Terrain Reconstruction Research Articles

Analysis of foothold selection during locomotion using terrain reconstruction.

Relatively little is known about the way vision is used to guide locomotion in the natural world. What visual features are used to choose paths in natural complex terrain? To answer this question, we measured eye and body movements while participants walked in natural outdoor environments. We incorporated measurements of the three-dimensional (3D) terrain structure into our analyses and reconstructed the terrain along the walker's path, applying photogrammetry techniques to the eye tracker's scene camera videos. Combining these reconstructions with the walker's body movements, we demonstrate that walkers take terrain structure into account when selecting paths through an environment. We find that they change direction to avoid taking steeper steps that involve large height changes, instead of choosing more circuitous, relatively flat paths. Our data suggest walkers plan the location of individual footholds and plan ahead to select flatter paths. These results provide evidence that locomotor behavior in natural environments is controlled by decision mechanisms that account for multiple factors, including sensory and motor information, costs, and path planning.

Analysis of foothold selection during locomotion using terrain reconstruction

Relatively little is known about the way vision is used to guide locomotion in the natural world. What visual features are used to choose paths in natural complex terrain? To answer this question, we measured eye and body movements while participants walked in natural outdoor environments. We incorporated measurements of the three-dimensional (3D) terrain structure into our analyses and reconstructed the terrain along the walker’s path, applying photogrammetry techniques to the eye tracker’s scene camera videos. Combining these reconstructions with the walker’s body movements, we demonstrate that walkers take terrain structure into account when selecting paths through an environment. We find that they change direction to avoid taking steeper steps that involve large height changes, instead of choosing more circuitous, relatively flat paths. Our data suggest walkers plan the location of individual footholds and plan ahead to select flatter paths. These results provide evidence that locomotor behavior in natural environments is controlled by decision mechanisms that account for multiple factors, including sensory and motor information, costs, and path planning.

Precise beam control in optical phased arrays using the four steps rotating element electric field vector method

The integrated optical phase arrays (OPAs) possess the capability for rapid modulation and precise control of output beam deflection, making it widely applicable in fields such as three-dimensional terrain reconstruction, autonomous driving, and holographic imaging. However, the unknown initial phase introduced during the manufacturing and packaging processes of current OPAs results in low beam alignment quality and random output beam phases, significantly limiting the development and application of OPAs. To address these challenges, this paper proposes a precise control technology for OPA output beams, utilizing a beam calibration method we have developed, known as the Four Steps Rotating Element Electric Field Vector Method. This method enables rapid and accurate calibration, achieving precise phase control for each antenna on the OPA chip by calibrating the phase shift and controlling the voltage relationship. It overcomes the challenges of unknown phase distributions common in passive calibration methods, aligning the calibrated phase distribution more closely with theoretical expectations. The proposed method further enhances control over the OPA output beam. Based on this technology, we constructed an experimental platform to achieve a main lobe with a PSLR of 15.98 dB and successfully generated vortex beams using a 4×4 OPA. This innovation not only addresses the initial phase issues caused by manufacturing errors but also significantly enhances the precise control of OPA phases, expanding its applications in LiDAR systems.

Generative 3D Reconstruction of Martian Surfaces using Monocular Images

Abstract. High-resolution digital terrain models (DEM) are crucial for Mars scientific exploration and engineering demands, such as mission route planning, landing site selection, and topography research. However, DEMs generated based on photogrammetry or photometry suffer from limited stereo coverage and complex generative processes. Therefore, we propose a GAN-based monocular 3D reconstruction method, which utilizes high-resolution monocular orbital images to achieve pixel-level Martian terrain reconstruction. We preprocess the image data and then invert the elevation using the trained generative model. Finally, we recover the absolute scale through post-processing. In this work, we use HiRISE orthorectified images and DEMs with a resolution of 2 m and 0.25 m to validate the effectiveness of our method. We evaluated our method in four areas with different landforms and found that the predicted DEM and HiRISE DEM have height consistency with the root mean square error of about 2 m. This indicates that the proposed GAN-based method has certain effectiveness and generalization and great potential in high-resolution monocular Martian DEM reconstruction.

From land to ocean: bathymetric terrain reconstruction via conditional generative adversarial network

From land to ocean: bathymetric terrain reconstruction via conditional generative adversarial network

Large-Scale 3D Terrain Reconstruction Using 3D Gaussian Splatting for Visualization and Simulation

Abstract. The fusion of low-cost unmanned aerial systems (UAS) with advanced photogrammetric techniques has revolutionized 3D terrain reconstruction, enabling the automated creation of detailed models. Concurrently, the advent of 3D Gaussian Splatting has introduced a paradigm shift in 3D data representation, offering visually realistic renditions distinct from traditional polygon-based models. Our research builds upon this foundation, aiming to integrate Gaussian Splatting into interactive simulations for immersive virtual environments. We address challenges such as collision detection by adopting a hybrid approach, combining Gaussian Splatting with photogrammetry-derived meshes. Through comprehensive experimentation covering varying terrain sizes and Gaussian densities, we evaluate scalability, performance, and limitations. Our findings contribute to advancing the use of advanced computer graphics techniques for enhanced 3D terrain visualization and simulation.

Unsupervised Terrain Reconstruction From Side-Scan Sonar Constrained to the Imaging Mechanism

Unsupervised Terrain Reconstruction From Side-Scan Sonar Constrained to the Imaging Mechanism

A deep learning-based local feature extraction method for improved image matching and surface reconstruction from Yutu-2 PCAM images on the Moon

Topographic reconstruction of the lunar surface or other planets is important to engineering applications and scientific research in a planetary exploration mission. The typical methods of terrain reconstruction are usually based on photogrammetry techniques. Structure-from-Motion (SfM) is one of the most effective and commonly used photogrammetric technologies that estimate three-dimensional structures from two-dimensional image sequences. To find correspondences and align photos, SfM approaches require invariant features, such as Scale Invariant Feature Transform (SIFT). The hand-crafted features, however, seriously degrade performance due to weak texture and low light conditions causing image matching failure, which will directly affect the accuracy and robustness of reconstruction. Robust local descriptors based on deep learning outperform hand-crafted descriptors since convolutional neural networks are more robust than hand-engineered representations. Therefore, a novel and robust deep learning-based local feature extraction method is proposed, comprising two branch networks integrated with attention mechanisms for generating reliable keypoints and descriptors, respectively. Furthermore, a 3D terrain surface reconstruction workflow is constructed by combining it with the modern advanced image matching method and SfM system. The effectiveness of the proposed method and the workflow were verified in experiments using Panoramic Camera (PCAM) images acquired from three waypoints explored by the Yutu-2 lunar rover during the Chang’e-4 mission. We also illustrate how our approach supports other applications, such as creating panoramic mosaics of surface imagery. This provides a new and powerful method for planetary terrain reconstruction at a high spatial resolution that can meet the requirements for rover navigation and positioning, as well as geological analysis of the Moon and other planets. The source codes developed in this study are openly available at https://github.com/Atypical-Programmer/Deep_Reconstruction_Workflow.

Integrating Topographic Skeleton into Deep Learning for Terrain Reconstruction from GDEM and Google Earth Image

The topographic skeleton is the primary expression and intuitive understanding of topographic relief. This study integrated a topographic skeleton into deep learning for terrain reconstruction. Firstly, a topographic skeleton, such as valley, ridge, and gully lines, was extracted from a global digital elevation model (GDEM) and Google Earth Image (GEI). Then, the Conditional Generative Adversarial Network (CGAN) was used to learn the elevation sequence information between the topographic skeleton and high-precision 5 m DEMs. Thirdly, different combinations of topographic skeletons extracted from 5 m, 12.5 m, and 30 m DEMs and a 1 m GEI were compared for reconstructing 5 m DEMs. The results show the following: (1) from the perspective of the visual effect, the 5 m DEMs generated with the three combinations (5 m DEM + 1 m GEI, 12.5 m DEM + 1 m GEI, and 30 m DEM + 1 m GEI) were all similar to the original 5 m DEM (reference data), which provides a markedly increased level of terrain detail information when compared to the traditional interpolation methods; (2) from the perspective of elevation accuracy, the 5 m DEMs reconstructed by the three combinations have a high correlation (>0.9) with the reference data, while the vertical accuracy of the 12.5 m DEM + 1 m GEI combination is obviously higher than that of the 30 m DEM + 1 m GEI combination; and (3) from the perspective of topographic factors, the distribution trends of the reconstructed 5 m DEMs are all close to the reference data in terms of the extracted slope and aspect. This study enhances the quality of open-source DEMs and introduces innovative ideas for producing high-precision DEMs. Among the three combinations, we recommend the 12.5 m DEM + 1 m GEI combination for DEM reconstruction due to its relative high accuracy and open access. In regions where a field survey of high-precision DEMs is difficult, open-source DEMs combined with GEI can be used in high-precision DEM reconstruction.

Optimal Landing Zone Selection and Online Trajectory Programming Method for Lunar Lander

To achieve a safe landing on the lunar surface with complex terrain, online landing zone selection is required. A DEM-based landing zone selection technique is proposed. The method firstly obtains several candidate landing zones by analyzing the gradient and variance information from the DEM, and then selects the integrated candidate landing zone by the overlap ratio. Finally, the optimal trajectory programming is carried out with the current state of the lander as the initial value and each integrated candidate landing zone as the final position, and the optimal landing zone is selected by comparing the required fuel to each landing zone. Compared with the classical DEM-based online landing site selection technique, the proposed method does not perform terrain reconstruction, responds quickly, and achieves real-time performance, and it also reduces fuel consumption, provides residual fuel for the subsequent landing phase, and increases the reliability of the landing process. Simulation results show that the proposed method is able to select several candidate landing zones in complex terrain, meanwhile, it is capable of achieving the optimal trajectory programming with the target of these several candidate landing zones, and finally, the optimal landing zone is selected by comparing the fuel consumption of each path.

Research on the purification enhancement of ecological ponds: Integrating water cycle optimization and plants layout

The hydrodynamic conditions of ponds are generally poor, which seriously affects the long–term water quality guarantee. In this research, the numerical simulation method was used to establish an integrated model of hydrodynamics and water quality for the simulation of the plant purification effect in ponds. Based on the flushing time using the tracer method, the purification rate of plants was introduced to consider the purification effect of plants on water quality. In–situ monitoring was carried out at the Luxihe pond in Chengdu, and the model parameters such as the purification rate of typical plants were calibrated. The degradation coefficient of NH3–N in the non–vegetated area was 0.014 d−1 in August and 0.010 d−1 in November. In areas with vegetation, the purification rate of NH3–N was 0.10–0.20 g/(m2·d) in August and 0.06–0.12 g/(m2·d) in November. The comparison of the results in August and November showed that due to the higher temperature in August, the plant growth effect was better, and the degradation rate of pollutants and the purification rate of pollutants by plants were higher.The flushing time distribution of the proposed Baihedao pond under the conditions of terrain reconstruction, water replenishment, and plant layout was simulated, and the frequency distribution curve of flushing time was used to evaluate the results. Terrain reconstruction and water replenishment can significantly improve the water exchange capacity of ponds. The reasonable planting of plants can reduce the variability of the water exchange capacity. Based on this combined with the purification effect of plants on NH3–N, the layout plan of Canna, Cattails, and Thalia in ponds was proposed.

Filtering Airborne LiDAR Data in Forested Environments Based on Multi-Directional Narrow Window and Cloth Simulation

Ground filtering is one of the essential steps for processing airborne light detection and ranging data in forestry applications. However, the performance of existing methods is still limited in forested areas due to the complex terrain and dense vegetation. To overcome this limitation, we proposed an improved surface-based filter based on multi-directional narrow window and cloth simulation. The innovations mainly involve two aspects as follows: (1) sufficient and uniformly distributed ground seeds are identified by merging the lowest points and line segments from the point clouds within a multi-directional narrow window; (2) complete and accurate ground points are extracted using a cyclic scheme that includes incorrect ground point elimination using the internal force adjustment of cloth simulation, terrain reconstruction with moving least-squares plane fitting, and ground point extraction based on progressively refined terrain. The proposed method was tested in five forested sites with various terrain characteristics and vegetation distributions. Experimental results showed that the proposed method could accurately separate ground points from non-ground points in different forested environments, with the average kappa coefficient of 88.51% and total error of 4.22%. Moreover, the comparative experiments proved that the proposed method performed better than the classical methods involving the slope-based, mathematical morphology-based and surface-based methods.

Real-Time Neural Dense Elevation Mapping for Urban Terrain With Uncertainty Estimations

Having good knowledge of terrain information is essential for improving the performance of various downstream tasks on complex terrains, especially for the locomotion and navigation of legged robots. We present a novel framework for neural urban terrain reconstruction with uncertainty estimations. It generates dense robot-centric elevation maps online from sparse LiDAR observations. We design a novel pre-processing and point features representation approach that ensures high robustness and computational efficiency when integrating multiple point cloud frames. A generative Bayesian model then recovers the detailed terrain structures while simultaneously providing the pixel-wise reconstruction uncertainty. We evaluate the proposed pipeline through both simulation and real-world experiments. Our approach achieves high-quality terrain reconstruction with real-time performance on a mobile platform, and the uncertainty estimates may further benefit the downstream tasks of legged robots.

Extraction of terrain ridge lines and valley lines based on agglomeration analysis of terrain points: a cluster analysis method

Terrain feature extraction is one of the critical issues in geographic information science. As important terrain feature lines, ridge lines and valley lines play an important role in hydrological analysis, terrain reconstruction and automatic integration of contour lines. But the extraction of terrain feature lines is complicated and time-consuming task. In this paper, a terrain feature line extraction method is proposed based on clustering technique. The terrain feature points are automatically extracted according to the agglomeration of terrain points, and the similar points are automatically identified according to the DBSCAN clustering algorithm. The points with high similarity are clustered along the direction of ridge or valley, and the whole terrain will be clustered into multiple sub-regions. The nearest sub-regions are found by calculating the minimum distance between these sub-regions, the adjacent sub-regions are connected orderly by their center line to obtain terrain feature lines. Compared with other methods, the cluster analysis method in this paper has simple process and high efficiency.

A Learning-Free Method for Locomotion Mode Prediction by Terrain Reconstruction and Visual-Inertial Odometry.

This research introduces a novel, highly precise, and learning-free approach to locomotion mode prediction, a technique with potential for broad applications in the field of lower-limb wearable robotics. This study represents the pioneering effort to amalgamate 3D reconstruction and Visual-Inertial Odometry (VIO) into a locomotion mode prediction method, which yields robust prediction performance across diverse subjects and terrains, and resilience against various factors including camera view, walking direction, step size, and disturbances from moving obstacles without the need of parameter adjustments. The proposed Depth-enhanced Visual-Inertial Odometry (D-VIO) has been meticulously designed to operate within computational constraints of wearable configurations while demonstrating resilience against unpredictable human movements and sparse features. Evidence of its effectiveness, both in terms of accuracy and operational time consumption, is substantiated through tests conducted using open-source dataset and closed-loop evaluations. Comprehensive experiments were undertaken to validate its prediction accuracy across various test conditions such as subjects, scenarios, sensor mounting positions, camera views, step sizes, walking directions, and disturbances from moving obstacles. A comprehensive prediction accuracy rate of 99.00% confirms the efficacy, generality, and robustness of the proposed method.

A Deep Learning Solution for Height Estimation on a Forested Area Based on Pol-TomoSAR Data

Forest height and underlying terrain reconstruction is one of the main aims in dealing with forested areas. Theoretically, Synthetic Aperture Radar Tomography (TomoSAR) offers the possibility to solve the layover problem, making it possible to estimate the elevation of scatters located in the same resolution cell. This paper describes a deep learning approach, named Tomographic SAR Neural Network (TSNN), that aims at reconstructing forest and ground height using multipolarimetric multibaseline (MPMB) SAR data and Light Detection and Ranging (LiDAR) based data. The reconstruction of the forest and ground height is formulated as a classification problem, in which TSNN, a feed-forward network, is trained using covariance matrix elements as input vectors and quantized LiDAR-based data as the reference. In our work, TSNN is trained and tested with P-band MPMB data acquired by ONERA over Paracou region of French Guiana in the frame of the European Space Agency’s campaign TROPISAR and LiDAR-based data provided by the French Agricultural Research Center. The novelty of the proposed TSNN is related to its ability to estimate height with a high agreement with LiDAR-based measurement and actual height with no requirement for phase calibration. Experimental results of different covariance window sizes are included to demonstrate TSNN conducts height measurement with high spatial resolution and vertical accuracy outperforming the other two TomoSAR methods. Moreover, the conducted experiments on the effects of phase errors in different ranges show that TSNN has a good tolerance for small errors and is still able to precisely reconstruct forest heights.

Automatic Calibration between Multi-Lines LiDAR and Visible Light Camera Based on Edge Refinement and Virtual Mask Matching

To assist in the implementation of a fine 3D terrain reconstruction of the scene in remote sensing applications, an automatic joint calibration method between light detection and ranging (LiDAR) and visible light camera based on edge points refinement and virtual mask matching is proposed in this paper. The proposed method is used to solve the problem of inaccurate edge estimation of LiDAR with different horizontal angle resolutions and low calibration efficiency. First, we design a novel calibration target, adding four hollow rectangles for fully automatic locating of the calibration target and increasing the number of corner points. Second, an edge refinement strategy based on background point clouds is proposed to estimate the target edge more accurately. Third, a two-step method of automatically matching between the calibration target in 3D point clouds and the 2D image is proposed. Through this method, i.e., locating firstly and then fine processing, corner points can be automatically obtained, which can greatly reduce the manual operation. Finally, a joint optimization equation is established to optimize the camera’s intrinsic and extrinsic parameters of LiDAR and camera. According to our experiments, we prove the accuracy and robustness of the proposed method through projection and data consistency verifications. The accuracy can be improved by at least 15.0% when testing on the comparable traditional methods. The final results verify that our method is applicable to LiDAR with large horizontal angle resolutions.

An urban DEM reconstruction method based on multisource data fusion for urban pluvial flooding simulation

A digital elevation model (DEM) is crucial for hydraulic/hydrodynamic simulation of urban pluvial flooding. To reduce the simulation error caused by the low resolution and poor vertical accuracy of DEM data, a novel urban terrain reconstruction method is proposed based on multisource data fusion (MDF). A 1D-2D coupled pluvial flood model under the typhoon rainfall scenario is built to verify the superiority of the proposed method. The results show that the systematic elevation error of terrain is reduced with MDF-kriging and MDF-IDW, and the performances of simulations are improved by 5% and 23% in terms of peak water-level error compared to using conventional kriging and IDW methods, and by 0.13m and 0.11m in terms of manhole water level accuracy compared to using raw DEM. Moreover, the temporospatial patterns of the flooding simulation results are more rational with the scattered flooding eliminated, and the proportion of flood area with ground elevation lower than 8 m increased from 37.9% to 92.34% and 89.33%, respectively. This method is proven to be a notably effective and low-cost DEM reconstruction method for improving the accuracy of urban pluvial flooding simulation.

An InSAR Interferogram Filtering Method Based on Multi-Level Feature Fusion CNN.

Interferogram filtering is an essential step in processing data from interferometric synthetic aperture radar (InSAR), which greatly improves the accuracy of terrain reconstruction and deformation monitoring. Most traditional interferogram filtering methods achieve noise suppression and detail preservation through morphological estimation based on the statistical properties of the interferogram in the spatial or frequency domain. However, as the interferogram’s spatial distribution is diverse and complex, traditional filtering methods struggle to adapt to different distribution and noise conditions and cannot handle detail preservation and noise suppression simultaneously. The study proposes a convolutional neural network (CNN)-based multi-level feature fusion model for interferogram filtering that differs from the traditional feedforward neural network (FNN). Adopting a multi-depth multi-path convolution strategy, the method preserves phase details and suppresses noise during interferogram filtering. In filtering experiments based on simulated data, qualitative and quantitative evaluations were used to validate the performance and generalization capabilities of the proposed method. The method’s applicability was evaluated by visual observation during filtering and unwrapping experiments on real data, and the time-series deformation acquired by time series (TS)-InSAR technique is used to evaluate the effect of interferogram filters on deformation monitoring accuracy. Compared to commonly used interferogram filtering methods, the proposed method has significant advantages in terms of performance and efficiency. The study findings suggest new directions for research on high-precision InSAR data processing and provide technical support for practical applications of InSAR.

A novel precise pose prediction algorithm for setting the sleeping mode of the Yutu-2 rover based on a multiview block bundle adjustment

AbstractTo set the sleeping mode for the Yutu-2 rover, a visual pose prediction algorithm including terrain reconstruction and pose estimation was first studied. The terrain reconstruction precision is affected by using only the stereo navigation camera (Navcam) images and the rotation angles of the mast. However, the hazard camera (Hazcam) pose is fixed, and an image network was constructed by linking all of the Navcam and Hazcam stereoimages. Then, the Navcam pose was refined based on a multiview block bundle adjustment. The experimental results show that the mean absolute errors of the check points in the proposed algorithm were 10.4 mm over the range of $\boldsymbol{L}$ from 2.0 to 6.1 m, and the proposed algorithm achieved good prediction results for the rover pose (the average differences of the values of the pitch angle and the roll angle were −0.19 degrees and 0.29 degrees, respectively). Under the support of the proposed algorithm, engineers have completed the remote setting of the sleeping mode for Yutu-2 successfully in the Chang’e-4 mission operations.