Map Of Environment Research Articles

From concept to application: building and testing a low-cost light detection and ranging system for small mobile robots using time-of-flight sensors

Advancements in light detection and ranging (LiDAR) technology have significantly improved robotics and automated navigation. However, the high cost of traditional LiDAR sensors restricts their use in small-scale robotic projects. This paper details the development of a low-cost LiDAR prototype for small mobile robots, using time-of-flight (ToF) sensors as a cost-effective alternative. Integrated with an ESP32 microcontroller for real-time data processing and Wi-Fi connectivity, the prototype facilitates accurate distance measurement and environmental mapping, crucial for autonomous navigation. Our approach included hardware design and assembly, followed by programming the ToF sensors and ESP32 for data collection and actuation. Experiments validated the accuracy of the ToF sensors under static, dynamic, and varied lighting conditions. Results show that our low-cost system achieves accuracy and reliability comparable to more expensive options, with an average mapping error within acceptable limits for practical use. This work offers a blueprint for affordable LiDAR systems, expanding access to technology for research and education, and demonstrating the viability of ToF sensors in economical robotic navigation and mapping solutions.

A novel deep learning algorithm for broad scale seagrass extent mapping in shallow coastal environments

A novel deep learning algorithm for broad scale seagrass extent mapping in shallow coastal environments

A scoping review of Indigenous Cultural Mapping of coastal, island, and marine environments

A scoping review of Indigenous Cultural Mapping of coastal, island, and marine environments

Optical beam steering and distance measurement experiments through an optical phased array and a 3D printed lens

We present beam steering experiments based on the wavelength tuning of edge-coupled 1D optical phased arrays (OPAs) on a 3 µm silicon on insulator (SOI) platform. Two versions of 512-channel OPA with different pitch values, namely 2 µm and 3 µm, are designed, fabricated, and characterized. For the 2 µm pitch, the width of the output array is 1 mm, steering sensitivity is measured to be 1°/nm, and the maximum beam steering angle is 45°. For the 3 µm pitch, the output array is 1.5 mm wide, the steering sensitivity is 0.6°/nm, and the maximum beam steering angle is 30°. Since the chip doesn’t offer vertical collimation, both a cylindrical (2D-shaped) lens and a 3D printed (3D-shaped) lens were tested with the OPA chips. The high number of output channels results in a wide width of the output array, therefore enabling frequency-modulated continuous-wave (FMCW) distance measurements up to a few meters. The ability to achieve precise steering angles and distance measurements with up to 5 cm accuracy improves the efficiency of light detection and ranging (LiDAR) systems in various fields, such as autonomous vehicles, robotics, and environmental mapping.

Human Prior Knowledge Estimation from Movement Cues for Information-Based Control of Mobile Robots during Search

Robotic search often involves teleoperating vehicles into unknown environments. In such scenarios, prior knowledge of target location or environmental map may be a viable resource to tap into and control other autonomous robots in the vicinity towards an improved search performance. In this article, we test the hypothesis that despite having the same skill, prior knowledge of target or environment affects teleoperator actions, and such knowledge can therefore be inferred through robot movement. To investigate whether prior knowledge can improve human-robot team performance, we next evaluate an adaptive mutual-information blending strategy that admits a time-dependent weighting for steering autonomous robots. Human-subject experiments show that several features including distance travelled by the teleoperated robot, time spent staying still, speed, and turn rate, all depend on the level of prior knowledge and that absence of prior knowledge increased workload. Building on these results, we identified distance travelled and time spent staying still as movement cues that can be used to robustly infer prior knowledge. Simulations where an autonomous robot accompanied a human teleoperated robot revealed that whereas time to find the target was similar across all information-based search strategies, adaptive strategies that acted on movement cues found the target sooner more often than a single human teleoperator compared to non-adaptive strategies. This gain is diluted with number of robots, likely due to the limited size of the search environment. Results from this work set the stage for developing knowledge-aware control algorithms for autonomous robots in collaborative human-robot teams.

TC-Mapper: tightly-coupled LiDAR-inertial SLAM for robotic mapping in complex environments

TC-Mapper: tightly-coupled LiDAR-inertial SLAM for robotic mapping in complex environments

Hepatitis nursing robot based on RP-lidarA1 sensor

Background To alleviate the workload of medical staff and provide personalized care for hepatitis patients, this study focuses on developing a hepatitis care robot. Objective The objective of this study is to integrate the RP-lidarA1 sensor into a hepatitis care robot to achieve high-precision environmental perception, mapping, and navigation, thereby improving healthcare services. Methods The RP-lidarA1 sensor was utilized for environmental scanning, and the MPU6050 chip was used to collect attitude data. An improved RBPF-SLAM algorithm was employed for high-precision map construction. For positioning and navigation, a combination of the A* algorithm and Dynamic Window Approach (DWA) algorithm was used to optimize path planning and obstacle avoidance. Results Simulation experiments demonstrated that the improved algorithm reduced the number of particles to 50 in a 140 m2 area and shortened the map construction time to 1200 s. The A* algorithm effectively planned optimal paths, while the DWA algorithm improved navigation efficiency. Satisfaction surveys indicated that 92.4% of hepatitis patients and 81.8% of nurses were highly satisfied with the robot's performance. Conclusions The hepatitis care robot integrating the RP-lidarA1 sensor showed excellent performance in autonomous navigation, map construction, and obstacle avoidance, significantly enhancing the quality and efficiency of medical services.

Unmanned Aerial Vehicle-Enabled Aerial Radio Environment Map Construction: A Multi-Stage Approach to Data Sampling and Path Planning

Unmanned Aerial Vehicle-Enabled Aerial Radio Environment Map Construction: A Multi-Stage Approach to Data Sampling and Path Planning

Underwater DVL Optimization Network (UDON): A Learning-Based DVL Velocity Optimizing Method for Underwater Navigation

As the exploration of marine resources continues to deepen, the utilization of Autonomous Underwater Vehicles (AUVs) for conducting marine resource surveys and underwater environmental mapping has become a common practice. In order to successfully accomplish exploration missions, AUVs require high-precision underwater navigation information as support. A Strapdown Inertial Navigation System (SINS) can provide AUVs with accurate attitude and heading information, while a Doppler Velocity Log (DVL) is capable of measuring the velocity vector of the AUVs. Therefore, the integrated SINS/DVL navigation system can furnish the necessary navigational information required by an AUV. In response to the issue of DVL being susceptible to external environmental interference, leading to reduced measurement accuracy, this paper proposes an end-to-end deep-learning approach to enhance the accuracy of DVL velocity vector measurements. The utilization of the raw measurement data from an Inertial Measurement Unit (IMU), which includes gyroscopes and accelerometers, to assist the DVL in velocity vector estimation and to refine it towards the Global Positioning System (GPS) velocity vector, compensates for the external environmental interference affecting the DVL, therefore enhancing the navigation accuracy. To evaluate the proposed method, we conducted lake experiments using SINS and DVL equipment, from which the collected data were organized into a dataset for training and assessing the model. The research results show that the DVL vector predicted by our model can achieve a maximum improvement of 69.26% in terms of root mean square error and a maximum improvement of 78.62% in terms of relative trajectory error.

Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia

This study highlights the vital role of high-resolution (HR), open-source land cover maps for food security, land use planning, and environmental protection. The scarcity of freely available HR datasets underscores the importance of multi-spectral HR aerial images. We used unmanned aerial vehicle (UAV) to capture images for a centimeter-level orthomosaics, facilitating advanced remote sensing and spatial analysis. Our method compares the efficacy and accuracy of object-based image analysis (OBIA) combined with random forest and convolutional neural networks (CNN) for land cover classification. We produced detailed land cover maps for 27 varied landscapes across Serbia, identifying nine unique land cover classes and assessing human impact on natural habitats. This resulted in a valuable dataset of HR multi-spectral orthomosaics across ecological zones, alongside land cover classification with extensive metrics and training data for each site. This dataset is a valuable resource for researchers working on habitats mapping and assessment for biodiversity monitoring studies on one side and researchers working on novel machine learning methods for land cover classification.

Event-Based Visual Simultaneous Localization and Mapping (EVSLAM) Techniques: State of the Art and Future Directions

Recent advances in event-based cameras have led to significant developments in robotics, particularly in visual simultaneous localization and mapping (VSLAM) applications. This technique enables real-time camera motion estimation and simultaneous environment mapping using visual sensors on mobile platforms. Event cameras offer several distinct advantages over frame-based cameras, including a high dynamic range, high temporal resolution, low power consumption, and low latency. These attributes make event cameras highly suitable for addressing performance issues in challenging scenarios such as high-speed motion and environments with high-range illumination. This review paper delves into event-based VSLAM (EVSLAM) algorithms, leveraging the advantages inherent in event streams for localization and mapping endeavors. The exposition commences by explaining the operational principles of event cameras, providing insights into the diverse event representations applied in event data preprocessing. A crucial facet of this survey is the systematic categorization of EVSLAM research into three key parts: event preprocessing, event tracking, and sensor fusion algorithms in EVSLAM. Each category undergoes meticulous examination, offering practical insights and guidance for comprehending each approach. Moreover, we thoroughly assess state-of-the-art (SOTA) methods, emphasizing conducting the evaluation on a specific dataset for enhanced comparability. This evaluation sheds light on current challenges and outlines promising avenues for future research, emphasizing the persisting obstacles and potential advancements in this dynamically evolving domain.

Demographic inequities and cumulative environmental burdens within communities near superfund sites on Long Island, New York.

Nassau and Suffolk Counties of Long Island, New York are densely populated and contain 34 federally-designated and 449 state-designated Superfund sites, potentially exposing communities to toxic releases. We conducted a distributive justice analysis assessing proximity to Superfund sites, community socio-demographics, and other environmental burdens. Socio-demographic and environmental variables for 665 census tracts were obtained from the United States Census and Environmental Protection Agency's Environmental Justice Screening and Mapping Tool. Hierarchical Bayesian spatial Poisson regression models evaluated the relationship between socio-demographic and environmental variables and counts of Superfund sites per census tract. Analyses were further stratified by county and site type (Federal versus State). A 10% increase in low-income residents was associated with a 47% increase in Superfund sites (Risk Ratio [RR]: 1.47; 95% credible interval (CI): 1.20-1.81). A 10% increase in Hispanic/Latino residents was associated with a 20% increase (RR: 1.20; 95%CI: 1.02-1.42). Higher PM2.5 concentrations (RR:1.64, 95% CI: 1.09-2.48), higher toxic air releases (RR: 1.27, 95%CI: 1.03-1.61), and greater proximity to underground gas storage tanks (RR: 1.27, 95%CI: 1.09-1.48) were associated with increases in Superfund counts. Stratified analyses revealed that low-income residents are concentrated near state not federal Superfund sites. County stratification found that only Suffolk County residents near Superfund sites have increased lead exposure potential, and Black residents in Suffolk (not Nassau) were more likely to live near Superfund sites. We observed localized distributive inequities in community demographics near Superfund sites on Long Island, and communities near Superfund sites are more likely to experience other environmental burdens.

Parallelized SLAM: Enhancing Mapping and Localization Through Concurrent Processing.

Simultaneous Localization and Mapping (SLAM) systems face high computational demands, hindering their real-time implementation on low-end computers. An approach to addressing this challenge involves offline processing, i.e., a map of the environment map is created offline on a powerful computer and then passed to a low-end computer, which uses it for navigation, which involves fewer resources. However, even creating the map on a powerful computer is slow since SLAM is designed as a sequential process. This work proposes a parallel mapping method pSLAM for speeding up the offline creation of maps. In pSLAM, a video sequence is partitioned into multiple subsequences, with each processed independently, creating individual submaps. These submaps are subsequently merged to create a unified global map of the environment. Our experiments across a diverse range of scenarios demonstrate an increase in the processing speed of up to 6 times compared to that of the sequential approach while maintaining the same level of robustness. Furthermore, we conducted comparative analyses against state-of-the-art SLAM methods, namely UcoSLAM, OpenVSLAM, and ORB-SLAM3, with our method outperforming these across all of the scenarios evaluated.

Particle-based Instance-aware Semantic Occupancy Mapping in Dynamic Environments

Particle-based Instance-aware Semantic Occupancy Mapping in Dynamic Environments

Atlas for sustainable Egyptian governorates buildings based on wind/solar potential: Power, efficiency, economic, environmental, and thermal comfort maps

Atlas for sustainable Egyptian governorates buildings based on wind/solar potential: Power, efficiency, economic, environmental, and thermal comfort maps

Environmental justice mapping tools in the United States: A review of national and state tools.

Environmental justice (EJ) mapping tools are geographic information system (GIS)-based digital maps that integrate environmental, socioeconomic, health, and demographic data to identify areas experiencing environmental injustices. These tools are increasingly used to guide investments toward disadvantaged communities. This review examines 25 EJ tools, describing their functionalities, coverage, and indicator types, ranging from biological susceptibilities to socioeconomic and environmental factors. We discuss the tools' resolutions, update frequencies, and data breadth, emphasizing their role in informing EJ interventions. However, gaps exist, particularly in the underrepresentation of U.S. territories and the limited inclusion of communicable diseases and climate impacts. This underscores the need for more comprehensive tools that consider diverse health risks and socio-environmental factors. Cumulative impact assessments should be integrated into EJ tools, incorporating a broad spectrum of indicators to capture the multifaceted nature of environmental injustices. Community engagement is also crucial in developing and updating EJ tools to ensure they accurately reflect community needs and conditions. By addressing these recommendations, EJ tools can better serve as effective instruments for highlighting and mitigating environmental disparities, supporting broader environmental justice and health equity goals.

Radio Environment Map Reconstruction Via Tensor Completion: Bayesian and Semantic Approaches

Radio Environment Map Reconstruction Via Tensor Completion: Bayesian and Semantic Approaches

Research on Robot SLAM Technology Based on Semantic Information in Dynamic Environment

Aiming at the problems of low positioning accuracy, poor map readability and weak robustness when the mobile robot implements the SLAM technology due to the existence of dynamic objects in the mobile robot working environment, an SLAM technology algorithm for mobile robot dynamic environment positioning based on semantic information is proposed. Firstly, the front end of the ORB-SLAM2 framework will be used, combined with the YOLO v4 target detection algorithm, to extract the ORB features of the input image. Meanwhile, the YOLO v4 target detection algorithm is used to obtain the dynamic and static areas of the object containing semantic information in the scene image, to obtain the preliminary semantic dynamic and static areas, and to perform semantic segmentation in the image; Then, the dynamic object region is selected by using the image polar equation, and the ORB feature points distributed on the dynamic object are eliminated. Finally, the processed feature points and adjacent key frames are used for inter-frame matching to estimate the camera pose to build a static environment map. The experiment used the open TUM dataset to compare the proposed algorithm with the traditional ORB-SLAM2 test results show that the proposed algorithm improves the pose estimation accuracy by 75% compared with the ORB-SLAM2 in the dynamic environment, and the map construction effect is significantly enhanced. The experimental results show that this algorithm can eliminate the influence of dynamic objects on SLAM technology, improve the positioning accuracy of the system, and expand the application field of the system.

Development of a Collision-Free Path Planning Method for a 6-DoF Orchard Harvesting Manipulator Using RGB-D Camera and Bi-RRT Algorithm.

With the decreasing and aging agricultural workforce, fruit harvesting robots equipped with higher degrees of freedom (DoF) manipulators are seen as a promising solution for performing harvesting operations in unstructured and complex orchard environments. In such a complex environment, guiding the end-effector from its starting position to the target fruit while avoiding obstacles poses a significant challenge for path planning in automatic harvesting. However, existing studies often rely on manually constructed environmental map models and face limitations in planning efficiency and computational cost. Therefore, in this study, we introduced a collision-free path planning method for a 6-DoF orchard harvesting manipulator using an RGB-D camera and the Bi-RRT algorithm. First, by transforming the RGB-D camera's point cloud data into collision geometries, we achieved 3D obstacle map reconstruction, allowing the harvesting robot to detect obstacles within its workspace. Second, by adopting the URDF format, we built the manipulator's simulation model to be inserted with the reconstructed 3D obstacle map environment. Third, the Bi-RRT algorithm was introduced for path planning, which performs bidirectional expansion simultaneously from the start and targets configurations based on the principles of the RRT algorithm, thereby effectively shortening the time required to reach the target. Subsequently, a validation and comparison experiment were conducted in an artificial orchard. The experimental results validated our method, with the Bi-RRT algorithm achieving reliable collision-free path planning across all experimental sets. On average, it required just 0.806 s and generated 12.9 nodes per path, showing greater efficiency in path generation compared to the Sparse Bayesian Learning (SBL) algorithm, which required 0.870 s and generated 15.1 nodes per path. This method proved to be both effective and fast, providing meaningful guidance for implementing path planning for a 6-DoF manipulator in orchard harvesting tasks.

High-Altitude Stakes in the Himalayas: China-India Boundary Dispute

Abstract. The more than half-century old boundary dispute between China and India is a complex and fraught standoff between the two most populous nations over some of the most inhospitable and least populated land on Earth. The disagreement can be traced back in part to early twentieth-century maps prepared by British Foreign Secretary Henry McMahon demarcating the border between British India and Tibet for the 1913–1914 Shimla Convention. Challenging and oftentimes inaccessible terrain complicated efforts to accurately survey the Himalayas. The result was less precise boundary demarcation and mapping, the consequences of which continue to drive a wedge between China-India relations today. The geography and mapping of this unique alpine environment has shaped regional geopolitics for more than a century and will continue influencing diplomacy going forward.