Terrain Types Research Articles

Ski-orienteering: a scientific perspective on a multi-dimensional challenge

Ski-orienteering, which combines cross-country skiing with orienteering, dates back to the late 1800s, with the first World Championships in 1975. While researchers have explored the physiological and biomechanical determinants of success in cross-country skiing and orienteering separately in detail, scientific knowledge concerning ski-orienteering remains limited. Based on the information that is presently available together with interviews with elite ski-orienteers, we explore here for the first time the historical development, physiological, biomechanical, and psychological demands, certain training strategies, and future prospects and challenges associated with this sport, including its potential to become an Olympic event. A demanding endurance sport (with racing times of 12–120 min), ski-orienteering requires both considerable aerobic and anaerobic capacity, as well as well-trained upper and lower body muscles. In addition, ski-orienteering demands advanced skiing technique on various types of terrain, with frequent changes between sub-techniques, on both wide and narrow tracks and with numerous turns on downhill terrain. Moreover, success in this sport requires accurate and rapid orienteering—the ability to navigate a complex network of ski tracks with numerous intersections/crossings in a manner designed to pass the multiple control points in the order indicated on the map as rapidly as possible, i.e., advanced spatial cognition and highly developed navigational skills. Thus, ski-orienteering requires training designed to improve both relevant physiological characteristics and orienteering skills, which should become the focus of future interdisciplinary research on this complex sport.

Complexation of correlation-extreme navigation systems

The constantly increasing intensity of air traffic, due to the growth in the number of manned and unmanned aerial vehicles, as well as the increasingly frequent disruptions of radio navigation systems in recent years, require the development of new methods for increasing the accuracy and continuity of navigation support for flights. The use of correlation-extreme navigation systems (CENS), using digital terrain matrices (DEM) of the earth's surface, makes it possible to solve navigation problems when flying over many types of terrain. However, in the case of a multi-extreme correlation dependence, the problem of ambiguity in determining the location of the aircraft arises, which reduces the accuracy and continuity of the data generated by such a CENS. The aim of article to develop a method for integrating of airborne CENSs using DEM and optical images of the earth's surface to improve the accuracy and continuity of navigation determinations. A method has been developed for integrating CENS, built on the use of DEM and digital devices for the formation and processing of optical images, and allowing to increase the accuracy of classical correlation-extremal methods in the conditions of the emergence of multi-extremal correlation dependence. The paper examines the possibility of using digital devices for forming and processing images as part of the on-board equipment of an aircraft as a means of clarifying its current location when solving autonomous navigation problems, in particular, to correct deviations of the flight path of the aircraft from a given course.

Multiband Spectropolarimetry of Lunar Maria, Pyroclastics, Fresh Craters, and Swirl Materials

Imaging polarimetry is a well-known method for examining the small-scale structure of the surface regolith of airless celestial bodies. In this study, we examine (for the first time) the wavelength-dependent polarization behavior of selected lunar areas, including maria, highlands, fresh craters, pyroclastic deposits, and the Reiner Gamma swirl, based on telescopic multiband UBVRI imaging polarimetry at phase angles within the range of the positive polarization branch. The terrain-dependent spectropolarimetric behavior is studied for the first time in this work. For each study area, we conduct a mapping of the relative regolith grain size, an analysis of the exponent of the Umov law, and the wavelength dependence of the degree of linear polarization. Furthermore, we perform area-specific principal component analyses of the degree of linear polarization, followed by unsupervised machine learning (clustering) to segment different terrain types. We find that fresh mare craters and high-titanium pyroclastic deposits have an increased regolith grain size, whereas crater ray material, low-titanium pyroclastic material, and the Reiner Gamma swirl are more finely grained than the average regolith. The degree of linear polarization decreases with increasing wavelength-dependent albedo according to a power law whose exponent is itself positively correlated with the albedo. For a constant albedo and grain size, the degree of linear polarization increases linearly with wavelength. The clustering step yields a library of terrain-dependent prototype spectra of the degree and angle of linear polarization.

Kinematic performance analysis of a robot climbing steps without movable tracks on a swing arm

A lightweight tracked robot with no moving swing arm is designed, which is mainly used in exploring narrow spaces. The mathematical model of the position of the robot’s center of mass with the elevation Angle of the vehicle body and the Angle between the swinging arm is established. The kinematics of the robot in different typical terrains, such as ramp, ditch, and step, is analyzed. By analyzing the critical state of the change of the position of the center of mass, the maximum Angle and width of climbing and crossing are calculated under ideal conditions. The maximum height of the robot is also calculated when using different ways to overcome obstacles, corresponding to the position attitude under the maximum climbing height. It provides a data theory basis for robot motion control strategy.

The main results of monitoring the power of the active layer at CALM sites of the Nadymsky object

Monitoring of the thickness of the active layer within the lake-marsh type of terrain in the northern taiga was carried out at three sites organized in 1997, 2013 and 2022. In these areas, two types of tracts are distinguished: frozen peat bogs and swampy runoff hollows with a lowered roof of permafrost (the cross-section of the sites is the same type, the only difference is in the thickness of the peat). The work methodology complied with the unified protocol of the international CALM program. Additionally, the surface settlement was assessed (based on leveling from a local benchmark), geophysical studies were carried out and the temperature of the active layer rocks was measured at different gullies. In swampy runoff hollows, hollows with open water and on specific mineral spots within peat bogs, the depth of the permafrost roof is 3–10 m. At the boundaries of peat bogs and in swampy runoff hollows, the thawing depth exceeds 2 m, and on peat bogs it varies from 0.8 to 1.6 m depending on the thickness of the organic layer and the condition of the ground cover. On peatlands with an organic layer more than 1 m thick and a continuous ground cover, the depth of the seasonal thawing layer in some places does not exceed 0.4 m. These peatlands respond poorly to climate change. The depth of the permafrost roof within thin peat bogs (less than 0.5 m of peat) with oppressed vegetation cover is currently more than 2 m, marking the beginning of the replacement of the seasonally thawed layer with seasonally frozen and the formation of areas with a lowered permafrost roof. Monitoring of soil temperatures in such areas has showed that a random combination of climatic factors in individual years can lead to short-term (two years) new formation of permafrost or the merging of seasonal permafrost with permafrost. Thus, the formation of non-merging permafrost is of a reciprocating nature.

Evaluating spatially enabled machine learning approaches to depth to bedrock mapping, Alberta, Canada.

Maps showing the thickness of sediments above the bedrock (depth to bedrock, or DTB) are important for many geoscience studies and are necessary for many hydrogeological, engineering, mining, and forestry applications. However, it can be difficult to accurately estimate DTB in areas with varied topography, like lowland and mountainous terrain, because traditional methods of predicting bedrock elevation often underestimate or overestimate the elevation in rugged or incised terrain. Here, we describe a machine learning spatial prediction approach that uses information from traditional digital elevation model derived estimates of terrain morphometry and satellite imagery, augmented with spatial feature engineering techniques to predict DTB across Alberta, Canada. First, compiled measurements of DTB from borehole lithologs were used to train a natural language model to predict bedrock depth across all available lithologs, significantly increasing the dataset size. The combined data were then used for DTB modelling employing several algorithms (XGBoost, Random forests, and Cubist) and spatial feature engineering techniques, using a combination of geographic coordinates, proximity measures, neighbouring points, and spatially lagged DTB estimates. Finally, the results were contrasted with DTB predictions based on modelled relationships with the auxiliary variables, as well as conventional spatial interpolations using inverse-distance weighting and ordinary kriging methods. The results show that the use of spatially lagged variables to incorporate information from the spatial structure of the training data significantly improves predictive performance compared to using auxiliary predictors and/or geographic coordinates alone. Furthermore, unlike some of the other tested methods such as using neighbouring point locations directly as features, spatially lagged variables did not generate spurious spatial artifacts in the predicted raster maps. The proposed method is demonstrated to produce reliable results in several distinct physiographic sub-regions with contrasting terrain types, as well as at the provincial scale, indicating its broad suitability for DTB mapping in general.

Gait Learning for Hexapod Robot Facing Rough Terrain Based on Dueling-DQN Algorithm

In the handling of dangerous goods in explosive environments, robots are increasingly being used instead of human operators. Robots designed for operation in explosive environments are mostly equipped with tracked structures, which, due to their limited terrain adaptability, struggle to movement rugged landscapes. Hexapod robots, with their excellent maneuverability and adaptability, possess advantages in completing hazardous material handling tasks in such rugged terrains. One current challenge lies in enabling hexapod robots to autonomously adjust their gaits to cope with rugged terrain. This paper proposes a gait learning method based on the Dueling Deep Q-Network (Dueling-DQN) algorithm to address the gait adjustment problem of hexapod robots in sloped, terraced, and rugged terrain. The method combines lidar data and reinforcement learning to extract features from the lidar data to determine terrain types and foot coordinates. Finally, the Dueling-DQN algorithm and redundant phase strategy are employed to facilitate the motion of hexapod robots in these three types of rugged terrain. Simulation and prototype experiments are conducted to evaluate the Dueling-DQN algorithm's performance in terms of rewards and stability margins for the three types of terrain. During algorithm training on sloped, terraced, and rugged terrain, stable rewards and positive stability margins are achieved after approximately 490 iterations. The effectiveness and feasibility of the proposed method are further validated through Gazebo simulation and prototype experiments. In the context of movement in rugged terrain within explosive environments, the gait learning method based on the Dueling-DQN algorithm offers valuable insights into the control of hexapod robots.

A New Classification Rule-Set for Mapping Surface Water in Complex Topographical Regions Using Sentinel-2 Imagery

Surface water is a critical natural resource, but its mapping accuracy is vulnerable to cloud cover, snow, shadows, and diverse roofing materials. Recognizing the limitations of a single threshold segmentation method that fails to achieve high-precision extraction of surface water in complex terrain areas, this study introduces a multiple threshold water detection rule (MTWDR) method to improve water extraction results. This method uses the multi-band reflectance characteristics of ground features to construct a water index and combines brightness features with the Otsu algorithm to eliminate interference from highly reflective ground features like ice, snow, bright material buildings, and clouds. The Yunan–Guizhou Plateau was selected as the study area due to its complex terrain and multiple types of surface water, and experiments were conducted using Sentinel-2 data on the Google Earth Engine (GEE). The results demonstrate that: (1) The proposed method achieves an overall accuracy of 94.08% and a kappa coefficient of 0.8831 in mountainous areas. In urban areas, the overall accuracy reaches 95.15%, accompanied by a kappa coefficient of 0.8945. (2) Compared to five widely used water indexes and rules, the MTWDR method improves accuracy by more than 3%. (3) It effectively overcomes interference from highly reflective ground features while maintaining the integrity and accuracy of water boundary extraction. In conclusion, the proposed method enhances extraction accuracy across different types of surface water within complex terrain areas, and can provide significant theoretical implications and practical value for researching and applying surface water resources.

Spatial modeling of air pollution in Kharkiv city


 
 
 One of the most pressing problems facing cities, which negatively affects public health. There is a global practice of online monitoring of the content of pollutants in the air of settlements and informing the population about dangerous periods of increased pollution concentrations for certain categories of diseases and the population as a whole. It is also likely that one of the factors of pollution spread is the landscape differentiation of the city’s territory. The purpose of the study presented in this article is to develop statistical and cartographic models of the spatial distribution of air pollutants within the city of Kharkiv and to establish the relationship between the degree of air pollution and types of urban landscapes. Methods. Based on the analysis of the main methodological approaches and tasks of cartographic modeling of the spatial distribution of air pollution, the study substantiates the choice of the modeling method in this study; the features of a large city as an object of cartographic modeling of air pollution are assessed. Results. Based on the results of long-term monitoring by the Kharkiv Regional Center for Hydrometeorology, a statistical analysis of the results of the maximum and average content of pollutants in the atmosphere of Kharkiv was made and the spatial distribution of air pollution in Kharkiv (including (dust, carbon monoxide, nitrogen dioxide, sulfur dioxide, formaldehyde) was modeled, and patterns of pollution distribution were identified according to landscape types. Conclusions. In general, the spatial analysis of pollutant concentrations in the territories of Kharkiv showed that, in terms of maximum concentrations, the southeastern part of Kharkiv is most affected by carbon monoxide, nitrogen dioxide, and formaldehyde. The reason for this is the large area of industrial facilities. The northwestern part of the city has the highest concentrations of dust and sulfur dioxide. There is a lowering of the relief in the floodplain type of landscape and dense development. Intensive traffic and lowered relief (floodplain landscape) cause high average concentrations of dust, carbon monoxide, and sulfur dioxide in the southern part of the city. Nitrogen dioxide concentrations exceed the TLK in the northwest. The reason is the lowering of the relief on the outskirts of the city (floodplain landscape). The central part of the city has a high average concentration of formaldehyde. In general, a pattern of high concentrations of pollutants was found on the floodplains of the Lopan and Kharkiv rivers, with the lowest concentrations confined to watershed types of terrain between the Kharkiv and Lopan rivers and the Udy and Lopan rivers.
 
 

Integrated Wheel–Foot–Arm Design of a Mobile Platform With Linkage Mechanisms

Abstract Inspired by lizards, a novel mobile platform with revolving linkage legs is proposed. The platform consists of four six-bar bipedal modules, and it is designed for heavy transportation on unstructured terrain. The platform possesses smooth-wheeled locomotion and obstacle-adaptive legged locomotion to enhance maneuverability. The kinematics of the six-bar bipedal modules is analyzed using the vector loop method, subsequently ascertaining the drive scheme. The foot trajectory compensation curve is generated using the fixed-axis rotation contour algorithm, which effectively reduces the centroid fluctuation and enables seamless switching between wheels and legs. When encountering obstacles, the revolving linkage legs act as climbing arms, facilitating seamless integration of wheel, foot, and arm. A physical prototype is developed to test the platform on three typical terrains: flat terrain, slope, and vertical obstacle. The experimental results demonstrated the feasibility of the platform structure. The platform can climb obstacles higher than its own height without adding extra actuation.

Research on DEM geomorphic factor terrain recognition algorithm using probabilistic neural networks based on tactile systems

The accurate recognition of unknown terrain by robots plays a vital role in completing walking tasks. Haptic-based terrain recognition methods have their own advantages over visual, audio, and vibration-based terrain recognition methods. With the increasing complexity of robot application scenarios, there are still many technical difficulties in using tactile systems to recognize terrain. For real-time terrain detection technology, how to improve the accuracy of terrain recognition and shorten recognition time based on tactile systems is an urgent problem to be solved. In this paper, the digital elevation model (DEM) geomorphic factor terrain recognition algorithm using probabilistic neural networks (DGFTRA-PNN) based on tactile force-feedback system is proposed to solve this problem. First, the tactile force-feedback system is mounted on the foot ends of the hexapod robot to collect terrain data. Second, the feasibility of using DEM geomorphic factors as terrain features to identify terrain was demonstrated. Third, DGFTRA-PNNs are established based on the different walking gait of the hexapod robot, achieving terrain recognition based on DEM feature values. Finally, the accuracy and speed of DGFTRA-PNN were verified through field experiments. Compared with the terrain recognition algorithm based on back propagation neural network (BP) and extreme learning machine (ELM) algorithm, the DGFTRA-PNN has advantages in terms of terrain recognition accuracy and recognition time: the average recognition accuracy for various types of terrain is 96%, and the calculation time is 0.04 seconds.

The Relationship Between the Energization of Moon‐Originating Ions and Terrain Type on the Lunar Surface

AbstractWe analyze data acquired by the Kaguya satellite on 14 October 2008 when the Moon was in the terrestrial magnetotail lobe to gain new insight into the energization of ions originating from the Moon. The Moon‐originating ions were detected over a broad range of latitudes from −80° to 50° above the Moon's dayside at ∼100 km altitude. The fluxes of the Moon‐originating ions were observed at energies from ∼50 to ∼1,000 eV. Additionally, these ions exhibited a wide distribution pitch angle spanning from ∼45 to 90°. The energy levels of ions originating from the Moon show rapid changes, either increasing or decreasing by a factor of ∼10 within 8 min without the solar zenith angle dependence. Such rapid energy changes were observed over the highland regions. These observations are discussed in light of possible acceleration mechanisms of Moon‐originating ions, including temporal and spatial effects.

Quantifying trade-offs in satellite hardware configurations using a super-resolution framework with realistic image degradation

ABSTRACT When designing and operating Earth observation satellites, trade-offs must often be made between different hardware components, operational considerations, etc… For example, considerations include how much of the mass budget should be spent on imaging lenses?, or how long should image exposure times be?. Resulting limitations in image resolution and quality can be partially compensated for using super-resolution (SR) techniques. However, deep SR networks recently applied to satellite imagery are often trained and tested on data that lacks the typical image degrading noise present in real satellite images. In this work, we combine a method for generating realistically degraded satellite images with a deep SR network, in the context of different satellite hardware configurations and geographical types. We use this framework to assess deep SR performance given realistic remote sensing payloads across different terrain types, by evaluating payload- and terrain-dependent SR performance in reconstructing realistically degraded images. The framework allows us to model the effect of alternative satellite hardware configurations on resulting SR image quality, providing insight into optimal satellite operations and payload design in the context of SR-based image quality enhancements.

Impact-Aware Foot Motion Reconstruction and Ramp/Stair Detection Using One Foot-Mounted Inertial Measurement Unit.

Motion reconstruction using wearable sensors enables broad opportunities for gait analysis outside laboratory environments. Inertial Measurement Unit (IMU)-based foot trajectory reconstruction is an essential component of estimating the foot motion and user position required for any related biomechanics metrics. However, limitations remain in the reconstruction quality due to well-known sensor noise and drift issues, and in some cases, limited sensor bandwidth and range. In this work, to reduce drift in the height direction and handle the impulsive velocity error at heel strike, we enhanced the integration reconstruction with a novel kinematic model that partitions integration velocity errors into estimates of acceleration bias and heel strike vertical velocity error. Using this model, we achieve reduced height drift in reconstruction and simultaneously accomplish reliable terrain determination among level ground, ramps, and stairs. The reconstruction performance of the proposed method is compared against the widely used Error State Kalman Filter-based Pedestrian Dead Reckoning and integration-based foot-IMU motion reconstruction method with 15 trials from six subjects, including one prosthesis user. The mean height errors per stride are 0.03±0.08 cm on level ground, 0.95±0.37 cm on ramps, and 1.27±1.22 cm on stairs. The proposed method can determine the terrain types accurately by thresholding on the model output and demonstrates great reconstruction improvement in level-ground walking and moderate improvement on ramps and stairs.

Research on the Identification of Typical Terrain Patterns in Yunnan Province Based on the K-Means Technology

Wire icing is a prevalent challenge in both industrial and scientific domains, and it is widely acknowledged that terrain and water vapor are significant contributing factors in the formation of wire icing. Consequently, the identification of terrains that are prone to inducing water vapor uplift serves as the scientific foundation for predicting ice accumulation on power lines. Yunnan Province, a mountainous province in China, features a large elevation difference. In winter, this region is prone to wire, pole and tower icing, which can affect power transmission and cause economic and property losses. Therefore, it is necessary to conduct research on the identification of typical terrain patterns in this region. In previous terrain studies, more attention has been focused on slope and aspect, watershed analysis and terrain profile analysis. When the purpose of the terrain identification is to analyze which terrains are more prone to collecting water vapor, we hope to obtain slightly larger terrain blocks and analyze the water vapor sources for different terrains in order to identify typical terrains that are conducive to icing formation. A new technology for identifying terrain patterns based on the K-means clustering method is proposed in this study to explore the typical terrain in Yunnan province. Additionally, the influences of different terrain patterns on water vapor movement are also analyzed. The results indicate that the typical terrains in Yunnan are “Valley-Air Channel”, “Topographic Uplifting”, “Ravine”, “Mountain Pass” and “Alpine Divide” patterns. The results show that the identified typical terrain is consistent with observations from satellite images, which verifies the effectiveness of this identification method. Among these five typical terrains, the “Valley-Air Channel”, the “Topographic Uplifting” and the “Mountain Pass” terrains are prone to collecting water vapor and forming ice cover. The “Alpine Divide” terrain is also prone to accumulating water vapor on both sides to form ice cover. The identified typical terrain demonstrates that typical terrain patterns near water bodies are more prone to the occurrence of wire, pole and tower icing because these areas are abundant in water vapor, and the extensive water vapor is easily condensed under the effects of terrain uplifting and cooling. In these key areas, existing wires and towers, as well as those to be constructed in the future, deserve our special attention.

Quantification of active layer depth at multiple scales in Interior Alaska permafrost

Much of Interior Alaska is underlain by permafrost that has been thawing at an unprecedented rate. Top-down expansion of the seasonally thawed ‘active layer’ and development of thermokarst features are increasing across the landscape. This can be attributed primarily due to a warming climate and disturbances like wildfires which have accelerated summer season permafrost thaw. Quantification of active-layer thickness (ALT) is critical to understanding the response of permafrost terrains to these disturbances. ALT measurements are time consuming, and point based. As a result, there are large uncertainties in ALT estimates at regional/global scales (100 km2 or larger) using field scale (1 m2) measurements as direct inputs for calibrating/validating large scale process-based or statistical/empirical models. Here we developed a framework to link field scale ALT measurements with satellite observations to a regional scale (100 km2) via an intermediary upscaling of field scale ALT to the local scale (1 km2) with fine-resolution airborne hyperspectral and light detection and ranging data, thus leading to a characterization of ALT across space and time at multiple scales. We applied an object-based machine learning ensemble approach to upscale field scale (1 m2) measurements to the local (1 km2) and regional scale (100 km2) and achieved encouraging results across three permafrost experimental sites in Interior Alaska that represent a variety of terrain types. Our study demonstrates that generating local scale data products is an effective approach to bridge the gap with field scale measurements and regional scale estimations as it seeks to reduce upscaling uncertainty.

Assessment of the global Copernicus, NASADEM, ASTER and AW3D digital elevation models in Central and Southern Africa

ABSTRACT Validation studies of global Digital Elevation Models (DEMs) in the existing literature are limited by the diversity and spread of landscapes, terrain types considered and sparseness of groundtruth. Moreover, there are knowledge gaps on the accuracy variations in rugged and complex landscapes, and previous studies have often not relied on robust internal and external validation measures. Thus, there is still only partial understanding and limited perspective of the reliability and adequacy of global DEMs for several applications. In this study, we utilize a dense spread of LiDAR groundtruth to assess the vertical accuracies of four medium-resolution, readily available, free-access and global coverage 1 arc-second (30 m) DEMs: NASADEM, ASTER GDEM, Copernicus GLO-30, and ALOS World 3D (AW3D). The assessment is carried out at landscapes spread across Cape Town, Southern Africa (urban/industrial, agricultural, mountain, peninsula and grassland/shrubland) and forested national parks in Gabon, Central Africa (low-relief tropical rainforest and high-relief tropical rainforest). The statistical analysis is based on robust accuracy metrics that cater for normal and non-normal elevation error distribution, and error ranking. In Cape Town, Copernicus DEM generally had the least vertical error with an overall Mean Error (ME) of 0.82 m and Root Mean Square Error (RMSE) of 2.34 m while ASTER DEM had the poorest performance. However, ASTER GDEM and NASADEM performed better in the low-relief and high-relief tropical forests of Gabon. Generally, the DEM errors have a moderate to high positive correlation in forests, and a low to moderate positive correlation in mountains and urban areas. Copernicus DEM showed superior vertical accuracy in forests with less than 40% tree cover, while ASTER and NASADEM performed better in denser forests with tree cover greater than 70%. This study is a robust regional assessment of these global DEMs.

Designing a Surveillance Sensor Network with Information Clearinghouse for Advanced Air Mobility

To ensure safe, secure, and efficient advanced air mobility (AAM) operations, an AAM surveillance network is needed to detect and track AAM traffic. Additionally, a cloud-based surveillance data collection, monitoring, and distribution center is needed, where AAM operators and service suppliers, law enforcement agencies, correctional facilities, and municipalities can subscribe to receiving relevant AAM traffic data to plan and monitor AAM operations. In this work, we developed an optimization model to design a surveillance sensor network for AAM that minimizes the total sensor cost while providing full coverage in the desired region of operation, considering terrain types of that region, terrain-based sensor detection probabilities, and meeting the minimum detection probability requirement. Moreover, we present a framework for the low altitude surveillance information clearinghouse (LASIC), connected to the optimized AAM surveillance network for receiving live surveillance feed. Additionally, we conducted a cost–benefit analysis of the AAM surveillance network and LASIC to justify an investment in it. We examine six potential types of AAM sensors and homogeneous and heterogeneous network types. Our analysis reveals the sensor types that are the most profitable options for detecting cooperative and non-cooperative aircraft. According to the findings, heterogeneous networks are more cost-effective than homogeneous sensor networks. Based on the sensitivity analysis, changes in parameters such as subscription fees, the number of subscribers, sensor detection probabilities, and the minimum required detection probability significantly impact the surveillance network design and cost–benefit analysis.

A contact parameter estimation method for multi-modal robot locomotion on deformable granular terrains

AbstractIn this paper, we consider the problem of contact parameters (slippage and sinkage) estimation for multi-modal robot locomotion on granular terrains. To describe the contact events in the same framework for robots operated at different modes (e.g., wheel, leg), we propose a unified description of contact parameters for multi-modal robots. We also provide a parameter estimation method for multi-modal robots based on CNN and DWT (discrete wavelet transformation) techniques and verify its effectiveness over different types of granular terrains. Besides motion modes, this paper also considers the influence of slope angles and the robot’s handing angles over contact parameters. Through comparison and analysis of the prediction results, our method can not only effectively predict the contact parameters of multi-modal robot locomotion on a granular medium (better than $96\%$ accuracy) but also achieves the same or better performance when compared to other (direct) contact measurement methods designed for individual motion modes, that is, single-modal robots such as quadruped robots and mars rovers. Our proposed unified contact parameter estimation method can be useful for studying the interaction mechanics between multi-modal robots and granular terrains as well as terrain classification tasks due to its superior sensitivity which is analyzed in the experiments.

Digital surface model generation from high‐resolution satellite stereos based on hybrid feature fusion network

AbstractRecent studies have demonstrated that deep learning‐based stereo matching methods (DLSMs) can far exceed conventional ones on most benchmark datasets by both improving visual performance and decreasing the mismatching rate. However, applying DLSMs on high‐resolution satellite stereos with broad image coverage and wide terrain variety is still challenging. First, the broad coverage of satellite stereos brings a wide disparity range, while DLSMs are limited to a narrow disparity range in most cases, resulting in incorrect disparity estimation in areas with contradictory disparity ranges. Second, high‐resolution satellite stereos always comprise various terrain types, which is more complicated than carefully prepared datasets. Thus, the performance of DLSMs on satellite stereos is unstable, especially for intractable regions such as texture‐less and occluded regions. Third, generating DSMs requires occlusion‐aware disparity maps, while traditional occlusion detection methods are not always applicable for DLSMs with continuous disparity. To tackle these problems, this paper proposes a novel DLSM‐based DSM generation workflow. The workflow comprises three steps: pre‐processing, disparity estimation and post‐processing. The pre‐processing step introduces low‐resolution terrain to shift unmatched disparity ranges into a fixed scope and crops satellite stereos to regular patches. The disparity estimation step proposes a hybrid feature fusion network (HF2Net) to improve the matching performance. In detail, HF2Net designs a cross‐scale feature extractor (CSF) and a multi‐scale cost filter. The feature extractor differentiates structural‐context features in complex scenes and thus enhances HF2Net's robustness to satellite stereos, especially on intractable regions. The cost filter filters out most matching errors to ensure accurate disparity estimation. The post‐processing step generates initial DSM patches with estimated disparity maps and then refines them for the final large‐scale DSMs. Primary experiments on the public US3D dataset showed better accuracy than state‐of‐the‐art methods, indicating HF2Net's superiority. We then created a self‐made Gaofen‐7 dataset to train HF2Net and conducted DSM generation experiments on two Gaofen‐7 stereos to further demonstrate the effectiveness and practical capability of the proposed workflow.