Terrain Mapping Research Articles

Algorithms for the visual analysis of an environment by an autonomous mobile robot for area cleanup

Objectives. At present, increasing rates of pollution of vast areas by various types of household waste are becoming an increasingly serious problem. In this connection, the creation of a robotic complex capable of performing autonomous litter collection functions becomes an urgent need. One of the key components of such a complex comprises a vision system for detecting and interacting with target objects. The purpose of this work is to develop the underlying algorithmics for the vision system of robots executing area cleaning functions.Methods. Within the framework ofthe proposed structure ofthe system for visual analysis ofthe external environment, algorithms for detecting and classifying objects of various appearance have been developed using convolutional neural networks. The neural network detector was set up by gradient descent on the open dataset of TACO training samples. To determine the geometric parameters of a surface in the field of view of the robot and estimate the coordinates of objects on the ground, a homography matrix was formed to take into account information about the characteristics and location of the video camera.Results. The developed software and algorithms for a mobile robot equipped with a monocular video camera are capable of implementing the functions of neural network detection and classification of litter objects in the frame, as well as projection of found objects on a terrain map for their subsequent collection.Conclusions. Experimental studies have shown that the developed system of visual analysis of the external environment of an autonomous mobile robot has sufficient efficiency to solve the tasks of detecting litter in the field of view of an autonomous mobile robot.

Monogenetic scoria cone and associated lava flow volume estimates and their controlling factors

Estimating eruption volumes of volcanoes is crucial for studying the development and evolution of volcanoes and assessing volcanic hazards. Volume estimates for polygenetic volcanoes are well-explored but individual monogenetic volcanoes have received less attention. This could be attributed to the lower perceived hazards resulting from their smaller size and rare eruptive occurrences within volcanic fields. However, accurately determining the volume of individual monogenetic volcanoes is significant for understanding volcanic field development and evolution. Estimates of individual monogenetic eruptions may be challenging due to overlapping lava flows from different vents within a volcanic field or underestimation resulting from the breaching of small-volume scoria cones. This study aims to evaluate the relationship between the morphometric parameters of scoria cones and the volumes of associated lava flows using the globally free Advanced Land Observing Satellite (ALOS) World 3D 30 m (AW3D30) DEM, the US National Elevation Dataset (NED) 10 m DEM, and related satellite images and terrain maps. The results show that the diameter of the scoria cone base (Wco) correlates best with the associated lava flow volume, and Wco is the parameter least affected by later onlapping lava flows. Numerous factors influence the volumes of monogenetic volcanic eruptions. The regional tectonic environment, such as tectonic setting and crust thickness, has been found to control Wco and hence the volume of monogenetic volcanoes. Subduction zones and thicker crust settings are characterized by the most voluminous monogenetic volcanoes. These environments facilitate the accumulation of magma, supporting larger volcanic eruptions. Magma density also correlates with monogenetic eruption volume. Lower density magma is more likely to erupt and form larger monogenetic volcanoes. Furthermore, pre-existing crustal weaknesses such as fault systems are the main factors affecting magma movement in monogenetic shallow plumbing systems and facilitate magma ascent to the surface. Local stresses appear to have a lesser influence on eruptive volumes. Magma source shape has minor influence on monogenetic eruption volumes. Evaluation of all these parameters will provide more robust estimates of potential eruption volumes, hence informing volcanic field hazards assessment.

Pilot Study of Low-Light Enhanced Terrain Mapping for Robotic Exploration in Lunar PSRs

The recent discovery of water ice in the lunar polar shadowed regions (PSRs) has driven interest in robotic exploration, due to its potential utilization to generate water, oxygen, and hydrogen that would enable sustainable human exploration in the future. However, the absence of direct sunlight in the PSRs poses a significant challenge for the robotic operation to obtain clear images, consequently impacting crucial tasks such as obstacle avoidance, pathfinding, and scientific investigation. In this regard, this study proposes a visual simultaneous localization and mapping (SLAM)-based robotic mapping approach that combines dense mapping and low-light image enhancement (LLIE) methods. The proposed approach was experimentally examined and validated in an environment that simulated the lighting conditions of the PSRs. The mapping results show that the LLIE method leverages scattered low light to enhance the quality and clarity of terrain images, resulting in an overall improvement of the rover’s perception and mapping capabilities in low-light environments.

End-to-End Learning for Optimization via Constraint-Enforcing Approximators

In many real-world applications, predictive methods are used to provide inputs for downstream optimization problems. It has been shown that using the downstream task-based objective to learn the intermediate predictive model is often better than using only intermediate task objectives, such as prediction error. The learning task in the former approach is referred to as end-to-end learning. The difficulty in end-to-end learning lies in differentiating through the optimization problem. Therefore, we propose a neural network architecture that can learn to approximately solve these optimization problems, particularly ensuring its output satisfies the feasibility constraints via alternate projections. We show these projections converge at a geometric rate to the exact projection. Our approach is more computationally efficient than existing methods as we do not need to solve the original optimization problem at each iteration. Furthermore, our approach can be applied to a wider range of optimization problems. We apply this to a shortest path problem for which the first stage forecasting problem is a computer vision task of predicting edge costs from terrain maps, a capacitated multi-product newsvendor problem, and a maximum matching problem. We show that this method out-performs existing approaches in terms of final task-based loss and training time.

High-Frequency, Multiband Projector Using 1–3 Connectivity Lithium Niobate Composites With an Inversion Layer

In this study, we demonstrate a sound navigation and ranging (SONAR) projector with a higher and wider frequency range using an inversion layer. Because the working frequency range of the acoustic transducers is limited, several types of transducers are required to operate at different frequencies for underwater acoustic systems. Multiband acoustic transducers have been developed to solve this problem; however, the operating frequency range is only 100–200 kHz. To overcome this, we used lithium niobate (LN) to maximize the advantage of using an inversion layer. The characteristics of LN according to the angle of the cutting direction and the characteristics of 1–3 composites were calculated, and finite element method (FEM) simulations were performed using these calculated values. The simulated and measured results of the inversion layer composite and fabricated projectors showed similar electrical impedance responses and transmitting voltage response (TVR). Resonance of the inversion layer composite occurred at 330, 680, and 1100 kHz. The fabricated projector had resonance frequencies of 390 and 790 kHz, and the working frequency range of the fabricated projector was 270–900 kHz. We expect that the developed projector will exhibit excellent performance in underwater navigation applications, such as small-scale object detection and terrain mapping.

The radiation shielding proficiency and hyperspectral-based spatial distribution of lateritic terrain mapping in Irikkur block, Kannur, Kerala

The practice of identifying the potential zones for mineral exploration in a speedy and low-cost method includes the use of satellite imagery analysis as a part of remote sensing techniques. It is challenging to explore the iron mineralization of a region through conventional methods which are a time-consuming process. The current study utilizes the Hyperion satellite imagery for mapping the iron mineralization and associated geological features in the Irikkur region, Kannur, Kerala. Along with the remote sensing results, the field study and laboratory-based analysis were conducted to retrieve the ground truth point and geochemical proportion to verify the iron ore mineralization. The MC simulation showed for shielding properties indicate an increase in the linear attenuation coefficient with raising the Fe2O3+SiO2 concentrations in the investigated rocks where it is varied at 0.662 MeV in the range 0.190 cm−1- 0.222 cm−1 with rising the Fe2O3+SiO2 content from 57.86 wt% to 71.15 wt%. The analysis also revealed that when the γ-ray energy increased from 0.221 MeV to 2.506 MeV, sample 1 had the largest linear attenuation coefficient, ranging from 9.33 cm−1 to 0.12 cm−1. Charnockite rocks were found to have exceptional shielding qualities, making them an excellent natural choice for radiation shielding applications.

Reconstruction of a video sequence based on a geometric model using a video descriptor

In recent years, the problem of reconstructing visual information has been a hot topic in machine vision and remote sensing using UAVs. The scope of the UAV is quite wide. They can monitor the traffic situation, both citywide and in remote areas, control the fire situation in forests or flood waters in the regions, deliver goods in a short time, and much more. Many different intelligent and widely used subsurface reconstruction methods are used for accident-free driving of UAVs in an urban environment with visual guidance. The main visualization problem in the practical applications described above are shadows from buildings, overlapping objects, sun glare, reflections from the surface, which require high-quality image reconstruction. The problem of classifying a dataset of the underlying surface is of great importance for the correct reconstruction of images. The article proposes an algorithm for reconstructing a video sequence based on a geometric model using a video descriptor to classify video into a static background and moving objects. To study the effectiveness of the new method, a qualitative analysis of the restored underlying surface of the urban environment was carried out. The subject of the study is the existing methods and algorithms for constructing descriptors for image classification, as well as methods for reconstructing dynamic images. The object of the study is a set of test video sequences of the terrain map obtained using a UAV. The result of the study is the development of an algorithm for constructing a global video descriptor for object classification and further reconstruction of the underlying surface based on the descriptor. The novelty of the work is an algorithm that allows you to reconstruct a map of the underlying surface based on the construction of an object classification descriptor. The results of calculating the root-mean-square error for evaluating the proposed method for processing the considered map of the area are shown. When conducting a comparative analysis, it was revealed that almost all methods for recovering a video sequence have disadvantages, which are complicated by a number of reasons: the presence of a non-stationary background: objects located at different distances from the UAV camera can be moving; the difficulty of distinguishing between objects and the background in the case of slow movement of foreground objects; lighting conditions, etc. Therefore, this article proposes a method that allows you to overcome these difficulties and more accurately restore the map of the area.

Design of a Terrain Mapping System for Low-cost Exploration Robots based on Stereo Vision

Design of a Terrain Mapping System for Low-cost Exploration Robots based on Stereo Vision

Exploring modern bathymetry: A comprehensive review of data acquisition devices, model accuracy, and interpolation techniques for enhanced underwater mapping

Building a high-precision bathymetry digital elevation model is essential for navigation planning, marine and lake resource planning, port construction, and underwater archaeological projects. However, existing bathymetry methods have yet to be effectively and comparatively analyzed. This paper comprehensively reviews state-of-the-art bathymetry methods, including data acquisition techniques, model accuracy, and interpolation algorithms for underwater terrain mapping. First, We assess the merits and drawbacks of novel data acquisition devices, such as single-beam/multi-beam echo sounders and light detection and ranging systems. After that, we analyze the accuracy of the ETOPO1, GEBCO_2022 and SRTM15 to provide valuable insights into their performance. Furthermore, we evaluate ANUDEM, Inverse Distance Weighting, Kriging and Nearest Neighbor interpolation algorithms in different underwater terrains by comparing their applicability, reliability, and accuracy in various underwater environments. Finally, we discuss the development trends and challenges in underwater bathymetry technology and offer a forward-looking perspective on the future of this essential field.

Slum Terrain Mapping Using Low-Cost 2D Laser Scanners

This paper presents a motorbike-based custom made scanning and mapping system for surveying slums and highly populated urban regions. These vicinities are difficult to reach through standard vehicular scanning systems and require a compact solution as presented in this paper. The system consists of two small range 2D Hokuyo laser scanners mounted in right angle orientations to capture the environment. In addition, the global positioning system, the wheel encoder, the inertial measurement unit, and cameras have been integrated with the system to estimate the pose and visual information. Sensorial information has been used to localise the system using Kalman Filtering. Later, by applying the standard transformations, the 3D point cloud map of the surveyed vicinity has been developed. The scanning system has been tested at various locations including densely populated and slum regions. Precise and detailed 3D mapping results have been obtained, which are further extensively analysed to understand the built structure and the road furniture. The working of the system is found to be quite economical and faster than that of local urban surveying systems.

Quality-Aware Autonomous Navigation with Dynamic Path Cost for Vision-Based Mapping toward Drone Landing

This article presents a novel autonomous navigation approach that is capable of increasing map exploration and accuracy while minimizing the distance traveled for autonomous drone landings. For terrain mapping, a probabilistic sparse elevation map is proposed to represent measurement accuracy and enable the increasing of map quality by continuously applying new measurements with Bayes inference. For exploration, the Quality-Aware Best View (QABV) planner is proposed for autonomous navigation with a dual focus: map exploration and quality. Generated paths allow for visiting viewpoints that provide new measurements for exploring the proposed map and increasing its quality. To reduce the distance traveled, we handle the path-cost information in the framework of control theory to dynamically adjust the path cost of visiting a viewpoint. The proposed methods handle the QABV planner as a system to be controlled and regulate the information contribution of the generated paths. As a result, the path cost is increased to reduce the distance traveled or decreased to escape from a low-information area and avoid getting stuck. The usefulness of the proposed mapping and exploration approach is evaluated in detailed simulation studies including a real-world scenario for a packet delivery drone.

Development and evaluation of a method to identify potential release areas of snow avalanches based on watershed delineation

Abstract. Snow avalanches are a prevalent threat in mountain territories. Large-scale mapping of avalanche-prone terrain is a prerequisite for land-use planning where historical information about past events is insufficient. To this aim, the most common approach is the identification of potential release areas (PRAs) followed by numerical avalanche simulations. Existing methods for identifying PRAs rely on terrain analysis. Despite their efficiency, they suffer from (i) a lack of systematic evaluation on the basis of adapted metrics and past observations over large areas and (ii) a limited ability to distinguish PRAs corresponding to individual avalanche paths. The latter may preclude performing numerical simulations corresponding to individual avalanche events, questioning the realism of resulting hazard assessments. In this paper, a method that accurately identifies individual snow avalanche PRAs based on terrain parameters and watershed delineation is developed, and confusion matrices and different scores are proposed to evaluate it. Comparison to an extensive cadastre of past avalanche limits from different massifs of the French Alps used as ground truth leads to true positive rates (recall) between 80 % and 87 % in PRA numbers and between 92.4 % and 94 % in PRA areas, which shows the applicability of the method to the French Alps context. A parametric study is performed, highlighting the overall robustness of the approach and the most important steps/choices to maximize PRA detection, among which the important role of watershed delineation to identify the right number of individual PRAs is highlighted. These results may contribute to better understanding avalanche hazard in the French Alps. Wider outcomes include an in-depth investigation of the issue of evaluating automated PRA detection methods and a large data set that could be used for additional developments, and to benchmark existing and/or new PRA detection methods.

Mapping warming reefs—An application of multibeam acoustic water column analysis to define threatened abalone habitat

Robust definition of the spatial extent of seafloor habitats and how they may be changing through time is a holy grail for ecosystem management, particularly if an ecosystem is approaching a tipping point beyond which irreversible changes may occur. Here we generate and explore a new data set for the management of warming reefs in eastern Tasmania, Australia that will significantly improve the baseline maps required for fine-scaled spatial modelling and management that is, both robust at regional scales and is highly resolved within the water column. This procedure enabled the relative density of kelp vegetation to be identified in a region that is being overwhelmed by the range extension of a destructive grazer, the Longspined Sea Urchin, Centrostephanus rodgersii. We present a new online tool to visualize multibeam water column acoustic data as surfaces of kelp density at high resolution (50 cm) scale over seafloor terrain maps (spanning a total straight-line distance of 594 km and a total area of 29.14 km2) to reveal the types of reef structure on the East Coast of Tasmania where abalone habitat is threatened by kelp loss.

Towards Designing a Planet Walk Simulation in a Controlled Environment

Planet Walk Simulation involves several parametric readings like physical human body conditions, geographic and atmospheric conditions, etc. In this work, we have set goals including system state monitoring, terrain mapping, and user navigation. Specific objectives are displaying the user’s telemetry, elevating their spatial awareness through short range object detection, and displaying their location relative to origin using wi-fi routers. The experimental results showed navigation motion paths for astronauts and identify obstacles in the path with the help of LiDAR and Hololens.

Deep learning-based automated terrain classification using high-resolution DEM data

Landforms are a fundamental component of the natural environment, and digital terrain mapping on a large spatial scale is important when studying landforms. In this study, we adopted a semantic segmentation model in computer vision to classify elementary landform types using AW3D30 digital elevation model (DEM) data. We built a semantic segmentation model with an FCN-ResNet architecture that extracts features using a residual network (ResNet) and obtains pixel-level segmentation of the DEM using a fully convolutional network (FCN). A lightweight decoder based on skip connections was adopted to maintain detailed information at different scales. We used the 1:1,000,000 Chinese landform map as the label and tested different combinations of terrain factors. The experiments indicate that increasing the terrain factors has no significant influence on the model, and the semantic information can be learned using only DEM data. The model has strong feature extraction capability and is tolerant to noise and error. The results of landform category prediction confirm that deep learning methods have strong potential for landform classification and will have great application prospects in the field of geomorphological research.

PUnet: A robust framework for phase unwrapping in interferometric SAR

Synthetic Aperture Radar Interferometry (InSAR) has grown significantly over the past few decades, which were mainly used in remote sensing applications. Most InSAR applications (e.g., terrain mapping and monitoring) utilized a key technique called phase unwrapping Phase unwrapping obtained the absolute phase from the wrapped phase for the subsequent application. However, the collected wrapped phase inevitably contains noise due to the influence of factors such as atmosphere and temperature in the InSAR acquisition stage. This noise made it challenging to obtain the absolute phase from the wrapped phase. This study proposed a deep learning framework (PUnet) for phase unwrapping form InSAR data. pUnet was a robust framework using U-net as the basic structure combined with an attention mechanism and positional encoding, facilitating accurate phase unwrapping from the wrapped phase. Through comparative experiments with typical phase unwrapping algorithms, we demonstrated that pUnet could obtain absolute phases with high accuracy and robustness than from the wrapped phase under various levels of noise.

Comparative analysis of photometric parameters over Apollo landing sites from Terrain Mapping Camera (Chandrayaan-1/2) and lab measured data

Comparative analysis of photometric parameters over Apollo landing sites from Terrain Mapping Camera (Chandrayaan-1/2) and lab measured data

Back-Projection Imaging for Synthetic Aperture Radar with Topography Occlusion

When synthetic aperture radar (SAR) is conducting remote sensing or terrain mapping, its radar beam is inevitably occluded by the variations in the under-test topography. Although back-projection algorithm (BPA) can theoretically directly solve the imaging problems of topography variations that most current SAR imaging algorithms cannot handle, these BPAs only solve the phase focusing of SAR echo signal, and do not consider the mismatch of SAR imaging results caused by topography occlusion. To solve the mis-imaging issue of the occluded area generated by BPA under the case of topography variation, a topography-based BPA (Topo-BPA) is proposed in this paper. Firstly, a new beam occlusion judgment algorithm based on spherical wave assumption is proposed, and its core is depression angle interpolation and depression angle updating. Then, the proposed Topo-BPA embeds the proposed beam occlusion judgment algorithm before the classical BPA, which not only did not reduce the focus depth of BPA, but improved the imaging accuracy of classical BPA. Finally, numerical experiments have demonstrated the superiority of the Topo-BPA’s performance in comparison with classical BPA.

Airborne Millimeter-Wave InSAR Terrain Mapping Experiments Based on Automatic Extraction and Interferometric Calibration of Tie-Points

Limited by meteorological conditions, airspace, complex terrain and other factors, airborne millimeter-wave InSAR will inevitably face the situation of no control point layout when acquiring terrain data in the difficult mapping areas in Southwest China, which increases the difficulty of subsequent data processing. Moreover, the layout of control points in difficult mapping areas consumes a lot of manpower and time, which is not suitable for large-scale high-precision topographic mapping. To solve these problems, this paper proposes an automatic extraction of tie-points and interferometric calibration technology based on tie-points. This technology develops the automatic extraction algorithm of tie-points based on SAR + SIFT + RANSAC to obtain evenly distributed tie-points of adjacent images, and uses the evenly distributed tie-points as real known points to recalibrate the interference parameters, then carries out elevation transfer and elevation inversion through the tie-points of overlapping areas, thus realizing high-precision mapping without control points for airborne millimeter-wave InSAR. This paper uses measured data to verify the technology, and compares it with the areas with control points and marking points. The comparison results of elevation accuracy prove the feasibility and effectiveness of this method. This paper also discusses the difficulties in the treatment of typical areas, such as water areas, urban areas and mountain areas, and gives reasonable solutions that have good engineering application value.

Design of constrained dynamic path planning algorithms in large-scale 3D point cloud maps for UAVs

The technology of unmanned aerial vehicles (UAVs) has been growing rapidly in recent years. To achieve highly autonomous flight capability, collision-free path generation becomes a key research topic. However, for a large-scale terrain map, an efficient path searching algorithm is always a challenging problem. Based on the Goal-bias Rapidly-exploring Random Tree Star (GB-RRT*) together with the aid of a bidirectional search technique, in this paper, a new constrained path planning algorithm, called Parent Revisited Goal and Parent Bias RRT* (PRGPB-RRT*), is proposed. Three major objectives are considered as the development benchmark: 1). shorter total distance, 2). smoother flight path, and 3). high computational efficiency. To meet different task requirements, a dynamic path planning method is investigated to diminish the high costs due to large-scale environments and keep track of the changing destination by finding local target points. In addition, unknown object avoidance is an essential part of flight path planning. As a consequence, the Fitting-Octree is presented to partition the point clouds into multiple cubes to represent the obstacles precisely. Moreover, the method of grouping point clouds into clusters effectively increases the efficiency of the implementation under a large number of obstacles. Finally, the feasibility of the proposed method is verified by a real-world 3D large-scale point cloud map. Experiments show that the proposed method successfully enhances the path generation efficiency as well as the trajectory smoothness. Moreover, the presented PRGPB-RRT* can be applied to different intelligent UAVs subject to flight kinematics constrain.