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In this paper, we propose a simple method to generate the uniform illumination using a pyramid prism for Plane Array Laser Imaging Detection and Ranging (PA-LiDAR). The principle of the pyramid prism shaping the Gaussian beam to form a uniform beam was analyzed theoretically. By changing the parameters of the pyramid prism and laser beam, the profile distribution of the output beam can be easily adjusted. Based on the operation mode and illumination requirements of PA-LiDAR, we have developed a set of LiDAR prototypes using a pyramid prism and carried out experimental research on these prototypes. The simulation and experimental results demonstrated that this method can achieve a uniform illumination beam with excellent propagation properties for meeting the technical requirements of PA-LiDAR. This method of uniform illumination has the advantages of being simple, flexible, easily adjustable and convenient to operate. [ABSTRACT FROM AUTHOR]

Lidar data, which can describe the 3D spatial information of the environment in the form of point clouds, play an important role in autonomous driving and related downstream tasks such as 3D object detection. However, unlike for images, collecting and labeling lidar data is often very expensive. As an effective means to increase the quantity of annotated data for training deep learning models, data augmentation (DA) has been widely used in the image field, but studies on augmenting lidar point clouds are only at the beginning stage. In this article, we propose AdaMix, a novel framework for lidar DA via adaptive resampling of multiscale object mixup. AdaMix contains two different object mixup schemes, i.e., object-level and part-level mixup, to augment the lidar data with the existing object instances from different scenes. For object-level mixup, a learning-based point upsampling operation is employed to obtain a set of dense objects, such as vehicles and pedestrians. For part-level mixup, parts from different vehicles are composed together and upsampled to generate vehicles of complete and dense shapes. To mix the dense objects into a new scene, AdaMix introduces a novel projection-based downsampling method to adaptively downsample the objects based on the location generated from a location sampling module. We evaluate the performance of AdaMix with several 3D object detection models on the KITTI dataset. Experimental results demonstrate that AdaMix consistently surpasses state-of-the-art lidar DA methods in improving the average precision of vehicle and pedestrian detection. [ABSTRACT FROM AUTHOR]

【Objective】Single-photon detection and single-photon ranging systems have widespread applications in three-dimensional imaging and long-distance spatial remote sensing. However, device limitations and background noise from the sun light limit the application to only nighttime conditions.【Methods】We have improved traditional laser radar systems by implementing all-fiber optics systems with ultra-narrowband fiber optic filtering, leading to significantly improved system stability. By implementing mechanical motion control, we are able to attain single-photon imaging systems with ultra-wide scanning and ultra-high resolution. The systems can operate in optical environments that are typically challenging, such as daytime or foggy weather. Moreover, the laser radar system is designed based on the 1 550 nm wavelength which has good atmospheric penetration and low transmission loss, enhancing its ability to work in foggy and rainy conditions.【Results】In a foggy condition with a visibility of about 500 m, the system achieves three-dimensional imaging of objects 1.6 km away, which is a distance 3 times longer than the visibility at the time. The distance resolution can be significantly enhanced by optimizing the intensity and distance information of the histogram using the minimized negative logarithmic likelihood function, with the assistance of a high-resolution time/digital converter and the model constructed by the system response function of the laser radar. Compared to the maximum value method with a distance resolution of 0.05 m, this system achieves a distance resolution of 0.006 m via the minimized negative logarithmic likelihood function method. Additionally, the image normalization processing is performed to suppress fluctuations in photon technology and eliminate noise in the image. The 1 550 nm all-fiber laser radar system has strong mechanical stability and can work in adverse weather conditions.【Conclusion】It has significant application value in remote sensing and mapping, ground and vehicle-mounted laser radar, and other fields.

Copyright of Study on Optical Communications / Guangtongxin Yanjiu is the property of Study on Optical Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most existing road infrastructures were constructed before the emergence of automated vehicles (AVs) without considering their operational needs. Whether and how AVs could safely adapt to as-built highway geometry are questions that remain inconclusive, and a plausible concern is a challenge from vertical alignments. To fill this gap, this study uses a virtual simulation to investigate the available sight distance (ASD) of AVs on vertical alignments subject to the current highway geometric design specification and its implications for speed limits. According to the scenario generation framework, several scenarios featuring vertical geometric elements and lidar sensors were created and tested. Moreover, the maximum speed for adequate ASD is calculated to determine the AV speed limit, considering safe sight distance and speed consistency requirements. The results indicate that crest curves are not disadvantaged in ASD compared with either sag curves or tangent grades. Only equipped with multichannel lidar and advanced perception algorithms enabling a lower detection threshold would a level 4 AV be compatible with the as-built vertical alignment with a design speed (Vd) of 100 km/h. However, a level 3 AV can only adapt to the vertical profile with Vd = 60 km/h. The findings of this study should be of interest to the road-oriented operational design domain and support road administrators in regulating AV safe speeds. [ABSTRACT FROM AUTHOR]

The acquisition and analysis of high-quality sensor data constitute an essential requirement in shaping the development of fully autonomous driving systems. This process is indispensable for enhancing road safety and ensuring the effectiveness of the technological advancements in the automotive industry. This study introduces the Interaction of Autonomous and Manually Controlled Vehicles (IAMCV) dataset, a novel and extensive dataset focused on intervehicle interactions. The dataset, enriched with a sophisticated array of sensors such as lidar, cameras, inertial measurement unit/Global Positioning System, and vehicle bus data acquisition, provides a comprehensive representation of real-world driving scenarios that include roundabouts, intersections, country roads, and highways, recorded across diverse locations in Germany. Furthermore, the study shows the versatility of the IAMCV dataset through several proof-of-concept use cases. First, an unsupervised trajectory clustering algorithm illustrates the dataset’s capability in categorizing vehicle movements without the need for labeled training data. Second, we compare an online camera calibration method with the Robot Operating System-based standard, using images captured in the dataset. Finally, a preliminary test employing the YOLOv8 object-detection model is conducted, augmented by reflections on the transferability of object detection across various lidar resolutions. These use cases underscore the practical utility of the collected dataset, emphasizing its potential to advance research and innovation in the area of intelligent vehicles. [ABSTRACT FROM AUTHOR]

Objectives: The accuracy of relative track between orbiter and ascender can ensure success and safety during the meeting and docking process of Lunar. Methods: Based on dual-target joint measurement of Chang’E-5, we established the same beam interferomery(SBI) measurement and inter-satellite measure⁃ ment model, and designed a statistical orbit determination algorithm combined with orbiter and ascender. Results: We used the reference orbit to analyze delay and delay rate of SBI, and the range and range accuracy of microwave radar/laser radar. The accuracy of SBI is equivalent to that of very long baseline interferometry, and the range accuracy of microwave radar/laser radar is at meter level. Conclusions: By establishing the relative motion relationship between the two targets, the relative orbit accuracy can be sig⁃ nificantly improved in orbit determination. [ABSTRACT FROM AUTHOR]

In this paper, an analysis of the precision of range sensors is presented. Light Detection and Ranging (LIDAR) data from three different sensors (HLS-LFCD-LDS, SICK TIM561, and Kinect V2), stereo data from the Realsense D435, and structured light data from both Kinect V1 for Xbox and the Xtion PRO Live were analyzed regarding the influence of range, incident angle to the surface, and surface material. In addition, a comparison with standard deviation models based on vendor-provided specifications was performed. We found that the vendor specifications are in general not specific enough to facilitate accurate sensor simulations. Therefore, we developed a data-driven model for range precision. Our model can be used to create realistic sensor simulations and to develop robot navigation algorithms weighing sensor range readings based on the precision. [ABSTRACT FROM AUTHOR]

Abstract This study aims to enhance the safety and efficiency of port navigation by reducing ship collision accidents, minimizing environmental risks, and optimizing waterways to increase port throughput. Initially, a three-dimensional map of the port’s waterway, including data on water depth, rocks, and obstacles, is generated through laser radar scanning. Visual perception technology is adopted to process and identify the data for environmental awareness. Single Shot MultiBox Detector (SSD) is utilized to position ships and obstacles, while point cloud data create a comprehensive three-dimensional map. In order to improve the optimal navigation approach of the Rapidly-Exploring Random Tree (RRT), an artificial potential field method is employed. Additionally, the collision prediction model utilizes K-Means clustering to enhance the Faster R-CNN algorithm for predicting the paths of other ships and obstacles. The results indicate that the RRT enhanced by the artificial potential field method reduces the average path length (from 500 to 430 m), average time consumption (from 30 to 22 s), and maximum collision risk (from 15 to 8%). Moreover, the accuracy, recall rate, and F1 score of the K-Means + Faster R-CNN collision prediction model reach 92%, 88%, and 90%, respectively, outperforming other models. Overall, these findings underscore the substantial advantages of the proposed enhanced algorithm in autonomous navigation and collision prediction in port waterways.

This study aims to enhance the safety and efficiency of port navigation by reducing ship collision accidents, minimizing environmental risks, and optimizing waterways to increase port throughput. Initially, a three-dimensional map of the port's waterway, including data on water depth, rocks, and obstacles, is generated through laser radar scanning. Visual perception technology is adopted to process and identify the data for environmental awareness. Single Shot MultiBox Detector (SSD) is utilized to position ships and obstacles, while point cloud data create a comprehensive three-dimensional map. In order to improve the optimal navigation approach of the Rapidly-Exploring Random Tree (RRT), an artificial potential field method is employed. Additionally, the collision prediction model utilizes K-Means clustering to enhance the Faster R-CNN algorithm for predicting the paths of other ships and obstacles. The results indicate that the RRT enhanced by the artificial potential field method reduces the average path length (from 500 to 430 m), average time consumption (from 30 to 22 s), and maximum collision risk (from 15 to 8%). Moreover, the accuracy, recall rate, and F1 score of the K-Means + Faster R-CNN collision prediction model reach 92%, 88%, and 90%, respectively, outperforming other models. Overall, these findings underscore the substantial advantages of the proposed enhanced algorithm in autonomous navigation and collision prediction in port waterways. [ABSTRACT FROM AUTHOR]

The high-precision dynamic positioning of highway vehicles is the foundation and prerequisite for achieving intelligent connected transportation. To address the shortcomings of the GNSS/INS combination and GNSS/INS/OD combination in tunnel vehicle positioning, this paper proposes a tunnel vehicle positioning method for the GNSS/INS/OD combination based on lateral distance measurements and lane constraints. Firstly, a lateral distance measurement of vehicles inside the tunnel is conducted based on laser radar point cloud data. Secondly, map matching positioning is performed based on lateral distance measurements, odometer, and lane markings. Experimental results demonstrate that, for a 4.6 km tunnel, the average absolute error in the lateral positioning is 0.294 m, and the longitudinal positioning error is no more than 0.6 m, which can effectively meet practical operational requirements. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The application of autonomous driving technology in the field of transportation has become a hot research direction, and autonomous vehicles need to accurately detect and track moving targets around. As a kind of sensor widely used in the field of automatic driving, LiDAR has the characteristics of high precision and long distance detection. Therefore, this paper adopts a target detection algorithm based on three-dimensional LiDAR, which can identify moving targets accurately. Then the motion path of the detected target is captured and tracked by optical method, and the motion state of the target is monitored in real time. The experimental results show that the moving target detection algorithm and optical motion acquisition method based on 3D LiDAR can detect and track the moving target effectively, and capture its moving trajectory accurately. The application of this method to autonomous vehicles can improve vehicle perception and driving safety, and also provide a useful reference for other fields of moving object detection and tracking research. [ABSTRACT FROM AUTHOR]

Path planning is a critical aspect of autonomous driving, necessitating the calculation of the most efficient route considering real‐time traffic conditions, road status, and obstacles. The primary goal of path planning algorithms is to identify the optimal route from a starting point to a destination within a given environment. In autonomous driving, these algorithms are broadly categorizedinto global planning and local planning. Noteworthy global path planning algorithms include Dijkstra's algorithm, A*, and others. Local path planning algorithms encompass local perception and obstacle avoidance, utilizing real‐time information from devices like laser radar and cameras. This approach, suitable for complex and dynamic environments, involves collision avoidance and path adjustments. Analysis reveals that both commonly used global planning algorithms and local path planning algorithms possess distinct advantages and drawbacks. Whether in simulation debugging or practical operations, the collaborative functioning of both types of algorithms is imperative for ensuring the safe and efficient operation of autonomous vehicles in complex and dynamic environments. [ABSTRACT FROM AUTHOR]

In urban settings, roadside infrastructure LiDAR is a ground-based remote sensing system that collects 3D sparse point clouds for the traffic object detection of vehicles, pedestrians, and cyclists. Current anchor-free algorithms for 3D point cloud object detection based on roadside infrastructure face challenges related to inadequate feature extraction, disregard for spatial information in large 3D scenes, and inaccurate object detection. In this study, we propose AFRNet, a two-stage anchor-free detection network, to address the aforementioned challenges. We propose a 3D feature extraction backbone based on the large sparse kernel convolution (LSKC) feature set abstraction module, and incorporate the CBAM attention mechanism to enhance the large scene feature extraction capability and the representation of the point cloud features, enabling the network to prioritize the object of interest. After completing the first stage of center-based prediction, we propose a refinement method based on attentional feature fusion, where fused features incorporating raw point cloud features, voxel features, BEV features, and key point features are used for the second stage of refinement to complete the detection of 3D objects. To evaluate the performance of our detection algorithms, we conducted experiments using roadside LiDAR data from the urban traffic dataset DAIR-V2X, based on the Beijing High-Level Automated Driving Demonstration Area. The experimental results show that AFRNet has an average of 5.27 percent higher detection accuracy than CenterPoint for traffic objects. Comparative tests further confirm that our method achieves high accuracy in roadside LiDAR object detection. [ABSTRACT FROM AUTHOR]

A solid-state three-dimensional (3-D) range imager using the pulsed time-of-flight principle and a single photon avalanche diode (SPAD) based receiver is demonstrated. Contrary to conventional flood illumination, the suggested system uses sequentially scanned illumination stripes to increase the irradiance under the measured field of view and to reduce the receiver complexity. At equal average illumination power levels this approach increases the signal-to-noise ratio in the receiver enabling thus longer range and/or better performance in high background illumination conditions, albeit at the cost of lower transversal resolution. The transmitter uses eight sequentially driven laser diodes, each of which produces an optical pulse power, length and wavelength of ∼50 W, 2 ns and 905 nm, respectively at a pulse rate of ∼30 kHz so that the total effective pulsing rate is ∼250 kHz and the average illumination power is ∼30 mW. 3-D imaging is demonstrated outdoors in over 50 klux ambient lighting up to ∼10 m distance with 15 Hz frame rate and mm–cm level longitudinal precision in a field of view of about 40 × 10 degrees. The transversal resolution of the system is 16 × 128 pixels. The measurement results show 3-D measurement precision scaling according to the laser pulse width and averaging.

[Significance]As the research focus of future agricultural machinery, agricultural wheeled robots are developing in the direction of intelligence and multi-functionality. Advanced environmental perception technologies serve as a crucial foundation and key components to promote intelligent operations of agricultural wheeled robots. However, considering the non-structured and complex environments in agricultural on-field operational processes, the environmental information obtained through conventional 2D perception technologies is limited. Therefore, 3D environmental perception technologies are highlighted as they can provide more dimensional information such as depth, among others, thereby directly enhancing the precision and efficiency of unmanned agricultural machinery operation. This paper aims to provide a detailed analysis and summary of 3D environmental perception technologies, investigate the issues in the development of agricultural environmental perception technologies, and clarify the future key development directions of 3D environmental perception technologies regarding agricultural machinery, especially the agricultural wheeled robot.[Progress]Firstly, an overview of the general status of wheeled robots was introduced, considering their dominant influence in environmental perception technologies. It was concluded that multi-wheel robots, especially four-wheel robots, were more suitable for the agricultural environment due to their favorable adaptability and robustness in various agricultural scenarios. In recent years, multi-wheel agricultural robots have gained widespread adoption and application globally. The further improvement of the universality, operation efficiency, and intelligence of agricultural wheeled robots is determined by the employed perception systems and control systems. Therefore, agricultural wheeled robots equipped with novel 3D environmental perception technologies can obtain high-dimensional environmental information, which is significant for improving the accuracy of decision-making and control. Moreover, it enables them to explore effective ways to address the challenges in intelligent environmental perception technology. Secondly, the recent development status of 3D environmental perception technologies in the agriculture field was briefly reviewed. Meanwhile, sensing equipment and the corresponding key technologies were also introduced. For the wheeled robots reported in the agriculture area, it was noted that the applied technologies of environmental perception, in terms of the primary employed sensor solutions, were divided into three categories: LiDAR, vision sensors, and multi-sensor fusion-based solutions. Multi-line LiDAR had better performance on many tasks when employing point cloud processing algorithms. Compared with LiDAR, depth cameras such as binocular cameras, TOF cameras, and structured light cameras have been comprehensively investigated for their application in agricultural robots. Depth camera-based perception systems have shown superiority in cost and providing abundant point cloud information. This study has investigated and summarized the latest research on 3D environmental perception technologies employed by wheeled robots in agricultural machinery. In the reported application scenarios of agricultural environmental perception, the state-of-the-art 3D environmental perception approaches have mainly focused on obstacle recognition, path recognition, and plant phenotyping. 3D environmental perception technologies have the potential to enhance the ability of agricultural robot systems to understand and adapt to the complex, unstructured agricultural environment. Furthermore, they can effectively address several challenges that traditional environmental perception technologies have struggled to overcome, such as partial sensor information loss, adverse weather conditions, and poor lighting conditions. Current research results have indicated that multi-sensor fusion-based 3D environmental perception systems outperform single-sensor-based systems. This superiority arises from the amalgamation of advantages from various sensors, which concurrently serve to mitigate individual shortcomings.[Conclusions and Prospects]The potential of 3D environmental perception technology for agricultural wheeled robots was discussed in light of the evolving demands of smart agriculture. Suggestions were made to improve sensor applicability, develop deep learning-based agricultural environmental perception technology, and explore intelligent high-speed online multi-sensor fusion strategies. Currently, the employed sensors in agricultural wheeled robots may not fully meet practical requirements, and the system's cost remains a barrier to widespread deployment of 3D environmental perception technologies in agriculture. Therefore, there is an urgent need to enhance the agricultural applicability of 3D sensors and reduce production costs. Deep learning methods were highlighted as a powerful tool for processing information obtained from 3D environmental perception sensors, improving response speed and accuracy. However, the limited datasets in the agriculture field remain a key issue that needs to be addressed. Additionally, multi-sensor fusion has been recognized for its potential to enhance perception performance in complex and changeable environments. As a result, it is clear that 3D environmental perception technology based on multi-sensor fusion is the future development direction of smart agriculture. To overcome challenges such as slow data processing speed, delayed processed data, and limited memory space for storing data, it is essential to investigate effective fusion schemes to achieve online multi-source information fusion with greater intelligence and speed.

In this paper, we propose a simple method to generate the uniform illumination using a pyramid prism for Plane Array Laser Imaging Detection and Ranging (PA-LiDAR). The principle of the pyramid prism shaping the Gaussian beam to form a uniform beam was analyzed theoretically. By changing the parameters of the pyramid prism and laser beam, the profile distribution of the output beam can be easily adjusted. Based on the operation mode and illumination requirements of PA-LiDAR, we have developed a set of LiDAR prototypes using a pyramid prism and carried out experimental research on these prototypes. The simulation and experimental results demonstrated that this method can achieve a uniform illumination beam with excellent propagation properties for meeting the technical requirements of PA-LiDAR. This method of uniform illumination has the advantages of being simple, flexible, easily adjustable and convenient to operate.

The demand for drone-based synthetic aperture radar (SAR) systems is growing, especially for applications in cases where satellites or airborne systems are not sufficiently flexible. However, the combination of a long operating range and high spatial resolution causes the atmosphere to pose challenges for these systems. In this article, we present our $K$ -band drone system that has a long range sensibility through modification to a commercially available radar. In addition, a moving baseline configuration has been integrated to ensure accurate attitude data, especially the heading. The desired spatial resolution is achieved by our proposed autofocus algorithm based on image sharpness. It is designed to also work in challenging cases of nonlinear flight paths and can be integrated into a processing framework based on back-projection. Several experiments were conducted to demonstrate the drone system's capabilities. These included a comparison with an established airborne radar: MIRANDA35. The obtained results demonstrate the ability of the drone SAR system to map wide areas at high spatial resolution. [ABSTRACT FROM AUTHOR]

Individual tree crown delineation (ITCD) employing unmanned aerial vehicle light detection and ranging data can directly obtain high-precision tree-level structural information within a block, with this information being the foundation for monitoring and management of the forest, thus reducing time-consuming labor. Despite the fact that numerous ITCD algorithms have been proposed, there has not yet been a robust and comprehensive comparison of these algorithms in plantations. In this article, we evaluated the performance of seven classic ITCD methods under various stand densities and crown classes and analyzed the parameter sensitivity as well as the correlation of segmentation accuracy with optimal parameters and stand metrics. The results demonstrate that the segmentation and crown description accuracy, stability, and adaptability of the algorithm should be comprehensively considered when choosing an algorithm. The forest characteristics impact the accuracy of the algorithms, and the complexity of the forest canopy structure and omission error of suppressed trees are the key factors impacting ITCD accuracy. Furthermore, this study shows that it is feasible to control the parameters of the algorithm through stand measurement. These results will be helpful in guiding the selection of ITCD methods and will provide support for improving the ITCD algorithm in the future. [ABSTRACT FROM AUTHOR]

[Significance] As the research focus of future agricultural machinery, agricultural wheeled robots are developing in the direction of intelligence and multi-functionality. Advanced environmental perception technologies serve as a crucial foundation and key components to promote intelligent operations of agricultural wheeled robots. However, considering the non-structured and complex environments in agricultural on-field operational processes, the environmental information obtained through conventional 2D perception technologies is limited. Therefore, 3D environmental perception technologies are highlighted as they can provide more dimensional information such as depth, among others, thereby directly enhancing the precision and efficiency of unmanned agricultural machinery operation. This paper aims to provide a detailed analysis and summary of 3D environmental perception technologies, investigate the issues in the development of agricultural environmental perception technologies, and clarify the future key development directions of 3D environmental perception technologies regarding agricultural machinery, especially the agricultural wheeled robot. [Progress] Firstly, an overview of the general status of wheeled robots was introduced, considering their dominant influence in environmental perception technologies. It was concluded that multi-wheel robots, especially four-wheel robots, were more suitable for the agricultural environment due to their favorable adaptability and robustness in various agricultural scenarios. In recent years, multi wheel agricultural robots have gained widespread adoption and application globally. The further improvement of the universality, operation efficiency, and intelligence of agricultural wheeled robots is determined by the employed perception systems and control systems. Therefore, agricultural wheeled robots equipped with novel 3D environmental perception technologies can obtain high-dimensional environmental information, which is significant for improving the accuracy of decision-making and control. Moreover, it enables them to explore effective ways to address the challenges in intelligent environmental perception technology. Secondly, the recent development status of 3D environmental perception technologies in the agriculture field was briefly reviewed. Meanwhile, sensing equipment and the corresponding key technologies were also introduced. For the wheeled robots reported in the agriculture area, it was noted that the applied technologies of environmental perception, in terms of the primary employed sensor solutions, were divided into three categories: LiDAR, vision sensors, and multi-sensor fusion-based solutions. Multi-line LiDAR had better performance on many tasks when employing point cloud processing algorithms. Compared with LiDAR, depth cameras such as binocular cameras, TOF cameras, and structured light cameras have been comprehensively investigated for their application in agricultural robots. Depth camera-based perception systems have shown superiority in cost and providing abundant point cloud information. This study has investigated and summarized the latest research on 3D environmental perception technologies employed by wheeled robots in agricultural machinery. In the reported application scenarios of agricultural environmental perception, the state-of-the-art 3D environmental perception approaches have mainly focused on obstacle recognition, path recognition, and plant phenotyping. 3D environmental perception technologies have the potential to enhance the ability of agricultural robot systems to understand and adapt to the complex, unstructured agricultural environment. Furthermore, they can effectively address several challenges that traditional environmental perception technologies have struggled to overcome, such as partial sensor information loss, adverse weather conditions, and poor lighting conditions. Current research results have indicated that multi-sensor fusion-based 3D environmental perception systems outperform single-sensor-based systems. This superiority arises from the amalgamation of advantages from various sensors, which concurrently serve to mitigate individual shortcomings. [Conclusions and Prospects] The potential of 3D environmental perception technology for agricultural wheeled robots was discussed in light of the evolving demands of smart agriculture. Suggestions were made to improve sensor applicability, develop deep learning based agricultural environmental perception technology, and explore intelligent high-speed online multi-sensor fusion strategies. Currently, the employed sensors in agricultural wheeled robots may not fully meet practical requirements, and the system's cost remains a barrier to widespread deployment of 3D environmental perception technologies in agriculture. Therefore, there is an urgent need to enhance the agricultural applicability of 3D sensors and reduce production costs. Deep learning methods were highlighted as a powerful tool for processing information obtained from 3D environmental perception sensors, improving response speed and accuracy. However, the limited datasets in the agriculture field remain a key issue that needs to be addressed. Additionally, multi-sensor fusion has been recognized for its potential to enhance perception performance in complex and changeable environments. As a result, it is clear that 3D environmental perception technology based on multi-sensor fusion is the future development direction of smart agriculture. To overcome challenges such as slow data processing speed, delayed processed data, and limited memory space for storing data, it is essential to investigate effective fusion schemes to achieve online multi-source information fusion with greater intelligence and speed. [ABSTRACT FROM AUTHOR]

Using a series of detector measurements taken at different locations to localize a source of radiation is a well-studied problem. The source of radiation is sometimes constrained to a single point-like source, in which case the location of the point source can be found using techniques such as maximum likelihood. Recent advancements have shown the ability to locate point sources in 2-D and even 3-D but few have studied the effect of intervening material on the problem. In this work, we examine gamma-ray data taken from a freely moving system and develop voxelized 3-D models of the scene using data from its onboard light detection and ranging (LiDAR) unit. Ray casting is used to compute the distance each gamma ray travels through the scene material, which is then used to calculate attenuation assuming a single attenuation coefficient for solids within the geometry. Parameter estimation using maximum likelihood is performed to simultaneously find the attenuation coefficient, source activity, and source position that best match the data. Using a simulation, we validate the ability of this method to reconstruct the true location and activity of a source, along with the true attenuation coefficient of the structure it is inside, and then we apply the method to measured data with sources and find good agreement.

The Polaris-LAMP multi-modal 3-D gamma-ray imager is a radiation mapping and imaging platform which uses a commercial off-the-shelf (COTS) detector integrated with a contextual sensor localization and mapping platform. The integration of these systems enables a free-moving radiation imaging capability with proximity mapping, coded-aperture, and Compton imaging modalities, which can create 3-D reconstruction of photon sources from tens of keV to several MeV. Gamma-ray events are recorded using a segmented cadmium zinc telluride (CZT) detector (Polaris-H Quad by H3D Inc., Ann Arbor, MI, USA), while scene data are derived from a contextual sensor and computation package developed by Lawrence Berkeley National Laboratory which includes GPS, laser ranging, and inertial measurement sensors. An onboard computer uses these inputs to create rapidly updating pose (10 Hz) and 3-D scene estimates using a simultaneous localization and mapping (SLAM) algorithm. The precise gamma-ray event location and timing resolution of the Polaris CZT sensor enables Compton imaging above several hundred keV, while photon sources at lower images are localized using coded-aperture imaging techniques. The multi-modal imaging concept enables imaging of diverse radiation sources spanning from the 59-keV emission of 241Am to the 1.1 and 1.3 MeV lines of 60Co. This work focuses on the description of the operational principles of the detector system and demonstrating the 3-D imaging performance in a variety of source detection and mapping scenarios. As a proof of concept, we demonstrate mapping complex environments, including both point source and distributed-source environments using proximity, coded-aperture, and Compton imaging modalities. Furthermore, we show the successful use of the system to perform measurements in high-background environments through analysis of arrays of uranium hexafluoride cylinders at the Paducah UF6 project site.

We examine and test in the laboratory our laser pulse remote sensing system, based on the number of cycles of a local oscillator. We calculate the range of an object, using a peak transmission power of 2 mW from a Mesa HP-pumped He–Ne laser with a pulse frequency of 3 kHz and a pulse width of 150 ns; also, we establish a correlation between the counted cycle and the laser wavelength. The results of outer range measurements with a range resolution of 45 m at a distance of 60 meters are presented. Here, photons are used for tracking and object identification, which being encoded using encoding technology, will be challenging to duplicate, and our optical radar will not be misplaced. [ABSTRACT FROM AUTHOR]

Autonomous driving (AD), including single-vehicle intelligent AD and vehicle–infrastructure cooperative AD, has become a current research hot spot in academia and industry, and multi-sensor fusion is a fundamental task for AD system perception. However, the multi-sensor fusion process faces the problem of differences in the type and dimensionality of sensory data acquired using different sensors (cameras, lidar, millimeter-wave radar, and so on) as well as differences in the performance of environmental perception caused by using different fusion strategies. In this article, we study multiple papers on multi-sensor fusion in the field of AD and address the problem that the category division in current multi-sensor fusion perception is not detailed and clear enough and is more subjective, which makes the classification strategies differ significantly among similar algorithms. We innovatively propose a multi-sensor fusion taxonomy, which divides the fusion perception classification strategies into two categories—symmetric fusion and asymmetric fusion—and seven subcategories of strategy combinations, such as data, features, and results. In addition, the reliability of current AD perception is limited by its insufficient environment perception capability and the robustness of data-driven methods in dealing with extreme situations (e.g., blind areas). This article also summarizes the innovative applications of multi-sensor fusion classification strategies in AD cooperative perception. [ABSTRACT FROM AUTHOR]

Desk workers spend long periods sitting with poor posture. This leads to back pain and musculoskeletal disorders. Current solutions to track posture use sensors or cameras that may make sitting uncomfortable and violate privacy. This letter presents a system that detects the leg position when sitting at a desk via 2-D light detection and ranging (LiDAR), and a support vector machine running on a Raspberry Pi 4. The system recognizes six common positions: legs slightly open with knees bent, legs crossed, one leg on the other leg’s knee, legs extended forward, backward, and legs parallel with an angle of ~90° between upper and lower legs. The results of an experiment involving 30 workers showed an accuracy of 99.2%. [ABSTRACT FROM AUTHOR]

The advent of lidar has revolutionized the way we observe and measure vegetation structure from the ground and from above and represents a major advance toward the quantification of 3D ecological observations. Developments in lidar hardware systems and data processing algorithms have greatly improved the accessibility and ease of use of lidar observations in ecological studies. A wide range of studies has been devoted to accurately measuring and modeling vegetation structural and functional attributes from lidar data across a range of spatial scales (from individual organs to global scales) and ecosystem types (e.g., forest, agricultural, grassland, and urban ecosystems).

The high-precision dynamic positioning of highway vehicles is the foundation and prerequisite for achieving intelligent connected transportation. To address the shortcomings of the GNSS/INS combination and GNSS/INS/OD combination in tunnel vehicle positioning, this paper proposes a tunnel vehicle positioning method for the GNSS/INS/OD combination based on lateral distance measurements and lane constraints. Firstly, a lateral distance measurement of vehicles inside the tunnel is conducted based on laser radar point cloud data. Secondly, map matching positioning is performed based on lateral distance measurements, odometer, and lane markings. Experimental results demonstrate that, for a 4.6 km tunnel, the average absolute error in the lateral positioning is 0.294 m, and the longitudinal positioning error is no more than 0.6 m, which can effectively meet practical operational requirements.

With the rapid development of Lidar technology, the use of Lidar for underwater terrain detection has become feasible. There is still a challenge in the process of signal resolution: the underwater laser echo signal is different to propagating in the air, and it is easy to produce weak waves and superimposed waves. However, existing waveform decomposition methods are not effective in processing these waveform signals, and the underwater waveform signal cannot be correctly decomposed, resulting in subsequent data-processing errors. To address these issues, this study used a drone equipped with a 532 nm laser to detect a pond as the study background. This paper proposes an improved inflection point selection decomposition method to estimate the parameter. By comparing it with other decomposition methods, we found that the RMSE is 2.544 and R2 is 0.995975, which is more stable and accurate. After estimating the parameters, this study used oscillating particle swarm optimization (OPSO) and the Levenberg–Marquardt algorithm (LM) to optimize the estimated parameters; the final results show that the method in this paper is closer to the original waveform. In order to verify the processing effect of the method on complex waveform, this paper decomposes and optimizes the simulated complex waveforms; the final RMSE is 0.0016, R2 is 1, and the Gaussian component after decomposition can fully represent the original waveform. This method is better than other decomposition methods in complex waveform decomposition, especially regarding weak waves and superimposed waves. [ABSTRACT FROM AUTHOR]

Global Ecosystem Dynamics Investigation (GEDI) is a lidar system on-board the International Space Station designed to study forest ecosystems. However, GEDI footprint low accuracy geolocation is a major impediment to the optimal benefit of the data. We thus proposed a geolocation correction method, GeoGEDI, only based on high-resolution digital elevation models (DEMs) and GEDI derived ground elevations. For each footprint, an error map between GEDI ground estimates and reference DEM was computed, and a flow accumulation algorithm was used to retrieve the optimal footprint position. GeoGEDI was tested on 150 000 footprints in Landes and Vosges, two French forests with various stands and topographic conditions. It was applied to GEDI versions 1 (v1) and 2 (v2), by either a single or four full-power laser beam tracks. GeoGEDI output accuracy was evaluated by analyzing shift distributions and comparing GEDI ground elevations and surface heights to reference data. GeoGEDI corrections were greater for v1 than for v2 and agreed with errors published by NASA. Within forests, GeoGEDI improved the root mean square error (RMSE) of ground elevation in Landes by 26.8% (0.34 m) and by 13.3% (0.14 m) for v1 and v2, respectively. For Vosges, ground elevation RMSE improved by 59.6% (3.82 m) and 36.2% (1.41 m), for v1 and v2, respectively. Regarding surface heights, except for v2 in Landes, where insufficient variations in topography combined to GEDI ground detection issues might have penalized the adjustment, GeoGEDI improved GEDI estimates. Using GeoGEDI showed efficient to improve positioning bias and precision. [ABSTRACT FROM AUTHOR]

Forest characterization and monitoring are highly important for tracking climate change, using ecology resources, and biodiversity applications. Synthetic aperture radar tomography (TomoSAR) provides the opportunity to reconstruct 3-D structures of the penetrable media relying on multibaseline image acquisition. In forest applications, TomoSAR serves as a powerful technical tool for reconstructing forest height and underlying topography. Presently, a number of reconstruction methods are based on fully polarimetric (FP) TomoSAR (Pol-TomoSAR) datasets which require costly data acquisition. The aim of this letter is to go beyond the limitation of the requirement for full polarization by extending tomographic SAR neural network (TSNN), a neural network for TomoSAR, to the case of single-polarimetric (SP) and dual-polarimetric (DP) TomoSAR data for retrieving forest height and underlying topography. Experimental results indicate that TSNN trained by SP or DP TomoSAR data is a powerful candidate to estimate forest height and underlying topography with high accuracy. [ABSTRACT FROM AUTHOR]

In recent years, the joint classification approach of hyperspectral image (HSI) and light detection and ranging (LiDAR) data based on deep learning (DL) has received increasing attention. However, existing methods either lack interaction between heterogeneous features during feature extraction or treat them equally during interaction, inevitably resulting in redundant information stacking and reaching the performance bottleneck. To this end, we propose a novel discrepant bi-directional interaction fusion network (DBIFNet) for the collaborative classification of HSI and LiDAR data. First, a discrepant bi-directional interaction module (DBDIM) is designed to establish correlations between heterogeneous features to enhance the respective feature learning. Furthermore, a cross-modal attention fusion module (CAFM) is developed to dynamically fuse multimodal features, which can further improve classification performance. Extensive experiments on the Houston and Trento datasets demonstrate that the proposed DBIFNet can achieve competitive classification performance. [ABSTRACT FROM AUTHOR]

The utilization of multimodal data from multisensors (e.g., hyperspectral and light detection ranging (LiDAR) data) to classify ground objects has been an important topic in remote sensing interpretation. However, complex background leads to the difficulty in extracting context relationships; at the same time, redundancy and noise among multimodal data bring great challenges to accurate classification. In this letter, we propose a novel spatial context and de-redundant fusion network (SCDNet) to fuse hyperspectral and LiDAR data for land cover classification. Specifically, a multiscale attention fusion module (MSAF) is developed in the feature extraction stage, which adaptively fuses global and local information of different scales to obtain a more accurate spatial context. In the feature fusion stage, a fusion module based on gated mechanism is proposed, which can remove the redundant information of multimode data and obtain discriminative fusion features. We design a series of comparisons and ablation experiments on the Houston2013 dataset and Trento dataset, and the results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The Global Ecosystem Dynamics Investigation (GEDI) aims to provide improved characterization of forest structure, and plant area index (PAI) is one of many variables provided in the official GEDI Level 2B (L2B) product suite. However, since release, few quantitative validation studies have been conducted. To reach Stage 1 of the validation hierarchy proposed by the Land Product Validation (LPV) subgroup of the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV), we provide an initial assessment of PAI estimates from GEDI’s L2B product. This is achieved using 18 in situ reference measurements available through the Copernicus Ground Based Observations for Validation (GBOV) service. We show that GEDI L2B PAI retrievals provide a nearly unbiased estimate of effective (PAIe; RMSD = 0.95, bias = 0.02, slope = 1.07), but systematically underestimate PAI (RMSD = 1.42, bias = −0.91, slope = 0.77). This is attributed to an assumed random distribution of plant material in the algorithm. To reach Stage 2 of the CEOS WGCV LPV hierarchy, continued work is needed to validate the product against additional in situ reference measurements covering further locations and time periods. [ABSTRACT FROM AUTHOR]

Passive multispectral imaging and active bathymetric lidar make a great achievement for bathymetry in optically shallow waters. However, due to the attenuation of the water column precludes deep penetration of the light, accurately obtaining the underwater topography in turbid waters through remote sensing techniques, both passive and active, is still a challenging task. Airborne lidars can obtain water surface topography with high accuracy and resolution, which can further be used to derive the water depth based on wave theory. In this study, an “indirect” method to determine water depth is proposed using airborne lidar measured water surface points. As the wavelength and wave direction can be accurately tracked from the water surface topography, a $20\times20$ m underwater topography near Ganquan Island, South China Sea, is generated with a root mean square error (RMSE) of 0.91 m and a mean absolute percent error (MAPE) of 7.1%. The basic theory of deriving water depths is totally different from airborne lidar bathymetry (ALB), i.e., this method is independent of water clarity and can be used in turbid waters or even works with near infrared airborne lidar that can only obtain water surface points. [ABSTRACT FROM AUTHOR]

The fusion and classification of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data have been extensively studied using deep learning. However, traditional real-valued deep-learning methods have limitations in distinguishing internal and external relations and capturing fine spatial characteristics. To break through these limitations, this letter proposes a quaternion convolutional neural network (QCNN) with extended morphological attribute profile (EMAP) quaternion representation (called EQR) for multisource remote-sensing (RS) data classification by utilizing quaternion properties. Specifically, we first propose the EQR for each single-source data, which encodes the multiattribute features in a compact, yet comprehensive manner, highlighting the internal relations. Second, we embed EQR into QCNN to preserve the internal relations and enable the interaction of multiattribute features. Then, we develop the 3-D quaternion convolution (3DQConv) to better exploit the 3-D characteristic of HSIs. Finally, we design different attention mechanisms and a two-level fusion strategy for multisource data to learn enhanced features. Experiments on two multisource RS datasets show that the proposed method achieved better performance than other state-of-the-art classification methods. [ABSTRACT FROM AUTHOR]

The global ecosystem dynamics investigation (GEDI) contains a full waveform, multibeam laser altimeter, which provides vertical vegetation structure information. However, few studies have assessed the accuracy of using GEDI light detection and ranging (LiDAR) data in retrieving ground topography from dense forest environments. There has been no in-depth study assessing the accuracy of GEDI data with different kinds of elevation index in footprint samples from densely forested areas (canopy cover > 70%). To address this limitation, this study aims to assess the accuracy of ground topography estimates from dense forested terrain through the combination of airborne LiDAR data. The results show that under dense forest conditions, the mean elevation in the GEDI footprint performed more accurate than the max elevation, the min elevation, and the median elevation in the GEDI footprint. GEDI provides reasonable estimates of mean terrain height, with ${R} ^{2}$ of 0.99, root mean squared error (RMSE) of 6.08 m, and mean absolute error (MAE) of 3.92 m. In addition, dense forest ecosystems reduce the accuracy of the terrain height estimates. Overall, GEDI with its full-waveform technology represents promising spaceborne LiDAR for terrain height retrieval in dense forest environments. [ABSTRACT FROM AUTHOR]

Existing point cloud modeling datasets primarily express the modeling precision by pose or trajectory precision rather than the point cloud modeling effect itself. Under this demand, we first independently construct a set of LiDAR system with an optical stage, and then we build an HPMB dataset based on the constructed LiDAR system, a High-Precision, Multi-Beam, real-world dataset. Second, we propose a modeling evaluation method based on HPMB for object-level modeling to overcome this limitation. In addition, the existing point cloud modeling methods tend to generate continuous skeletons of the global environment, hence lacking attention to the shape of complex objects. To tackle this challenge, we propose a novel learning-based joint framework, DSMNet, for high-precision 3-D surface modeling from sparse point cloud frames. DSMNet comprises density-aware point cloud registration (PCR) and geometry-aware point cloud sampling (PCS) to effectively learn the implicit structure feature of sparse point clouds. Extensive experiments demonstrate that DSMNet outperforms the state-of-the-art methods in PCS and PCR on the Multi-View Partial Point Cloud (MVP) database. Furthermore, the experiments on the open-source KITTI and our proposed HPMB datasets show that DSMNet can be generalized as a postprocessing of simultaneous localization and mapping (SLAM), thereby improving modeling precision in environments with sparse point clouds. [ABSTRACT FROM AUTHOR]

The new generation photon-counting lidar on Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) can obtain the subsurface optical properties of sea waters. Recent studies highlight the applications of deriving the lidar attenuation coefficient $K_{\mathrm {lidar}}$ and then substituting $K_{\mathrm {lidar}}$ into the bio-optical model to obtain more information of sea waters. As commonly used bio-optical models are built for the diffuse attenuation coefficient $K_{d}$ that are traditionally derived by passive ocean color sensors, whether $K_{\mathrm {lidar}}$ derived from ICESat-2 can be directly used as $K_{d}$ is a fundamental question. Given that $K_{d}$ is an apparent optical property (AOP) in the water column rather than an inherent optical property (IOP), $K_{d}$ is closely related to the zenith angle of the incident light. The zenith angle of the incident light of the sunlight is normally tens of degrees for ocean color sensors, while the maximum laser off-nadir angle is ~1.5° for the ICESat-2 lidar. To demonstrate this issue, we select hundreds of ground tracks of ICESat-2 in both open ocean and coastal sea waters and compare the derived $K_{\mathrm {lidar}}$ with their corresponding Moderate Resolution Imaging Spectroradiometer (MODIS)-derived $K_{d}$. The results indicate that the corrected results of $1.2\times K_{\mathrm {lidar}}$ , instead of the direct results of $K_{\mathrm {lidar}}$ , are more consistent with MODIS $K_{d}$. This study is of great significance to the better fusion of active lidar data and passive optical data in ocean observations. [ABSTRACT FROM AUTHOR]

The shape reconstruction of buildings based on LiDAR point clouds is extremely significant in remote sensing. In recent years, reconstruction methods based on the implicit network have been widely used in object-level shape reconstruction. However, the incompleteness and sparsity of airborne LiDAR scanning point clouds will lead to poor reconstruction results. To solve this problem, GEOP-Net: an implicit modeling framework embedded with high-dimensional geometric features, is proposed in this letter. First, the geometric encoding (GE) module added to extract high-dimensional features enhances the feature extraction ability of the network to the detailed structures. The point clouds of buildings in Zurich are collected and used to evaluate the performance of the proposed method. The experimental results show that the proposed method has better accuracy than the existing methods, so it provides a new research idea for building reconstruction. [ABSTRACT FROM AUTHOR]

Oceanic lidar has emerged as a strong technology for oceanic 3-D remote sensing. However, most existing oceanic lidars are bulky and high-power consumption and thus difficult to enable underwater operation. Here, we present a compact single-photon lidar system for long-range underwater measurement. A single-photon detector was adopted to achieve a high signal-to-noise ratio. Benefiting from the single-photon sensitivity in detection, long-range active detection was realized with a low pulse energy laser at $1~\mu \text{J}$ and a small-aperture coupler at 12 mm. Moreover, a narrow linewidth picosecond fiber laser with high repetition rate was employed to guarantee a high spatial resolution and high update rate. A fiber-connected configuration was specially designed for the miniaturized and robust structure in an optical receiver. In an experimental demonstration, the profile of backscattered signal from clean water was obtained over 70 m with high spatial–temporal resolution to demonstrate the capability of this lidar system. The maximum detection distance of the single-photon lidar reaches $\sim $ 3.6/ $K_{\mathrm {d}}$ ($K_{\mathrm {d}}$ is the diffuse attenuation coefficient) for waterbody and up to 5.5/ $K_{\mathrm {d}}$ for a hard target. Furthermore, it exhibits a high update-rate capability and realizes the localization and quantification of underwater bubbles up to 26 m away at a high update rate of 100 Hz. These results indicate its potential in a variety of applications, including remote sensing of marine biogeochemical parameters, the quantification of seabed gas emissions, and long-range underwater imaging. [ABSTRACT FROM AUTHOR]

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) equips with a novel photon-counting LiDAR system, which can generate underwater reflections in nearshore environments. However, due to the water reflection, scattering, and absorption, the distribution of bathymetric photons in the nearshore data varies with depth. The existing bathymetric photon extraction algorithms need more adaptability to seafloor topography. The changing density of bathymetric photons and the fluctuation of underwater topography make the noise removal of nearshore data full of challenges. This study proposed a bathymetric photon extraction algorithm using pre-pruning quadtree isolation (PQI). First, the pre-pruning step judges whether to stop the growth of quadtree in advance during quadtree isolation (QI) to avoid excessive division of noise photons. Second, the maximum inter-class variance algorithm (also called the Otsu method) obtains the best threshold of isolation depth (ID) and extracts bathymetric photons. The algorithm was tested on the Florida coast. The results show that the PQI algorithm can wholly and accurately extract bathymetric photons with different acquisition times from the data. The F1-score of the extracted results is 93.96%. This study provides an intelligent solution to processing bathymetric data in nearshore environments worldwide. [ABSTRACT FROM AUTHOR]

Height estimation from a single synthetic aperture radar (SAR) imagery has shown a great potential in real-time scene understanding and environment detection. It is a mathematical ill-posed problem for that a single 2-D image may be projected from multiple 3-D scenes. Then, the problem is that the accuracy of height estimation from a single SAR image is not high enough without prior knowledge, especially in mountain areas. Thus, we propose a geometry-aware consistent constraint (GACC) to improve the performance of single SAR height estimation. The simulated SAR images generated from height maps are used to establish high-precision transformation between map and radar coordinates in mountain areas. The main idea of GACC is that the simulated SAR image generated from estimated height map should be consistent with the ground-truth simulated SAR image. A sparse height (SH) map is included as the supplementary inputs to further improve the accuracy of estimated height maps. Comparison experiments are performed in Geermu, Huangshan, and Guiyang datasets, and the results show that the root-mean-square error (RMSE) of the estimated height map by U-shaped convolution neural network (Unet) with GACC and 0.0434% SH gets reduced by about 98%, 78%, and 94% compared with that by Unet in the three datasets. [ABSTRACT FROM AUTHOR]

Under the background of the rapid development of remote sensing (RS) technology, multimodal RS image classification has attracted great attention. Considerable research has been devoted to designing more adequate multimodal feature-level fusion networks. However, few have noted that in the process of feature fusion, if the multimodal heterogeneous features are quite different, direct fusion may introduce noise. This greatly affects the classification performance of the network. This letter proposes a shuffle feature enhancement-based fusion network (SFE-FN) for hyperspectral and light detection and ranging (LiDAR) classification, which effectively alleviates the aforementioned problems. Specifically, first, an SFE module is proposed to achieve self-enhancement and mutual enhancement of each modal feature to preliminary reduce the feature difference. Then, a cross-layer and cross-interaction module (CLCI) is designed to further enhance the consistency of features by updating parameters across layers. Finally, the proposed shuffle feature concatenation (SFC) module and the shuffle feature fusion (SFF) module are utilized to adequately merge fewer differentiated features. Experiments on Houston2013 and Trento datasets show that the proposed method is effective. [ABSTRACT FROM AUTHOR]

This letter describes a model-based algorithm for estimating tree height and other bio-physical land parameters from time series of synthetic aperture radar (SAR) interferometric coherence and backscatter supported by sparse lidar data. The random-motion-over-ground model (RMoG) is extended to time series and revisited to capture the short- and long-term temporal coherence variability caused by motion of the scatterers and changes in the soil and canopy backscatter. The proposed retrieval algorithm estimates first the spatially slow-varying RMoG model parameters using sparse lidar data, and subsequently the spatially fast-varying model parameters such as tree height. The recently published global Sentinel-1 (S-1) interferometric coherence and backscatter data set and sparse spaceborne GEDI lidar data are used to illustrate the algorithm. Results obtained for a small region over Spain show that the temporal coherence and backscatter time series have the potential to be used for global, model-based land parameter estimation. [ABSTRACT FROM AUTHOR]

This letter proposes a LiDAR and image line-guided stereo matching method (L2GSM), which combines sparse but high-accuracy LiDAR points and sharp object edges of images to generate accurate and fine-structure point clouds. After extracting depth discontinuity lines on the image by using LiDAR depth information, we propose a trilateral update of cost volume and depth discontinuity lines-aware semi-global matching (SGM) strategies to integrate LiDAR data and depth discontinuity lines into the dense matching algorithm. The experimental results for the indoor and aerial datasets show that our method significantly improves the results of the original SGM and outperforms two state-of-the-art LiDAR constraints’ SGM methods, especially in recovering the 3-D structure of low-textured and depth discontinuity regions. In addition, the 3-D point clouds generated by our proposed method outperform the LiDAR data and dense matching point clouds generated by Metashape and SURE aerial in terms of completeness and edge accuracy. [ABSTRACT FROM AUTHOR]

This article describes the development and implementation of a 3D lidar perception framework to guarantee the precise cognition of the surrounding environment for urban autonomous driving. The proposed framework consists of two different detection modules operating in parallel: a deep learning-based and a geometric model-free cluster-based method. The first module utilizes the convolutional gated recurrent unit (ConvGRU)-based residual network (CGRN). The module aims to repredict 3D objects based on results from a continuous single-frame detection network. A vision-fusion methodology based on 2D projection is adopted for postprocessing in the first module. The second module utilizes geometric model-free area (GMFA) cluster detection and is designed to cope with false-negative cases of unclassified objects from the prior module. For the second module, a cluster variance-based ground removal is conducted to prevent false-positive cases. A kinematic model-based particle filter (PF) is then applied to estimate the dynamic states of detection. The suggested framework has been developed with real-time operation in mind, to be implemented in autonomous vehicles equipped with automotive lidars and low-cost cameras. The test results show that the framework with CGRN and GMFA successfully improved the surrounding object detection and state estimation accuracy in urban autonomous driving. [ABSTRACT FROM AUTHOR]