity of these cells during exploration. Our findings demonstrate that hippocampal neurons may bridge external and internal signals, indicating a potential connection between spatial representation and intentional states in the construction of internal navigation systems.

Inspection Robot Navigation Based on Improved TD3 Algorithm.

The swift advancements in robotics have rendered navigation an essential task for mobile robots. While map-based navigation methods depend on global environmental maps for decision-making, their efficacy in unfamiliar or dynamic settings falls short. Current deep reinforcement learning navigation strategies can navigate successfully without pre-existing map data, yet they grapple with issues like inefficient training, slow convergence, and infrequent rewards. To tackle these challenges, this study introduces an improved two-delay depth deterministic policy gradient algorithm (LP-TD3) for local planning navigation. Initially, the integration of the long-short-term memory (LSTM) module with the Prioritized Experience Re-play (PER) mechanism into the existing TD3 framework was performed to optimize training and improve the efficiency of experience data utilization. Furthermore, the incorporation of an Intrinsic Curiosity Module (ICM) merges intrinsic with extrinsic rewards to tackle sparse reward problems and enhance exploratory behavior. Experimental evaluations using ROS and Gazebo simulators demonstrate that the proposed method outperforms the original on various performance metrics.

Reconstruction of Radio Environment Map Based on Multi-Source Domain Adaptive of Graph Neural Network for Regression.

The graph neural network (GNN) has shown outstanding performance in processing unstructured data. However, the downstream task performance of GNN strongly depends on the accuracy of data graph structural features and, as a type of deep learning (DL) model, the size of the training dataset is equally crucial to its performance. This paper is based on graph neural networks to predict and complete the target radio environment map (REM) through multiple complete REMs and sparse spectrum monitoring data in the target domain. Due to the complexity of radio wave propagation in space, it is difficult to accurately and explicitly construct the spatial graph structure of the spectral data. In response to the two above issues, we propose a multi-source domain adaptive of GNN for regression (GNN-MDAR) model, which includes two key modules: (1) graph structure alignment modules are used to capture and learn graph structure information shared by cross-domain radio propagation and extract reliable graph structure information for downstream reference signal receiving power (RSRP) prediction task; and (2) a spatial distribution matching module is used to reduce the feature distribution mismatch across spatial grids and improve the model's ability to remain domain invariant. Based on the measured REMs dataset, the comparative results of simulation experiments show that the GNN-MDAR outperforms the other four benchmark methods in accuracy when there is less RSRP label data in the target domain.

A Compact Handheld Sensor Package with Sensor Fusion for Comprehensive and Robust 3D Mapping.

This paper introduces an innovative approach to 3D environmental mapping through the integration of a compact, handheld sensor package with a two-stage sensor fusion pipeline. The sensor package, incorporating LiDAR, IMU, RGB, and thermal cameras, enables comprehensive and robust 3D mapping of various environments. By leveraging Simultaneous Localization and Mapping (SLAM) and thermal imaging, our solution offers good performance in conditions where global positioning is unavailable and in visually degraded environments. The sensor package runs a real-time LiDAR-Inertial SLAM algorithm, generating a dense point cloud map that accurately reconstructs the geometric features of the environment. Following the acquisition of that point cloud, we post-process these data by fusing them with images from the RGB and thermal cameras and produce a detailed, color-enriched 3D map that is useful and adaptable to different mission requirements. We demonstrated our system in a variety of scenarios, from indoor to outdoor conditions, and the results showcased the effectiveness and applicability of our sensor package and fusion pipeline. This system can be applied in a wide range of applications, ranging from autonomous navigation to smart agriculture, and has the potential to make a substantial benefit across diverse fields.

UAV‐based simultaneous localization and mapping in outdoor environments: A systematic scoping review

AbstractThis study aims to investigate the current knowledge of unmanned aerial vehicle (UAV)‐based simultaneous localization and mapping (SLAM) in outdoor environments and to discuss challenges and limitations in this field. A literature search was conducted in three online databases (Web of Science, Scopus, and IEEE) for articles published before October 2022 related to UAV‐based SLAM. A scoping review was carried out to identify the key concepts and applications, and discover research gaps in the use of algorithm‐oriented and task‐oriented, open‐source studies. A total of 97 studies met the criteria after conducting a two‐step screening by a systematic method followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses. Among eligible studies, 97 were classified into two main categories: algorithm‐oriented studies and task‐oriented studies. The analysis of the literature revealed that the majority of the studies were focused on the development and implementation of new algorithms and algorithms. This review highlights the significance and diversity of sensors utilized in UAVs in different tasks and applications scenarios that employ different types of sensors. The evaluation method is able to show the real results and performance of the new algorithms in the target scenarios compared with the evaluation method by the public data set and simulation platform.

3D Radiation Mapping Using Gaussian Process Regression with Intensity Projection

This article presents a novel approach for generating a three‐dimensional radiation map using data collected by mobile robots, aimed at monitoring radiation distribution in environments such as nuclear power plants. The proposed approach leverages Gaussian process regression with a novel adaptation of the inverse square law as a kernel function, which accurately reflects the physical characteristics of radiation, enabling precise mapping from uncertain data. Additionally, a method is proposed for constructing a comprehensive radiation map in 3D environments by estimating the radiation source and project the radiation data from sparse data. The effectiveness of the methodology is validated through simulations and experiments. Utilizing Octomap, a 3D spatial mapping tool, the study not only successfully visualizes radiation distribution in complex settings with multiple sources but also quantitatively demonstrates the enhanced accuracy of our approach compared to other existing methods. This research offers contributions in managing radiation risks, providing essential insights into the field.

Environmental risk analysis of a Ramsar site: a case study of east Kolkata wetlands with PSR framework.

The East Kolkata Wetlands (EKWT), designated as a Ramsar Site for its crucial role in sewage water purification, agriculture and pisciculture, faces escalating environmental threats due to rapid urbanisation. Employing the pressure-state-response (PSR) framework and Environmental Risk Assessment (ERA), this study spans three decades to elucidate the evolving dynamics of EKWT. Using Landsat TM and OLI images from 1991, 2001, 2011 and 2021, the research identifies key parameters within the PSR framework. Principal component analysis generates environmental risk maps, revealing a 46% increase in urbanisation, leading to reduced vegetation cover and altered land surface conditions. The spatial analysis, utilizing Getis-Ord Gi* statistics, pinpoints risk hotspots and coldspots in the EKWT region. Correlation analysis underscores a robust relationship between urbanisation, climatic response and environmental risk. Decadal ERA exposes a noteworthy surge in high-risk areas, indicating a deteriorating trend. Quantitative assessments pinpoint environmental risk hotspots, emphasizing the imperative for targeted conservation measures. The study establishes a direct correlation between environmental risk and air quality, underscoring the broader implications of EKWT's degradation. While acknowledging the East Kolkata administration's efforts, the research recognises its limitations and advocates a holistic, multidisciplinary approach for future investigations. Recommendations encompass the establishment of effective institutions, real-time monitoring, public engagement and robust anti-pollution measures. In offering quantitative insights, this study provides an evidence-based foundation for conservation strategies and sustainable management practices essential to safeguard the East Kolkata Wetlands.

Revolutionizing Depth Sensing: A Review Study of Apple LiDAR Sensor for as-built Scanning Applications

Incorporating the LiDAR sensor in the most recent Apple devices represents a substantial development in 3D mapping technology. Meanwhile, Apple's Lidar is still a new sensor. Therefore, this article reviews the potential uses of the Apple Lidar sensor in various fields, including engineering and construction, focusing on indoor and outdoor as-built 3D mapping and cultural heritage conservation. The affordable cost and shorter observation times compared to traditional surveying and other remote sensing techniques make the Apple Lidar an attractive choice among scholars and professionals. This article highlights the need for continued research on the Apple LiDAR sensor technology while discussing its specifications and limitations. A comprehensive review found that the Apple LiDAR sensor has shown promise in capturing 3D point clouds of small to medium-sized objects with exceptional detail. This technology offers a cost-effective and accessible option to scan areas faster and analyze data more quickly and automatically for 3D mapping and modelling in indoor and outdoor environments, particularly in areas with restricted access when using other traditional techniques. It also opens the door for more sophisticated applications in future studies, including cultural heritage conservation, archaeological investigations and feature detection, building health monitoring and many more.

LVIO-Fusion:Tightly-Coupled LiDAR-Visual-Inertial Odometry and Mapping in Degenerate Environments

LVIO-Fusion:Tightly-Coupled LiDAR-Visual-Inertial Odometry and Mapping in Degenerate Environments

A comprehensive overview of core modules in visual SLAM framework

Visual Simultaneous Localization and Mapping (VSLAM) technology has become a key technology in autonomous driving and robot navigation. Relying on camera sensors, VSLAM can provide a richer and more precise perception means, and its advancement has accelerated in recent years. However, current review studies are often limited to in-depth analysis of a specific module and lack a comprehensive review of the entire VSLAM framework. The VSLAM system consists of five core components: (1) The camera sensor module is responsible for capturing visual information about the surrounding environment. (2) The front-end module uses image data to roughly estimate the camera’s position and orientation. (3) The back-end module optimizes and processes the pose information estimated by the front-end. (4) The loop detection module is used to correct accumulated errors in the system. (5) The mapping module is responsible for generating environmental maps. This review provides a systematic and comprehensive analysis of the SLAM framework by taking the core components of VSLAM as the entry point. Deep learning brings new development opportunities for VSLAM, but it still needs to solve the problems of data dependence, cost and real-time in practical application. We deeply explore the challenges of combining VSLAM with deep learning and feasible solutions. This review provides a valuable reference for the development of VSLAM. This will help push VSLAM technology to become smarter and more efficient. Thus, it can better meet the needs of future intelligent autonomous systems in multiple fields.

Computing Metric Dimension of Two Types of Claw-free Cubic Graphs with Applications

Consider the simple connected graph G with vertex set V(G) and edge set E(G). A graph \(G\) can be resolved by \(R\) if each vertex’s representation of distances to the other vertices in \(R\) uniquely identifies it. The minimum cardinality of the set \(R\) is the metric dimension of \(G\). The length of the shortest path between any two vertices, x, y in V(G), is signified by the distance symbol d(x, y). An ordered k-tuple \(r(x/R)=(d(x,z_1),d(x,\ z_2),…,d(x,z_k))\) represents representation of \(x\) with respect to \(R\) for an ordered subset \(R={\{z}_1,z_2,z_3…,z_k\}\) of vertices and vertex \(x\) in a connected graph. Metric dimension is used in a wide range of contexts where connection, distance, and connectedness are essential factors. It facilitates understanding the structure and dynamics of complex networks and problems relating to robotics network design, navigation, optimization, and facility location. Robots can optimize their localization and navigation methods using a small number of reference sites due to the pertinent idea of metric dimension. As a result, many robotic applications, such as collaborative robotics, autonomous navigation, and environment mapping, are more accurate, efficient, and resilient. A claw-free cubic graph (CCG) is one in which no induced subgraph is a claw. CCG proves helpful in various fields, including optimization, network design, and algorithm development. They offer intriguing structural and algorithmic properties. Developing algorithms and results for claw-free graphs frequently has applications in solving of challenging real-world situations. The metric dimension of a couple of claw-free cubic graphs (CCG), a string of diamonds (SOD), and a ring of diamonds (ROD) will be determined in this work.