Search Results (5,900)

This article addresses the scanning path plan strategy of a rover team composed of three rovers, such that the team explores unknown dark outer space environments. This research considers a dark outer space, where a rover needs to turn on its light and camera simultaneously to measure a limited space in front of the rover. The rover team is deployed from a symmetric base station, and the rover team’s mission is to scan a bounded obstacle-rich workspace, such that there exists no remaining detection hole. In the team, only one rover, the hauler, can locate itself utilizing stereo cameras and Inertial Measurement Unit (IMU). Every other rover follows the hauler, while not locating itself. Since Global Navigation Satellite System (GNSS) is not available in outer space, the localization error of the hauler increases as time goes on. For rover’s location estimate fix, one occasionally makes the rover home to the base station, whose shape and global position are known in advance. Once a rover is near the station, it uses its Lidar to measure the relative position of the base station. In this way, the rover fixes its localization error whenever it homes to the base station. In this research, one makes the rover team fully scan a bounded obstacle-rich workspace without detection holes, such that a rover’s localization error is bounded by letting the rover home to the base station occasionally. To the best of our knowledge, this article is novel in addressing the scanning path plan strategy, so that a rover team fully scans a bounded obstacle-rich workspace without detection holes, while fixing the accumulated localization error occasionally. The efficacy of the proposed scanning and localization strategy is demonstrated utilizing MATLAB-based simulations. Full article

We demonstrate a compact, partially end-pumping Innoslab laser based on a micro-cylindrical lens array homogenizer. A dimension of 12 × 0.4 mm² flat-top pumping line with a Gaussian intensity distribution across the line was simulated by the ray tracing technique. The rate equations considering the asymmetric transverse spatial distributions are theoretically developed. The simulation results are in good agreement with the experimental results. Preliminary data shows that for a pump power of 260 W, a maximum pulse energy of 15.7 mJ was obtained with a pulse width of 8.5 ns at a repetition frequency of 1 kHz. The beam quality M² factors in the unstable and stable directions were 1.732 and 1.485, respectively. The technology has been successfully applied to temperature and humidity profiling lidar and ozone lidar and has been productized, yielding direct economic value. Full article

The Streak-Tube Imaging Lidar (STIL) has been widely used in high-precision measurement systems due to its ability to capture detailed spatial and temporal information. In this paper, we proposed a ranging measurement method that integrates a Time-to-Digital Converter (TDC) with a streak camera in a remote STIL system. In this method, the TDC accurately measures the trigger pulse time, while the streak camera captures high time-resolution images of the laser echo, thereby enhancing both measurement accuracy and range. A corresponding ranging model is developed for this method. To validate the system’s performance, an outdoor experiment covering a distance of up to 6 km was conducted. The results demonstrate that the system achieved a distance measurement accuracy of 0.1 m, highlighting its effectiveness in long-range applications. The experiment further confirms that the combination of STIL and TDC significantly enhances accuracy and range, making it suitable for various long-range, high-precision measurement tasks. Full article

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. Full article

In September 2018, NASA launched the ICESat-2 satellite into a 500 km high Earth orbit. It carried a truly unique lidar system, i.e., the Advanced Topographic Laser Altimeter System or ATLAS. The ATLAS lidar is capable of detecting single photons reflected from a wide variety of terrain (land, ice, tree leaves, and underlying terrain) and even performing bathymetric measurements due to its green wavelength. The system uses a single 5-watt, Q-switched laser producing a 10 kHz train of sub-nanosecond pulses, each containing 500 microjoules of energy. The beam is then split into three “strong” and three “weak” beamlets, with the “strong” beamlets containing four times the power of the “weak” beamlets in order to satisfy a wide range of Earth science goals. Thus, ATLAS is capable of making up to 60,000 surface measurements per second compared to the 40 measurements per second made by its predecessor multiphoton instrument, the Geoscience Laser Altimeter System (GLAS) on ICESat-1, which was terminated after several years of operation in 2009. Low deadtime timing electronics are combined with highly effective noise filtering algorithms to extract the spatially correlated surface photons from the solar and/or electronic background noise. The present paper describes how the ATLAS system evolved from a series of unique and seemingly unconnected personal experiences of the author in the fields of satellite laser ranging, optical antennas and space communications, Q-switched laser theory, and airborne single photon lidars. Full article

This study aims to develop a robust and efficient system to identify ties and ballasts in motion using a variety of non-contact sensors mounted on a robotic rail cart. The sensors include distance LiDAR sensors and inductive proximity sensors for ferrous materials to collect data while traversing railroad tracks. Many existing tie/ballast health monitoring devices cannot be mounted on Hyrail vehicles for in-motion inspection due to their inability to filter out unwanted targets (i.e., ties or ballasts). The system studied here addresses that limitation by exploring several approaches based on distance LiDAR sensors. The first approach is based on calculating the running standard deviation of the measured distance from LiDAR sensors to tie or ballast surfaces. The second approach uses machine learning (ML) methods that combine two primary algorithms (Logistic Regression and Decision Tree) and three preprocessing methods (six models in total). The results indicate that the optimal configuration for non-contact, in-motion differentiation of ties and ballasts is integrating two distance LiDAR sensors with a Decision Tree model. This configuration provides rapid, accurate, and robust tie/ballast differentiation. The study also facilitates further sensor and inspection research and development in railroad track maintenance. Full article

Forest canopy height (FCH) is an important variable for estimating forest biomass and ecosystem carbon sequestration. Spaceborne LiDAR data have been used to create wall-to-wall FCH maps, such as the forest tree height map of China (FCHChina), Global Forest Canopy Height 2020 (GFCH2020), and Global Forest Canopy Height 2019 (GFCH2019). However, these products lack comprehensive assessment. This study used airborne LiDAR data from various topographies (e.g., plain, hill, and mountain) to assess the impacts of different topographical and vegetation characteristics on spaceborne LiDAR-derived FCH products. The results show that GEDI–FCH demonstrates better accuracy in plain and hill regions, while ICESat-2 ATLAS–FCH shows superior accuracy in the mountainous region. The difficulty in accurately capturing photons from sparse tree canopies by ATLAS and the geolocation errors of GEDI has led to partial underestimations of FCH products in plain areas. Spaceborne LiDAR FCH retrievals are more accurate in hilly regions, with a root mean square error (RMSE) of 4.99 m for ATLAS and 3.85 m for GEDI. GEDI–FCH is significantly affected by slope in mountainous regions, with an RMSE of 13.26 m. For wall-to-wall FCH products, the availability of FCH data is limited in plain areas. Optimal accuracy is achieved in hilly regions by FCHChina, GFCH2020, and GFCH2019, with RMSEs of 5.52 m, 5.07 m, and 4.85 m, respectively. In mountainous regions, the accuracy of wall-to-wall FCH products is influenced by factors such as tree canopy coverage, forest cover types, and slope. However, some of these errors may stem from directly using current ATL08 and GEDI L2A FCH products for mountainous FCH estimation. Introducing accurate digital elevation model (DEM) data can improve FCH retrieval from spaceborne LiDAR to some extent. This research improves our understanding of the existing FCH products and provides valuable insights into methods for more effectively extracting accurate FCH from spaceborne LiDAR data. Further research should focus on developing suitable approaches to enhance the FCH retrieval accuracy from spaceborne LiDAR data and integrating multi-source data and modeling algorithms to produce accurate wall-to-wall FCH distribution in a large area. Full article

Radiative transfer models (RTMs) are often used to retrieve biophysical parameters from earth observation data. RTMs with multi-temporal and realistic forest representations enable radiative transfer (RT) modeling for real-world dynamic processes. To achieve more realistic RT modeling for dynamic forest processes, this study presents the 3D-explicit reconstruction of a typical temperate deciduous forest in 2015 and 2022. We demonstrate for the first time the potential use of bitemporal 3D-explicit RT modeling from terrestrial laser scanning on the forward modeling and quantitative interpretation of: (1) remote sensing (RS) observations of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and canopy light extinction, and (2) the impact of canopy gap dynamics on light availability of explicit locations. Results showed that, compared to the 2015 scene, the hemispherical-directional reflectance factor (HDRF) of the 2022 forest scene relatively decreased by 3.8% and the leaf FAPAR relatively increased by 5.4%. At explicit locations where canopy gaps significantly changed between the 2015 scene and the 2022 scene, only under diffuse light did the branch damage and closing gap significantly impact ground light availability. This study provides the first bitemporal RT comparison based on the 3D RT modeling, which uses one of the most realistic bitemporal forest scenes as the structural input. This bitemporal 3D-explicit forest RT modeling allows spatially explicit modeling over time under fully controlled experimental conditions in one of the most realistic virtual environments, thus delivering a powerful tool for studying canopy light regimes as impacted by dynamics in forest structure and developing RS inversion schemes on forest structural changes. Full article

Global Navigation Satellite Systems (GNSSs) frequently encounter challenges in providing reliable navigation and positioning within urban canyons due to signal obstruction. Micro-Electro-Mechanical System (MEMS) Inertial Measurement Units (IMUs) offers an alternative for autonomous navigation, but they are susceptible to accumulating errors. To mitigate these influences, a LiDAR-based Simultaneous Localization and Mapping (SLAM) system is often employed. However, these systems face challenges in drift and error accumulation over time. This paper presents a novel approach to loop closure detection within LiDAR-based SLAM, focusing on the identification of previously visited locations to correct time-accumulated errors. Specifically, the proposed method leverages the vehicular drivable area and IMU trajectory to identify significant environmental changes in keyframe selection. This approach differs from conventional methods that only rely on distance or time intervals. Furthermore, the proposed method extends the SCAN CONTEXT algorithm. This technique incorporates the overall distribution of point clouds within a region rather than solely relying on maximum height to establish more robust loop closure constraints. Finally, the effectiveness of the proposed method is validated through experiments conducted on the KITTI dataset with an enhanced accuracy of 6%, and the local scenarios exhibit a remarkable improvement in accuracy of 17%, demonstrating improved robustness in loop closure detection for LiDAR-based SLAM. Full article

Tactile maps are widely recognized as useful tools for mobility training and the rehabilitation of visually impaired individuals. However, current tactile maps lack real-time versatility and are limited because of high manufacturing and design costs. In this study, we introduce a device (i.e., ClaySight) that enhances the creation of automatic tactile map generation, as well as a model for wearable devices that use low-cost laser imaging, detection, and ranging (LiDAR,) used to improve the immediate spatial knowledge of visually impaired individuals. Our system uses LiDAR sensors to (1) produce affordable, low-latency tactile maps, (2) function as a day-to-day wayfinding aid, and (3) provide interactivity using a wearable device. The system comprises a dynamic mapping and scanning algorithm and an interactive handheld 3D-printed device that houses the hardware. Our algorithm accommodates user specifications to dynamically interact with objects in the surrounding area and create map models that can be represented with haptic feedback or alternative tactile systems. Using economical components and open-source software, the ClaySight system has significant potential to enhance independence and quality of life for the visually impaired. Full article

As the adoption of large-scale model-based AI grows, the field of robotics is undergoing significant changes. The emergence of cloud robotics, where advanced tasks are offloaded to fog or cloud servers, is gaining attention. However, the widely used Robot Operating System (ROS) does not support communication between robot software across different networks. This paper introduces ROS Gateway, a middleware designed to improve the usability and extend the communication range of ROS in multi-network environments, which is important for processing sensor data in cloud robotics. We detail its structure, protocols, and algorithms, highlighting improvements over traditional ROS configurations. The ROS Gateway efficiently handles high-volume data from advanced sensors such as depth cameras and LiDAR, ensuring reliable transmission. Based on the rosbridge protocol and implemented in Python 3, ROS Gateway is compatible with rosbridge-based tools and runs on both x86 and ARM-based Linux environments. Our experiments show that the ROS Gateway significantly improves performance metrics such as topic rate and delay compared to standard ROS setups. We also provide predictive formulas for topic receive rates to guide the design and deployment of robotic applications using ROS Gateway, supporting performance estimation and system optimization. These enhancements are essential for developing responsive and intelligent robotic systems in dynamic environments. Full article

The emergence of automotive-grade LiDARs has given rise to new potential methods to develop novel advanced driver assistance systems (ADAS). However, accurate and reliable parking slot detection (PSD) remains a challenge, especially in the low-light conditions typical of indoor car parks. Existing camera-based approaches struggle with these conditions and require sensor fusion to determine parking slot occupancy. This paper proposes a parking slot detection (PSD) algorithm which utilizes the intensity of a LiDAR point cloud to detect the markings of perpendicular parking slots. LiDAR-based approaches offer robustness in low-light environments and can directly determine occupancy status using 3D information. The proposed PSD algorithm first segments the ground plane from the LiDAR point cloud and detects the main axis along the driving direction using a random sample consensus algorithm (RANSAC). The remaining ground point cloud is filtered by a dynamic Otsu’s threshold, and the markings of parking slots are detected in multiple windows along the driving direction separately. Hypotheses of parking slots are generated between the markings, which are cross-checked with a non-ground point cloud to determine the occupancy status. Test results showed that the proposed algorithm is robust in detecting perpendicular parking slots in well-marked car parks with high precision, low width error, and low variance. The proposed algorithm is designed in such a way that future adoption for parallel parking slots and combination with free-space-based detection approaches is possible. This solution addresses the limitations of camera-based systems and enhances PSD accuracy and reliability in challenging lighting conditions. Full article

In this study, the geomorphological evolution of an inner flank of the Dolomieu at Piton de La Fournaise/La Réunion was investigated with the help of terrestrial laser scanning (TLS) data, terrestrial photogrammetric images, and historical webcam photographs. While TLS data and the terrestrial images were recorded during three field surveys, the study was also able to use historical webcam images that were installed for the monitoring of the volcanic activity inside the crater. Although the webcams were originally intended to be used only for visual monitoring of the area, at certain times they captured image pairs that could be analyzed using structure from motion (SfM) and subsequently processed to create digital terrain models (DTMs). With the help of all the data, the geomorphological evolution of selected areas of the crater was investigated in high temporal and spatial resolution. Surface changes were detected and quantified on scree slopes in the upper area of the crater as well as on scree slopes at the transition from the slope to the crater floor. In addition to their quantification, these changes could be assigned to individual geomorphological processes over time. The webcam photographs were a very important additional source of information here, as they allowed the observation period to be extended further into the past. Besides this, the webcam images made it possible to determine the exact dates at which geomorphological processes were active. Full article

Place recognition is widely used to re-localize robots in pre-built point cloud maps for navigation. However, current place recognition methods can only be used to recognize previously visited places. Moreover, these methods are limited by the requirement of using the same types of sensors in the re-localization process and the process is time consuming. In this paper, a template-matching-based global re-localization framework is proposed to address these challenges. The proposed framework includes an offline building stage and an online matching stage. In the offline stage, virtual LiDAR scans are densely resampled in the map and rotation-invariant descriptors can be extracted as templates. These templates are hierarchically clustered to build a template library. The map used to collect virtual LiDAR scans can be built either by the robot itself previously, or by other heterogeneous sensors. So, an important feature of the proposed framework is that it can be used in environments that have never been visited by the robot before. In the online stage, a cascade coarse-to-fine template matching method is proposed for efficient matching, considering both computational efficiency and accuracy. In the simulation with 100 K templates, the proposed framework achieves a 99% success rate and around 11 Hz matching speed when the re-localization error threshold is 1.0 m. In the validation on The Newer College Dataset with 40 K templates, it achieves a 94.67% success rate and around 7 Hz matching speed when the re-localization error threshold is 1.0 m. All the results show that the proposed framework has high accuracy, excellent efficiency, and the capability to achieve global re-localization in heterogeneous maps. Full article

This study investigates the application of LiDAR point cloud datasets for building segmentation through a combined approach that integrates unsupervised segmentation with evolutionary optimization. The research evaluates the extent of improvement achievable through genetic algorithm (GA) optimization for LiDAR point cloud segmentation. The unsupervised methodology encompasses preprocessing, adaptive thresholding, morphological operations, contour filtering, and terrain ruggedness analysis. A genetic algorithm was employed to fine-tune the parameters for these techniques. Critical tunable parameters, such as the interpolation method for DSM and DTM generation, scale factor for contrast enhancement, adaptive constant and block size for adaptive thresholding, kernel size for morphological operations, squareness threshold to maintain the shape of predicted objects, and terrain ruggedness index (TRI) were systematically optimized. The study presents the top ten chromosomes with optimal parameter values, demonstrating substantial improvements of 29% in the average intersection over union (IoU) score (0.775) on test datasets. These findings offer valuable insights into LiDAR-based building segmentation, highlighting the potential for increased precision and effectiveness in future applications. Full article