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- 摘要
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Improved forest height estimation by fusion of simulated GEDI Lidar data and TanDEM-X InSAR data
摘要： Interferometric Synthetic Aperture Radar (InSAR) and lidar are increasingly used active remote sensing techniques for forest structure observation. The TanDEM-X (TDX) InSAR mission of German Aerospace Center (DLR) and the upcoming Global Ecosystem Dynamics Investigation (GEDI) of National Aeronautics and Space Administration (NASA) together may provide more accurate estimates of global forest structure and biomass via their synergic use. In this paper, we explored the efficacy of simulated GEDI data in improving height estimates from TDX InSAR data. Our study sites span three major forest types: a temperate forest, a mountainous conifer forest, and a tropical rainforest. The GEDI lidar coverage was simulated for the full nominal two-year mission duration, under both cloud-free and 50%-cloud conditions. We then used these GEDI data to parameterize the Random Volume over Ground (RVoG) model driven by TDX imagery. In particular, we explored the following three strategies for forest structure estimation: 1) TDX data alone; 2) TDX + GEDI-derived digital terrain model (DTM); and 3) TDX + GEDI DTM + GEDI canopy height. We then validated the retrieved forest heights against wall-to-wall airborne lidar measurements. We found relatively large biases at 90 [m] spatial resolution, from 4.2–11.9 [m], and root mean square errors (RMSEs), from 7.9–12.7 [m] when using TDX data alone under constrained RVoG assumptions of a fixed extinction coefficient (σ) and a zero ground-to-volume amplitude ratio (μ = 0). Results improved significantly with the aid of a DTM derived from GEDI data which enabled estimation of spatially-varying σ values (vs. fixed extinction) under a μ = 0 assumption, with biases reduced to 1.7–4.2 [m] and RMSEs to 4.9–8.6 [m] across cloudy and cloud-free cases. The best agreement was achieved in the third strategy by also incorporating information of GEDI-derived canopy height to further enhance the RVoG parameters. The improved model, when still assuming μ = 0, reduced biases to less than or close to 1 m and further reduced RMSEs to 4.0–6.7 [m]. Finally, we used GEDI data to estimate spatially-varying μ in the RVoG model. We found biases of between ?0.7–0.9 [m] and RMSEs in the range from 2.6–7.1 [m] over the three sites. Our results suggest that use of GEDI data improves height inversion from TDX, providing heights at more accuracy than can be achieved by TDX alone, and enabling wall-to-wall height estimation at much finer spatial resolution than can be achieved by GEDI alone.
关键词： Lidar,GEDI,ALS,Forest height,TanDEM-X,InSAR,RVoG
更新于2025-09-23 15:19:57
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Have I Seen This Place Before? A Fast and Robust Loop Detection and Correction Method for 3D Lidar SLAM
摘要： In this paper, we present a complete loop detection and correction system developed for data originating from lidar scanners. Regarding detection, we propose a combination of a global point cloud matcher with a novel registration algorithm to determine loop candidates in a highly effective way. The registration method can deal with point clouds that are largely deviating in orientation while improving the efficiency over existing techniques. In addition, we accelerated the computation of the global point cloud matcher by a factor of 2–4, exploiting the GPU to its maximum. Experiments demonstrated that our combined approach more reliably detects loops in lidar data compared to other point cloud matchers as it leads to better precision–recall trade-offs: for nearly 100% recall, we gain up to 7% in precision. Finally, we present a novel loop correction algorithm that leads to an improvement by a factor of 2 on the average and median pose error, while at the same time only requires a handful of seconds to complete.
关键词： loop detection,point clouds,lidar
更新于2025-09-19 17:15:36
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A Least Squares Collocation Method for Accuracy Improvement of Mobile LiDAR Systems
摘要： In environments that are hostile to Global Navigation Satellites Systems (GNSS), the precision achieved by a mobile light detection and ranging (LiDAR) system (MLS) can deteriorate into the sub-meter or even the meter range due to errors in the positioning and orientation system (POS). This paper proposes a novel least squares collocation (LSC)-based method to improve the accuracy of the MLS in these hostile environments. Through a thorough consideration of the characteristics of POS errors, the proposed LSC-based method effectively corrects these errors using LiDAR control points, thereby improving the accuracy of the MLS. This method is also applied to the calibration of misalignment between the laser scanner and the POS. Several datasets from different scenarios have been adopted in order to evaluate the effectiveness of the proposed method. The results from experiments indicate that this method would represent a significant improvement in terms of the accuracy of the MLS in environments that are essentially hostile to GNSS and is also effective regarding the calibration of misalignment.
关键词： Global Navigation Satellites Systems,mobile LiDAR system,least squares collocation,positioning and orientation system
更新于2025-09-19 17:15:36
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[IEEE 2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW) - Takayama, Japan (2018.11.27-2018.11.30)] 2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW) - A Secure LiDAR with AES-Based Side-Channel Fingerprinting
摘要： Sensor spoo?ng attack is an emerging threat to laser-based ranging. In this paper, we propose a countermeasure that superimposes authentication ?ngerprint onto light wave itself. In the proposed method, ampli?cation of laser output is directly modulated by power side-channel information leaked from a cryptographic device. The feasibility of the concept is veri?ed through experiments.
关键词： side-channel authentication,analog modulation,spoo?ng attack,Lidar
更新于2025-09-19 17:15:36
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[IEEE 2018 International Joint Conference on Neural Networks (IJCNN) - Rio de Janeiro (2018.7.8-2018.7.13)] 2018 International Joint Conference on Neural Networks (IJCNN) - DHA: Lidar and Vision data Fusion-based On Road Object Classifier
摘要： In this paper, we first extract three different kinds of high-level features from LIDAR point cloud, and combine them into the DHA (Depth, Height and Angle) channels. Integrated with the traditional RGB image from camera, we build a rich feature-based road object classifier by training a deep convolutional neural network model with six-channel (RGBDHA) data. Subsequently, this deep convolution neural network is fed by the integration of spacial and RGB information. With additional upsampled LIDAR data, the classifier reaches higher accuracy than single RGB image base methods. Several simulations on the famous autonomous vehicle benchmark of KITTI show that our fusion-based classifier outperforms RGB-based approaches about 15% and reaches average accuracy of 96%.
关键词： Deep learning,Autonomous vehicle,LIDAR,Sensor fusion
更新于2025-09-19 17:15:36
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[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Classification of LiDAR Point Cloud based on Multiscale Features and PointNet
摘要： Aiming at classifying the feature of LiDAR point cloud data in complex scenario, this paper proposed a deep neural network model based on multi-scale features and PointNet. The method improves the local feature of PointNet and realize automatic classification of LiDAR point cloud under the complex scene. Firstly, this paper adds multi-scale network on the basis of PointNet network to extract the local features of points. And then these local features of different scales are composed into a multi-dimensional feature through the fully connected layer, and combined with the global features extracted by PointNet, the scores of each point class are returned to complete the point cloud classification. The deep neural network model proposed in this paper is verified using the Semantic3D dataset and the Vaihingen dataset provided by ISPRS. The experimental results show that the proposed algorithm achieves higher classification accuracy compared with other neural networks used for point cloud classification.
关键词： Classification of point cloud,multi-scale features,PointNet,LiDAR
更新于2025-09-19 17:15:36
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Echo characteristics of polarized heterodyne lidar in nonspherical aerosol environments
摘要： Heterodyne lidar echoes in atmospheric detection are greatly affected by the shapes of aerosols. With the aid of T-matrix and vector Monte Carlo simulation, we investigated the properties of lidar echoes backscattered by randomly oriented polydisperse aerosols, including the soot, sea salt and mineral aerosols. The degree of polarization (DoP) and backscattered photon numbers in different range bins are calculated when the launched laser pulses are linearly polarized light and circularly polarized light at 1.55 μm. There are reductions of DoP along the 10 km detection path, which vary with aspect ratios (AR) and aerosol types. The ARs are chosen within 0.5–2.0 for cylinders and 0.3–1.0 for spheroids. The lidar echoes in soot aerosols have the largest DoP which is higher than 0.8. Moreover, the shape fading factor and polarization fading factor are defined and calculated based on the effective backscattered photon numbers, which shows that the mineral aerosols in nuc.mode (MINM) is less affected by aerosol shapes and laser polarization states, and that the LPL is suitable for the heterodyne detection of nonspherical atmospheric aerosols. The results provide echo characteristic deviations from the spherical particle scattering, which can be used in the practical modeling of atmospheric echoes and designs of heterodyne lidars.
关键词： Nonspherical aerosols,T-matrix,Heterodyne lidar,Polarization
更新于2025-09-19 17:15:36
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[IEEE 2018 Asia Communications and Photonics Conference (ACP) - Hangzhou, China (2018.10.26-2018.10.29)] 2018 Asia Communications and Photonics Conference (ACP) - Dispersion-engineered Optical Phased Array for Aliasing-free Beam-steering with a Plateau Envelope
摘要： We design and fabricate a chip-scale optical phased array (OPA) with a uniform emitting structure composed of bent waveguides. This silicon-on-insulator based device demonstrates aliasing-free beam-steering over the entire field-of-view available (-34°~34°), with a far-field addressability of 6.71°. In addition, the steering process exhibits a plateau envelope, with an intensity fluctuation of less than 0.45 dB from -30°~30°.
关键词： free space optical communications,photonic integrated circuits,integrated optics,LiDAR
更新于2025-09-19 17:15:36
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Real-Time Visualization Method for Estimating 3D Highway Sight Distance Using LiDAR Data
摘要： Light detection and ranging (LiDAR) data provide a rather precise depiction of the real three-dimensional (3D) road environment and have been used by some researchers to produce more precise available sight distance (ASD) results compared with those obtained based on conventional digital elevation models with low resolution. However, existing methods have some difficulties in creating digital surface models to accurately estimate ASD using LiDAR data. In addition, dynamic visualization of the driver’s visual conditions along the highway throughout ASD assessment (which is important for monitoring the results in real time) has not been achieved by existing studies. To fill these gaps, this paper discusses the development of a new procedure supported by MATLAB for evaluating, in a real-time visualization manner, ASD along an existing highway based on LiDAR data. With an innovative algorithm that combines cylindrical perspective projection and modified Delaunay triangulation, the computation is processed in real time along the vehicle trajectory, which is represented by a set of points, whereas the driver’s successive perspective views and sight distance results are generated simultaneously. A comparative case study is presented to demonstrate that the new method is more accurate than conventional methods and more flexible for evaluating ASD along highways with complicated roadside components.
关键词： Algorithm,Light detection and ranging (LiDAR) data.,Highway safety,Real time,Three-dimensional sight distance
更新于2025-09-19 17:15:36
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Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification
摘要： Asphalt pavement ages and incurs various distresses due to natural and human factors. Thus, it is crucial to rapidly and accurately extract different types of pavement distress to effectively monitor road health status. In this study, we explored the feasibility of pavement distress identification using low-altitude unmanned aerial vehicle light detection and ranging (UAV LiDAR) and random forest classification (RFC) for a section of an asphalt road that is located in the suburb of Shihezi City in Xinjiang Province of China. After a spectral and spatial feature analysis of pavement distress, a total of 48 multidimensional and multiscale features were extracted based on the strength of the point cloud elevations and reflection intensities. Subsequently, we extracted the pavement distresses from the multifeature dataset by utilizing the RFC method. The overall accuracy of the distress identification was 92.3%, and the kappa coefficient was 0.902. When compared with the maximum likelihood classification (MLC) and support vector machine (SVM), the RFC had a higher accuracy, which confirms its robustness and applicability to multisample and high-dimensional data classification. Furthermore, the method achieved an overall accuracy of 95.86% with a validation dataset. This result indicates the validity and stability of our method, which highway maintenance agencies can use to evaluate road health conditions and implement maintenance.
关键词： UAV,random forest classification,pavement health conditions,LiDAR,asphalt pavement distresses,multiscale features
更新于2025-09-19 17:15:36