- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Automatic Mapping of Center Line of Railway Tracks using Global Navigation Satellite System, Inertial Measurement Unit and Laser Scanner
摘要： Up-to-date geodatasets on railway infrastructure are valuable resources for the field of transportation. This paper investigates three methods for mapping the center lines of railway tracks using heterogeneous sensor data: (i) conditional selection of satellite navigation (GNSS) data, (ii) a combination of inertial measurements (IMU data) and GNSS data in a Kalman filtering and smoothing framework and (iii) extraction of center lines from laser scanner data. Several combinations of the methods are compared with a focus on mapping in tree-covered areas. The center lines of the railway tracks are extracted by applying these methods to a test dataset collected by a road-rail vehicle. The guard rails in the test area were also extracted during the center line detection process. The combination of methods (i) and (ii) gave the best result for the track on which the measurement vehicle had moved, mapping almost 100% of the track. The combination of methods (ii) and (iii) and the combination of all three methods gave the best result for the other parallel tracks, mapping between 25% and 80%. The mean perpendicular distance of the mapped center lines from the reference data was 1.49 meters.
关键词： Inertial Measurement Unit,Global Navigation Satellite System,automated mapping,Kalman filter,laser scanner,railway tracks
更新于2025-11-21 11:01:37
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[IEEE IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Yokohama, Japan (2019.7.28-2019.8.2)] IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Reconstruction of Airborne Laser Scanner Trajectory From Data
摘要： Multi-echo airborne laser scanner (ALS) has shown increasing utility for forestry applications in the two past decades. Among the numerous algorithms developed to process ALS data on forest environments some require to know actual sensor trajectory and deduced angles of incidence. However, sensor trajectory is not part of the ALS standard LAS file format and is often not delivered with point clouds. Scan angle is usually specified with a one byte precision or not given at all. This paper presents a method for the reconstruction of the sensor trajectory from a multi-echo ALS point cloud. It is based on the intersection of multi-echo pulses and was tested on three data sets acquired over a deciduous, a tropical and a mountainous forest, respectively. It allows sensor location estimate and scan angle estimate with less than 25 cm and 2·10-2° error.
关键词： Lidar,Trajectory Inversion,Airborne Laser Scanner,Forest
更新于2025-09-23 15:21:01
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A Local Projection-Based Approach to Individual Tree Detection and 3-D Crown Delineation in Multistoried Coniferous Forests Using High-Density Airborne LiDAR Data
摘要： Accurate crown detection and delineation of dominant and subdominant trees are crucial for accurate inventorying of forests at the individual tree level. The state-of-the-art tree detection and crown delineation methods have good performance mostly with dominant trees, whereas exhibits a reduced accuracy when dealing with subdominant trees. In this paper, we propose a novel approach to accurately detect and delineate both the dominant and subdominant tree crowns in conifer-dominated multistoried forests using small footprint high-density airborne Light Detection and Ranging data. Here, 3-D candidate cloud segments delineated using a canopy height model segmentation technique are projected onto a novel 3-D space where both the dominant and subdominant tree crowns can be accurately detected and delineated. Tree crowns are detected using 2-D features derived from the projected data. The delineation of the crown is performed at the voxel level with the help of both the 2-D features and 3-D texture information derived from the cloud segment. The texture information is modeled by using 3-D Gray Level Co-occurrence Matrix. The performance evaluation was done on a set of six circular plots for which reference data are available. The high detection and delineation accuracies obtained over the state of the art prove the performance of the proposed method.
关键词： forest,3-D tree crown delineation,tree top detection,airborne laser scanner,Light Detection and Ranging (LiDAR)
更新于2025-09-23 15:21:01
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[Developments in Earth Surface Processes] Remote Sensing of Geomorphology Volume 23 || Terrestrial laser scanner applied to fluvial geomorphology
摘要： Measuring river geometry and its evolution through time has always been a cornerstone of fluvial geomorphology. While experimental and numerical modeling of fluvial dynamics has been central in understanding long-term dynamics and testing ideas, they remain simplified versions of complex natural systems and cannot necessarily include all relevant processes. Field measurements are thus central to our understanding of elementary processes such as sediment entrainment and deposition, bank erosion, bedrock incision as well as the macroscopic dynamics of river reaches such as channel bed accretion/erosion, bedforms mobility, and river meandering. It is therefore not surprising that fluvial geomorphologists have quickly embraced the use of terrestrial laser scanner (TLS) to study rivers (e.g., Heritage and Hetherington, 2007; Hodge et al., 2009a). TLS allows 3D digitization of fluvial environment in a dense (sub-cm), accurate (mm precision), and nearly exhaustive way (Fig. 1). The very large range of spatial scales covered is particularly impressive, from individual pebbles to km long river reaches (e.g., Brasington et al., 2012). Sub-cm accuracy also offers the possibility of detecting very subtle changes (Lague et al., 2013), a key attribute to measure slow processes such as bedrock abrasion (Beer et al., 2017). Given the recent emphasis on the role of riparian processes on fluvial processes, the ability to digitize vegetation in 3D in relation to channel morphology offers a unique perspective in biogeomorphology. However, many of the promises of TLS have not really been fulfilled, and the scientific potential of the TLS dataset remains often untapped. This is largely due to the challenging aspects surrounding the processing of TLS data which, to a large extent, also apply to structure from motion (SfM) surveys (Passalacqua et al., 2015). Three challenges, akin to typical Big Data issues can be identified as follows: 1. Data Complexity: TLS data are 3D data and nearly exhaustive. This makes for very rich data but also extremely complex to process as the relevant information (e.g., ground, grains, riverbanks, vegetation) must be detected prior to scientific analysis (Fig. 1). TLS data is also natively non-regularly sampled, with strong spatial variations in point density and requires processing methods that are more complex than for 2D raster-based data such as satellite imagery. 2. Data Volume: the latest generation of TLS instruments generates billions of points in a day. Manual processing cannot realistically be applied, and automatic processing methods are paramount. This requires good programing skills as well as a culture of machine learning and computer vision approaches that are not necessarily part of the training of geomorphologists and requires bridging the gap with computer sciences. 3. Data Incompleteness: despite the very large field of view of TLS sensors, the resulting 3D data do not sample the entire surface (Fig. 1). The ground-based viewpoint imparts missing data behind obstacles (grains of any size and vegetation) and the laser is generally fully absorbed by water resulting in the lack of bathymetric data, a strong limitation in river environments. Processing methods must account for this lack of information.
关键词： Terrestrial laser scanner,sediment transport,vegetation classification,bank erosion,3D digitization,point cloud processing,bedrock incision,fluvial geomorphology
更新于2025-09-23 15:19:57
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Understanding Tree-to-Tree Variations in Stone Pine (Pinus pinea L.) Cone Production Using Terrestrial Laser Scanner
摘要： Kernels found in stone pinecones are of great economic value, often surpassing timber income for most forest owners. Visually evaluating cone production on standing trees is challenging since the cones are located in the sun-exposed part of the crown, and covered by two vegetative shoots. Very few studies were carried out in evaluating how new remote sensing technologies such as terrestrial laser scanners (TLS) can be used in assessing cone production, or in trying to explain the tree-to-tree variability within a given stand. Using data from 129 trees in 26 plots located in the Spanish Northern Plateau, the gain observed by using TLS data when compared to traditional inventory data in predicting the presence, the number, and the average weight of the cones in an individual tree was evaluated. The models using TLS-derived metrics consistently showed better fit statistics, when compared to models using traditional inventory data pertaining to site and tree levels. Crown dimensions such as projected crown area and crown volume, crown density, and crown asymmetry were the key TLS-derived drivers in understanding the variability in inter-tree cone production. These results underline the importance of crown characteristics in assessing cone production in stone pine. Moreover, as cone production (number of cones and average weight) is higher in crowns with lower density, the use of crown pruning, abandoned over 30 years ago, might be the key to increasing production in combination with stand density management.
关键词： modeling,terrestrial laser scanner,inter-tree variability,stone pinecone production,crown characteristics
更新于2025-09-23 15:19:57
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Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans
摘要： Autonomous navigation of ground vehicles on natural environments requires looking for traversable terrain continuously. This paper develops traversability classifiers for the three-dimensional (3D) point clouds acquired by the mobile robot Andabata on non-slippery solid ground. To this end, different supervised learning techniques from the Python library Scikit-learn are employed. Training and validation are performed with synthetic 3D laser scans that were labelled point by point automatically with the robotic simulator Gazebo. Good prediction results are obtained for most of the developed classifiers, which have also been tested successfully on real 3D laser scans acquired by Andabata in motion.
关键词： 3D laser scanner,field robotics,sensor simulation,traversability,supervised machine learning
更新于2025-09-23 15:19:57
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[IEEE 2019 2nd International Conference on Electrical, Communication, Computer, Power and Control Engineering (ICECCPCE) - Mosul, Iraq (2019.2.13-2019.2.14)] 2019 2nd International Conference on Electrical, Communication, Computer, Power and Control Engineering (ICECCPCE) - Virtual Environment Modelling using Simulated Laser Scanners
摘要： Life quality of people with sever motor disabilities can be improved by developing and inventing new assistive technologies. In this context, it is proposed to develop a semi-autonomous electric wheelchair that has capabilities of navigating through various environments which include different types and sizes of obstacles. This paper describes a methodology to use a range laser scanner mounted on an electric wheelchair to map different environments. The electric wheelchair is simulated in a virtual environment and is developed at the Neurophysiology Laboratory of University of Strathclyde. Mapping the environment is dependent on the information provided by the range laser. An algorithm was developed in MATLAB to record the data received by the range laser and use the data to map the environments and produce a 2D map. The suggested algorithm has been tested using two virtual environments representing rooms with different features. The results showed that range laser scanner can be used on an electric wheelchair platform for efficient mapping of the environment.
关键词： Assistive technologies,Range laser scanner,Virtual environment,Electric wheelchair,Shared control
更新于2025-09-23 15:19:57
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Riparian trees genera identification based on leaf-on/leaf-off airborne laser scanner data and machine learning classifiers in northern France
摘要： Riparian forests are valuable environments delivering multiples ecological services. Because they face both natural and anthropogenic constraints, riparian forests need to be accurately mapped in terms of genera/species diversity. Previous studies have shown that the Airborne Laser Scanner (ALS) data have the potential to classify trees in di?erent contexts. However, an assessment of important features and classi?cation results for broadleaved deciduous riparian forests mapping using ALS remains to be achieved. The objective of this study was to estimate which features derived from ALS data were important for describing trees genera from a riparian deciduous forest, and provide results of classi?cations using two Machine Learning algorithms. The procedure was applied to 191 trees distributed in eight genera located along the Sélune river in Normandy, northern France. ALS data from two surveys, in the summer and winter, were used. From these data, trees crowns were extracted and global morphology and internal structure features were computed from the 3D points clouds. Five datasets were established, containing for each one an increasing number of genera. This was implemented in order to assess the level of discrimination between trees genera. The most discriminant features were selected using a stepwise Quadratic Discriminant Analysis (sQDA) and Random Forest, allowing the number of features to be reduced from 144 to 3–9, depending on the datasets. The sQDA-selected features highlighted the fact that, with an increasing number of genera in the datasets, internal structure became more discrimi- nant. The selected features were used as variables for classi?cation using Support Vector Machine (SVM) and Random Forest (RF) algorithms. Additionally, Random Forest classi?cations were conducted using all features computed, without selection. The best classi?ca- tion performances showed that using the sQDA-selected features with SVM produced accuracy ranging from 83.15% when using three genera (Oak, Alder and Poplar). A similar result was obtained using RF and all features available for classi?cation. The latter also achieved the best classi?cation performances when using seven and eight genera. The results highlight that ML algorithms are suitable methods to map riparian trees.
关键词： Machine Learning,Riparian forests,tree genera identification,Support Vector Machine (SVM),Airborne Laser Scanner (ALS),Random Forest (RF)
更新于2025-09-19 17:13:59
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[IEEE 2020 IEEE/SICE International Symposium on System Integration (SII) - Honolulu, HI, USA (2020.1.12-2020.1.15)] 2020 IEEE/SICE International Symposium on System Integration (SII) - Calibration of Laser Optics for Sensor Guided Remote Cutting
摘要： In the last several years, laser based material processing has established itself in many industrial branches as an economical manufacturing process. Laser remote cutting in particular is widespread in the automotive sector. In order to take full advantage of laser remote cutting, it is necessary to position the laser beam precisely on the workpiece using a handling kinematic system. Therefore, this work focuses on the combination of standard industrial robots with laser scanner optics. To address this configuration, this paper proposes a method for the calibration of the parameters, which describe the precise position of the 3D laser scanner optics in respect to the robot. Moreover, the robot introduces a new source of deviation, which is then compensated by using an external measuring system to guide the scanner optics during processing.
关键词： thermography,remote laser scanner optics,machining,calibration,automation,digital image processing
更新于2025-09-19 17:13:59
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ESTIMATING SINGLE TREE STEM AND BRANCH BIOMASS USING TERRESTRIAL LASER SCANNING
摘要： This paper presents a novel non-destructive approach for individual tree stem and branch biomass estimation using terrestrial laser scanning data. The study area is located at the Royal Belum Reserved Forest area, Gerik, Perak. Each forest plot was designed with a circular shape and contains several scanning locations to ensure good visibility of each tree. Unique tree signage was located on trees with diameter at breast height (DBH) of 10cm and above. Extractions of individual trees were done manually and the matching process with the field collected tree properties were relied on the tree signage and tree location as collected by total station. Individual tree stems were reconstructed based on cylinder models from which the total stem volume was calculated. Biomass of individual tree stems was calculated by multiplying stem volume with specific wood density. Biomass of individual was estimated using similar concept of tree stem with the volume estimated from alpha-hull shape. The root mean squared errors (RMSE) of estimated biomass are 50.22kg and 27.20kg for stem and branch respectively.
关键词： Terrestrial laser scanner,stem and branch biomass
更新于2025-09-19 17:13:59