Search Results (8)

Accurate quantification of riprap volume is critical for cost control, quality assurance, and navigation safety in inland waterway maintenance projects. Conventional methods, such as draft mark reading and RTK-based point surveying, are constrained by limited accuracy, low efficiency, and operational risk. To address these limitations, this study proposes a fully automated riprap volume quantification method based on portable LiDAR simultaneous localization and mapping. The proposed framework establishes a seamless, intervention-free workflow. This automated process sequentially integrates real-time scan monitoring, target vessel extraction, riprap segmentation, deck baseline reconstruction, and 3D volume estimation. Specifically, riprap-laden transport vessels are automatically identified using density-based clustering and trajectory information. Subsequently, deck-loaded riprap piles are extracted through point-cloud geometric analysis and quantified via deck fitting and mesh reconstruction. The method was validated through ten field experiments in the Jingjiang reach of the middle Yangtze River, China. Compared to benchmark volumes established via standard point-cloud processing software, the proposed method achieved an average relative error of 1.37% and a maximum error strictly below 5%. Furthermore, the system proved highly efficient, requiring an average processing time of only 392.1 s per dataset. The results demonstrate that the proposed method is accurate, efficient, and robust, and has strong potential for intelligent riprap quantification in inland waterway engineering. Full article

Forest management planning depends on accurately collecting information on available resources, gathered by forest inventories. However, due to the extent of the planted areas in the world, collecting information traditionally has become challenging. Terrestrial light detection and ranging (LiDAR) has emerged as a promising tool to enhance forest inventory. However, selecting the optimal 3D point cloud density for accurately estimating tree attributes remains an open question. The objective of this study was to evaluate the accuracy of different point densities (points per square meter) in point clouds obtained through portable laser scanning combined with simultaneous localization and mapping (PLS-SLAM). The study aimed to identify tree positions and estimate the diameter at breast height (DBH) and total height (H) of 71 trees in a eucalyptus plantation in Brazil. We also tested a semi-automatic method for estimating total height. Point clouds with densities greater than 100 points/m² enabled the detection of over 88.7% of individual trees. The root mean square error (RMSE) of the best DBH measurement was 1.6 cm (RMSE = 5.9%) and the best H measurement (semi-automatic method) was 1.2 m (RMSE = 4.2%) for the point cloud with 36,000 points/m². When measuring the total heights of the largest trees (H > 31.4 m) using LiDAR, the values were always underestimated considering a reference value, and their measurements were significantly different (p-value < 0.05 by the t-test). For point clouds with a density of 36,000 points/m², the automated DBH and total tree height estimations yielded RMSEs of 5.9% and 14.4%, with biases of 4.8% and −1.4%, respectively. When using point clouds of 10 points/m², RMSE values increased to 18.8% for DBH and 28.4% for total tree height, while the bias was 6.2% and 18.4%, respectively. Additionally, total tree height estimations obtained via a semi-automatic method resulted in a lower RMSE of 4.2% and a bias of 1.5%. These findings indicate that point clouds acquired through PLS-SLAM with densities exceeding 100 points/m² are suitable for automated DBH estimation in the studied plantation. Despite the increased processing time required, the semi-automatic method is recommended for total tree height estimation due to its superior accuracy. Full article

This paper accompanies the initial public release of the EDI multi-modal SLAM dataset, a collection of long tracks recorded with a portable sensor package. These include two global shutter RGB camera feeds, LiDAR scans, as well as inertial and GNSS data from an RTK-enabled IMU-GNSS positioning module—both as satellite fixes and internally fused interpolated pose estimates. The tracks are formatted as ROS1 and ROS2 bags, with separately available calibration and ground truth data. In addition to the filtered positioning module outputs, a second form of sparse ground truth pose annotation is provided using independently surveyed visual fiducial markers as a reference. This enables the meaningful evaluation of systems that directly utilize data from the positioning module into their localization estimates, and serves as an alternative when the GNSS reference is disrupted by intermittent signals or multipath scattering. In this paper, we describe the methods used to collect the dataset, its contents, and its intended use. Full article

The Simultaneous Localization and Mapping (SLAM) scanner is an easy and portable Light Detection and Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper aims to assess the accuracy of two SLAM scanners: the NavVis VLX and the BLK2GO scanner. This assessment is conducted for both outdoor and indoor environments. In this context, two types of reference data were used: the total station (TS) and the static scanner Z+F Imager 5016. To carry out the assessment, four comparisons were tested: cloud-to-cloud, cloud-to-mesh, mesh-to-mesh, and edge detection board assessment. However, the results of the assessments confirmed that the accuracy of indoor SLAM scanner measurements (5 mm) was greater than that of outdoor ones (between 10 mm and 60 mm). Moreover, the comparison of cloud-to-cloud provided the best accuracy regarding direct accuracy measurement without manipulations. Finally, based on the high accuracy, scanning speed, flexibility, and the accuracy differences between tested cases, it was confirmed that SLAM scanners are effective tools for data acquisition. Full article

Forestry operations have become of great importance for a sustainable environment in the past few decades due to the increasing toll induced by rural abandonment and climate change. Robotics presents a promising solution to this problem; however, gathering the necessary data for developing and testing algorithms can be challenging. This work proposes a portable multi-sensor apparatus to collect relevant data generated by several onboard sensors. The system incorporates Laser Imaging, Detection and Ranging (LiDAR), two stereo depth cameras and a dedicated inertial measurement unit (IMU) to obtain environmental data, which are coupled with an Android app that extracts Global Navigation Satellite System (GNSS) information from a cell phone. Acquired data can then be used for a myriad of perception-based applications, such as localization and mapping, flammable material identification, traversability analysis, path planning and/or semantic segmentation toward (semi-)automated forestry actuation. The modular architecture proposed is built on Robot Operating System (ROS) and Docker to facilitate data collection and the upgradability of the system. We validate the apparatus’ effectiveness in collecting datasets and its flexibility by carrying out a case study for Simultaneous Localization and Mapping (SLAM) in a challenging woodland environment, thus allowing us to compare fundamentally different methods with the multimodal system proposed. Full article

The human population is constantly increasing throughout the world, and accordingly, construction is increasing in the same way. Therefore, there is an emergence of irregular and unplanned urbanization. In order to achieve the goal of preventing irregular and unplanned urbanization, it is necessary to monitor the cadastral borders quickly. In this sense, the concept of a sensitive, up-to-date, object-based, 3D, and 4D (4D, 3D + time) cadastral have to be a priority. Therefore, continuously updating cadastral maps is important in terms of sustainability and intelligent urbanization. In addition, due to the increase in urbanization, it has become necessary to update the cadastral information system and produce 3D cadastral maps. However, since there are big problems in data collection in urban areas where construction is rapid, different data-collection devices are constantly being applied. While these data-collection devices have proven themselves in terms of accuracy and precision, new technologies have started to be developed in urban areas especially, which is due to the increase in human population and the influence of environmental factors. For this reason, LiDAR data collection methods and the SLAM algorithm can offer a new perspective for producing cadastral maps in complex urban areas. In this study, 3D laser scanning data obtained from a portable sensor based on the SLAM algorithm are tested, which is a relatively new approach for cadastral surveys in complex urban areas. At the end of this study, two different statistical comparisons and accurate analyses of the proposed methodology with reference data were made. First, WMLS data were compared with GNSS data and RMSE values for X, Y, and Z, and were found to be 4.13, 4.91, and 7.77 cm, respectively. In addition, WMLS length data and cadastral length data from total-station data were compared and RMSE values were calculated as 4.76 cm. Full article

Accurate tree positioning and measurement of structural parameters are the basis of forest inventory and mapping, which are important for forest biomass calculation and community dynamics analyses. Portable backpack lidar that integrates the simultaneous localization and mapping (SLAM) technique with a global navigation satellite system receiver has greater flexibility for tree inventory than terrestrial laser scanning, but it has never been used to measure and map forest structure in a large area (>10¹ hectares) with high tree density. In the present study, we used the LiBackpack DG50 backpack lidar system to obtain the point cloud data of a 10 ha plot of subtropical evergreen broadleaved forest, and applied these data to quantify errors and related factors in the diameter at breast height (DBH) measurements and positioning for more than 1900 individual trees. We found an average error of 4.19 cm in the DBH measurements obtained by lidar, compared with manual field measurements. The incompleteness of the tree stem point clouds was the main factor that caused the DBH measurement errors, and the field DBH measurements and density of the point clouds also had significant impacts. The average tree positioning error was 4.64 m, and it was significantly affected by the distance and route length from the measured trees to the data acquisition start position, whereas it was affected little by the habitat complexity and characteristics of tree stems. The tree positioning measurement error led to increases in the mean value and variability of paired-tree distance error as the sample plot scale increased. We corrected the errors based on the estimates of predictive models. After correction, the DBH measurement error decreased by 31.3%, the tree positioning error decreased by 44.3%, and the paired-tree distance error decreased by 56.3%. As the sample plot scale increased, the accumulated paired-tree distance error stabilized gradually. Full article

A three-dimensional survey of natural caves is often a difficult task due to the roughness of the investigated area and the problems of accessibility. Traditional adopted techniques allow a simplified acquisition of the topography of caves characterized by an oversimplification of the geometry. Nowadays, the advent of LiDAR and Structure from Motion applications eased three-dimensional surveys in different environments. In this paper, we present a comparison between other three-dimensional survey systems, namely a Terrestrial Laser Scanner, a SLAM-based portable instrument, and a commercial photo camera, to test their possible deployment in natural caves survey. We presented a comparative test carried out in a tunnel stretch to calibrate the instrumentation on a benchmark site. The choice of the site is motivated by its regular geometry and easy accessibility. According to the result obtained in the calibration site, we presented a methodology, based on the Structure from Motion approach that resulted in the best compromise among accuracy, feasibility, and cost-effectiveness, that could be adopted for the three-dimensional survey of complex natural caves using a sequence of images and the structure from motion algorithm. The methods consider two different approaches to obtain a low resolution complete three-dimensional model of the cave and ultra-detailed models of most peculiar cave morphological elements. The proposed system was tested in the Gazzano Cave (Piemonte region, Northwestern Italy). The obtained result is a three-dimensional model of the cave at low resolution due to the site’s extension and the remarkable amount of data. Additionally, a peculiar speleothem, i.e., a stalagmite, in the cave was surveyed at high resolution to test the proposed high-resolution approach on a single object. The benchmark and the cave trials allowed a better definition of the instrumentation choice for underground surveys regarding accuracy and feasibility. Full article