Map Of Environment Research Articles

Geophysical Investigation and 3D Modeling of Bedrock Morphology in an Urban Sediment-Filled Basin: The Case of Bolzano (Northern Italy)

Bedrock mapping is essential for understanding seismic amplification, particularly in sediment-filled valleys or basins. However, this can be hard in urban environments. We conducted a geophysical investigation of the sediment-filled Bolzano basin in Northern Italy, where three valleys converge. This study uses low-impact, single-station geophysical methods suitable for urban areas to address the challenges of mapping in such environments. A dataset of 574 microtremor and gravity measurements, along with three seismic reflection lines, allows for the inference of the basin’s deep bedrock morphology, even without direct stratigraphic data. The dataset facilitates a detailed analysis of the spatial patterns of resonance frequencies and amplitudes, revealing 1D and 2D characteristics of the resonances. Notably, 2D resonances predominate along the Adige valley, i.e., the deepest part of the basin with depths up to 900 m. These 2D resonances, which cannot be interpreted through simple 1D frequency-depth relationships, are better understood by integrating gravity data to develop a depth model. The study identifies resonance frequencies ranging from 0.27 to over 3 Hz in Bolzano, affecting different building types during earthquakes. Maximum resonance amplitudes occur at lower frequencies, specifically at 2D resonance sites, therefore primarily impacting high structures. The 2D resonances are directional, with the most significant amplification occurring longitudinally along the valley axes. The resulting 3D bedrock model aids in seismic site response modeling, hydrogeological studies, and geothermal exploration and provides insights into the geological history of the basin, highlighting the role of the Adige Valley as a major drainage pathway.

A Comparative Review on Enhancing Visual Simultaneous Localization and Mapping with Deep Semantic Segmentation.

Visual simultaneous localization and mapping (VSLAM) enhances the navigation of autonomous agents in unfamiliar environments by progressively constructing maps and estimating poses. However, conventional VSLAM pipelines often exhibited degraded performance in dynamic environments featuring mobile objects. Recent research in deep learning led to notable progress in semantic segmentation, which involves assigning semantic labels to image pixels. The integration of semantic segmentation into VSLAM can effectively differentiate between static and dynamic elements in intricate scenes. This paper provided a comprehensive comparative review on leveraging semantic segmentation to improve major components of VSLAM, including visual odometry, loop closure detection, and environmental mapping. Key principles and methods for both traditional VSLAM and deep semantic segmentation were introduced. This paper presented an overview and comparative analysis of the technical implementations of semantic integration across various modules of the VSLAM pipeline. Furthermore, it examined the features and potential use cases associated with the fusion of VSLAM and semantics. It was found that the existing VSLAM model continued to face challenges related to computational complexity. Promising future research directions were identified, including efficient model design, multimodal fusion, online adaptation, dynamic scene reconstruction, and end-to-end joint optimization. This review shed light on the emerging paradigm of semantic VSLAM and how deep learning-enabled semantic reasoning could unlock new capabilities for autonomous intelligent systems to operate reliably in the real world.

Effect of genomic and cellular environments on gene expression noise

BackgroundIndividual cells from isogenic populations often display large cell-to-cell differences in gene expression. This “noise” in expression derives from several sources, including the genomic and cellular environment in which a gene resides. Large-scale maps of genomic environments have revealed the effects of epigenetic modifications and transcription factor occupancy on mean expression levels, but leveraging such maps to explain expression noise will require new methods to assay how expression noise changes at locations across the genome.ResultsTo address this gap, we present Single-cell Analysis of Reporter Gene Expression Noise and Transcriptome (SARGENT), a method that simultaneously measures the noisiness of reporter genes integrated throughout the genome and the global mRNA profiles of individual reporter-gene-containing cells. Using SARGENT, we perform the first comprehensive genome-wide survey of how genomic locations impact gene expression noise. We find that the mean and noise of expression correlate with different histone modifications. We quantify the intrinsic and extrinsic components of reporter gene noise and, using the associated mRNA profiles, assign the extrinsic component to differences between the CD24+ “stem-like” substate and the more “differentiated” substate. SARGENT also reveals the effects of transgene integrations on endogenous gene expression, which will help guide the search for “safe-harbor” loci.ConclusionsTaken together, we show that SARGENT is a powerful tool to measure both the mean and noise of gene expression at locations across the genome and that the data generatd by SARGENT reveals important insights into the regulation of gene expression noise genome-wide.

Sustainability challenges in airlines contemporary environmental management

Environmental concerns attract growing attention in aviation. Airlines and other aviation stakeholders are increasingly committed to more environmentally friendly operations. This paper aims to expand the understanding of the airlines' environmental attitudes and map best practices in the sector. To achieve this goal, the authors followed a two-stage analysis. Initially, a number of interviews were conducted with a focus group of key respondents from six Greek airlines, to draw the general picture of their environmental awareness. In the second stage, the authors studied the market's dominant airlines as a case to gain a deeper understanding of how an Environmental Management System (EMS) is deployed in practice and to allow for best practices to emerge. The research findings highlight the need for EMSs to become an integral part of airlines' operational strategy and the imperative for an articulate regulatory framework able to enhance the airlines' eco-path through concrete guidelines and knowledge exchange.

Autonomous navigation system for greenhouse tomato picking robots based on laser SLAM

Aiming at the specific operation path cruising problem of greenhouse tomato picking robots, this paper designs a laser SLAM (Simultaneous Localization And Mapping)-based autonomous navigation system for greenhouse tomato picking robots.The autonomous navigation system utilizes LiDAR to detect the greenhouse environment, creates an environment map with the Gmapping algorithm employing particle filtering, locates the robot with the AMCL (Adaptive Monte Carlo Localization) algorithm, devises the robot's path for picking tasks with the A* algorithm, and dynamically navigates around obstacles during operations with the DWA (Dynamic Window Approach) algorithm. The greenhouse experiment revealed that the robot's average deviance for both directions (X and Y) is 2.87 and 0.80 centimeters, respectively, when traveling a 320 centimeter path. On the other hand, its average variation across the directions of X and Y is 1.48 versus 1.08 centimeters when it passes through a 40 centimeter zone. The robot exhibits averaged variations of 1.73 and 2.24 millimeters in the X and Y axes, each, when navigating a 40-centimeter-radius turn. Furthermore, the robot exhibits average variations of 1.12 centimeters in the X and 0.78 centimeters in the Y directions when it follows a certain "S"-shaped course. Based on the fulfillment of prescribed path cruising, the system has a high level of navigation and positioning accuracy, better meeting the requirements for greenhouse tomato harvesting robot direction and placement.

Multiscale spatiotemporal characterisation of embodied environmental performance of building structures in Geneva from 1850 to 2018

Load-bearing systems in buildings, significant in material use and embodied greenhouse gas emissions (EGHGE), have lacked detailed analysis on their environmental and functional relationships over time and space. This study evaluates the environmental impacts of building structures in Geneva, Switzerland, considering factors like material usage, EGHGE, and urban development. A new method using a similarity-weighted function projects environmental impacts onto a GIS-based building stock, analysing 48 archetypal and 84,477 stock buildings built from 1850 to 2018. Results show a 37% reduction in structural volume per floor area and a 10% increase in mass over time. Buildings predating the masonry-to-concrete transition would produce 7% more EGHGE if constructed today. Multi-residential buildings emit 14% less EGHGE than single homes. A new indicator amortizes upfront environmental effects over a building's lifespan, aiding in historical comparisons of building stocks. This approach underscores the need for spatial-temporal environmental impact mapping to understand sustainable urban development dynamics.

Bridging depths and data in mines: 5G-based point cloud mapping in unstructured environments

Underground mines pose significant challenges, including hazardous working conditions, limited access, and the need to ensure the safety of human workers. Digital transformation through the integration of modern technologies is essential to mitigate these challenges and enhance the overall safety and efficiency of mining operations. This paper addresses the pressing need for 5G connectivity for the digital transformation of underground mines and demonstrates its application through a live 3D point cloud mapping by a mobile robot. The results of the experiments conducted to validate the network’s performance for such a use-case are presented. The first experiment involved testing the latency of the network over a test drift at various loads. The second experiment involved mapping the drift and streaming the 3D point cloud map of the drift over the 5G network. These initial experiments emphasize the potential of the 5G-enabled automation in underground mines and holds promise in digitalizing underground mining operations.

Gross depositional environment mapping in the onshore South Australian Otway Basin: an example of data integration

As part of a greater petroleum systems modelling project, gross depositional environment maps were made for six key intervals in the onshore portion of the Jurassic to Late-Cretaceous Otway Basin in South Australia. This mapping interpreted varied depositional systems including anoxic, extensive ice-influenced lacustrine, lacustrine turbidite, extensive fluvial/lacustrine, fluvial/flood plain and growth fault-bound ribbon-like alluvial fan aprons.

Shipwreck and Sherbrook supersequence regional gross depositional environments, offshore Otway Basin

The Shipwreck and Sherbrook supersequences together constitute the upper Cretaceous succession in the Otway Basin that was deposited during an extensional basin phase. In the Shipwreck Trough, where the upper Cretaceous succession is well explored, gas fields are hosted by the Shipwreck Supersequence (SS). Elsewhere, the upper Cretaceous interval is lightly explored, and the deep-water area is considered an exploration frontier. We present regional gross depositional environment (RGDE) maps for the LC1.1 and LC1.2 sequences of the Shipwreck SS, and the LC2 Sherbrook SS. Fluvial Plain, Coastal-Delta Plain and Shelf RGDEs were interpreted from wireline logs, cores, and seismic facies. The Fluvial Plain and Coastal-Delta Plain RGDEs are mostly restricted to the inboard platform areas and the inner Morum Sub-basin. The mud-prone Shelf RGDE is widespread across the deep-water Morum and Nelson depocentres. The extent of the Fluvial and Coastal-Delta Plain belts progressively increases up-section, imparting a regressive aspect to the succession, and delineating a large fluvial-deltaic complex in the north-west of the basin. Thick seal development across the greater Shipwreck Trough, potentially mature source rocks in the deep-water basin, and thick reservoir development in the hanging wall of growth faults in the inner Morum Sub-basin are insights derived from this study, and will inform area selection for detailed gross depositional environment mapping, formulation of new hydrocarbon and carbon dioxide storage plays, and inputs for petroleum systems modelling.

Enhanced Detection of Artisanal Small-Scale Mining with Spectral and Textural Segmentation of Landsat Time Series

Artisanal small-scale mines (ASMs) in the Amazon Rainforest are an important cause of deforestation, forest degradation, biodiversity loss, sedimentation in rivers, and mercury emissions. Satellite image data are widely used in environmental decision-making to monitor changes in the land surface, but ASMs are difficult to map from space. ASMs are small, irregularly shaped, unevenly distributed, and confused (spectrally) with other land clearance types. To address this issue, we developed a reliable and efficient ASM detection method for the Tapajós River Basin of Brazil—an important gold mining region of the Amazon Rainforest. We enhanced detection in three key ways. First, we used the time-series segmentation (LandTrendr) Google Earth Engine (GEE) Application Programming Interface to map the pixel-wise trajectory of natural vegetation disturbance and recovery on an annual basis with a 2000 to 2019 Landsat image time series. Second, we segmented 26 textural features in addition to 5 spectral features to account for the high spatial heterogeneity in ASM pixels. Third, we trained and tested a Random Forest model to detect ASMs after eliminating irrelevant and redundant features with the Variable Selection Using Random Forests “ensemble of ensembles” technique. The out-of-bag error and overall accuracy of the final Random Forest was 3.73 and 92.6%, which are comparable to studies mapping large industrial mines with the normalized difference vegetation index (NDVI) and LandTrendr. The most important feature in our study was NDVI, followed by textural features in the near and shortwave infrared. Our work paves the way for future ASM regulation through large area monitoring from space with free and open-source GEE and operational satellites. Studies with sufficient computational resources can improve ASM monitoring with advanced sensors consisting of spectral narrow bands (Sentinel-2, Environmental Mapping and Analysis Program, PRecursore IperSpettrale della Missione Applicativa) and deep learning.

Research on autonomous navigation system of greenhouse electric crawler tractor based on LiDAR.

The application of autonomous navigation technology of electric crawler tractors is an important link in the development of intelligent greenhouses. Aiming at the characteristics of enclosed and narrow space and uneven ground potholes in greenhouse planting, to improve the intelligence level of greenhouse electric crawler tractors, this paper develops a navigation system of electric crawler tractors for the greenhouse planting environment based on LiDAR technology. The navigation hardware system consists of five modules: the information perception module, the control module, the communication module, the motion module, and the power module. The software system is composed of three layers: the application layer, the data processing layer, and the execution layer. The developed navigation system uses LiDAR, Inertial Measurement Unit (IMU) and wheel speed sensor to sense the greenhouse environment and the crawler tractor's information, employs the Gmapping algorithm to build the greenhouse environment map, and utilizes the adaptive Monte Carlo positioning algorithm for positioning. The simulation test of different global path planning algorithms in Matlab shows that the A* algorithm obtains the optimal overall global path. In the scene of map 5, the path planned by the A* algorithm is the most significant, and the number of inflection points is reduced by 40.00% and 87.50%, respectively; meanwhile, the path length is the same as that of the Dijkstra algorithm, but the runtime is reduced by 68.87% and 81.49%, respectively; compared with the RRT algorithm, the path length is reduced by 7.27%. Therefore, the A* algorithm and the Dynamic Window Approach (DWA) method are used for tractor navigation and obstacle avoidance, which ensures global path optimality while also achieving effective local path planning for obstacle avoidance. The test results suggest that the maximum lateral deviation of the built map is 6cm, and the maximum longitudinal deviation is 16cm, which meets the requirement of map accuracy. Additionally, the results of the navigation accuracy test indicate that the maximum lateral deviation of navigation is less than 13cm, the average lateral deviation is less than 7cm, and the standard lateral deviation is less than 8cm. The maximum heading deviation is less than 14°, the average heading deviation is less than 7°, and the standard deviation is less than 8°. These results show that the developed navigation system meets the navigation accuracy requirements of electric crawler tractors in the greenhouse environment.

Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon

Bathymetry data is indispensable for a variety of aquatic field studies and benthic resource inventories. Determining water depth can be accomplished through an echo sounding system or remote estimation utilizing space-borne and air-borne data across diverse environments, such as lakes, rivers, seas, or lagoons. Despite being a common option for bathymetry mapping, the use of satellite imagery faces challenges due to the complex inherent optical properties of water bodies (e.g., turbid water), satellite spatial resolution limitations, and constraints in the performance of retrieval models. This study focuses on advancing the remote sensing based method by harnessing the non-linear learning capabilities of the machine learning (ML) model, employing advanced feature selection through a meta-heuristic algorithm, and using image extraction techniques (i.e., band ratio, gray scale morphological operation, and morphological multi-scale decomposition). Herein, we validate the predictive capabilities of six ML models: Random Forest (RF), Support Vector Machine (SVM), CatBoost (CB), Extreme Gradient Boost (XGB), Light Gradient Boosting Machine (LGBM), and KTBoost (KTB) models, both with and without the application of meta-heuristic optimization (i.e., Dragon Fly, Particle Swarm Optimization, and Grey Wolf Optimization), to accurately ascertain water depth. This is achieved using a diverse input dataset derived from multi-spectral Landsat 9 imagery captured on a cloud-free day (19 September 2023) in a shallow, turbid lagoon. Our findings indicate the superior performance of LGBM coupled with Particle Swamp Optimization (R2 = 0.908, RMSE = 0.31 m), affirming the consistency and reliability of the feature extraction and selection-based framework, while offering novel insights into the expansion of bathymetric mapping in complex aquatic environments.

DSOMF: A Dynamic Environment Simultaneous Localization and Mapping Technique Based on Machine Learning.

To address the challenges of reduced localization accuracy and incomplete map construction demonstrated using classical semantic simultaneous localization and mapping (SLAM) algorithms in dynamic environments, this study introduces a dynamic scene SLAM technique that builds upon direct sparse odometry (DSO) and incorporates instance segmentation and video completion algorithms. While prioritizing the algorithm's real-time performance, we leverage the rapid matching capabilities of Direct Sparse Odometry (DSO) to link identical dynamic objects in consecutive frames. This association is achieved through merging semantic and geometric data, thereby enhancing the matching accuracy during image tracking through the inclusion of semantic probability. Furthermore, we incorporate a loop closure module based on video inpainting algorithms into our mapping thread. This allows our algorithm to rely on the completed static background for loop closure detection, further enhancing the localization accuracy of our algorithm. The efficacy of this approach is validated using the TUM and KITTI public datasets and the unmanned platform experiment. Experimental results show that, in various dynamic scenes, our method achieves an improvement exceeding 85% in terms of localization accuracy compared with the DSO system.

A Study on a Health Impact Assessment and Healthcare Cost Calculation of Beijing–Tianjin–Hebei Residents under PM2.5 and O3 Pollution

Excessive fine particulate matter (PM2.5) and ozone (O3) are invisible killers affecting our wellbeing and safety, which cause great harm to people’s health, cause serious healthcare and economic losses, and affect the sustainable development of the social economy. The effective evaluation of the impact of pollutants on the human body, the associated costs, and the reduction of regional compound air pollution is an important research direction. Taking Beijing–Tianjin–Hebei (BTH) as the research area, this study constructs a comprehensive model for measuring the healthcare costs of PM2.5 and O3 using the Environmental Benefits Mapping and Analysis Program (BenMAP) as its basis. First, this study establishes a health impact assessment model and calculates the number of people affected by PM2.5 and O3 exposure using the health impact function in the BTH region. Then, the willingness to pay (WTP) and cost of illness (COI) methods are used to estimate the healthcare costs inflicted by the two pollutants upon residents from 2018 to 2021. The calculation results show that the total healthcare costs caused by PM2.5 and O3 pollution in BTH accounted for 1%, 0.7%, 0.5%, and 0.3% of the regional GDP in 2018, 2019, 2020, and 2021, respectively. Based on the research results, to further reduce these high healthcare costs, we propose policy suggestions for PM2.5 and O3 control in the BTH region.

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.