Jidong Liu

Modern mapping systems usually integrate cameras for visual data, LiDAR sensors for 3D scanning, and integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) units for georeferencing. These devices can be mounted on various platforms, including wheeled mobile units, Uncrewed Aerial Vehicles (UAVs), and backpack systems allowing for high spatial and temporal resolution mapping in diverse environments. However, data collected by modern mapping systems is often prone to misalignments due to system calibration and/or trajectory errors. This study proposes an Automated Universal Multi-Sensor Advanced Triangulation (AUMSAT) framework to address these misalignment errors. The key contribution of AUMSAT is the automated extraction and matching of features (i.e., point-to-plane and plane-to-plane pairs) from image and LiDAR data in various environments such as uninhibited locations characterized by rugged topography with complex land cover, fully foliated natural forests, and packed indoor bulk material storage sites. These features are then used in a Least Squares Adjustment (LSA) engine to minimize geospatial data misalignment by refining system calibration and/or trajectory parameters. AUMSAT can handle acquisitions involving combined GNSS/INS-assisted imaging and LiDAR systems or only GNSS/INS-assisted LiDAR units onboard single or multiple platforms capturing data at the same or different times. To improve feature extraction for scenarios involving rugged topography with complex land cover and fully foliated natural forests, a ground filtering framework is proposed to establish reliable multi-modal conjugate ground features. For other environments rich with manmade structures and impervious surfaces, the AUMSAT considers the entire data through a newly developed approach for identifying conjugate planar features. Next, a tightly coupled camera and/or LiDAR triangulation engine is designed to align extracted/matched features by refining system calibration and/or trajectory parameters. The research validates AUMSAT through three study sites—including uninhabited island characterized by rugged topography with complex land cover, fully foliated natural forest, and packed indoor bulk material storage site. Experimental results demonstrate the effectiveness of the approach in generating well-aligned products from multi-modal, multi-platform, and multi-temporal geospatial data by reducing discrepancies from more than a meter to less than 10 cm.

The utilization of remote sensing technologies for archaeology was motivated by their ability to map large areas within a short time at a reasonable cost. With recent advances in platform and sensing technologies, uncrewed aerial vehicles (UAV) equipped with imaging and Light Detection and Ranging (LiDAR) systems have emerged as a promising tool due to their low cost, ease of deployment/operation, and ability to provide high-resolution geospatial data. In some cases, archaeological sites might be covered with vegetation, which makes the identification of below-canopy structures quite challenging. The ability of LiDAR energy to travel through gaps within vegetation allows for the derivation of returns from hidden structures below the canopy. This study deals with the development and deployment of a UAV system equipped with imaging and LiDAR sensing technologies assisted by an integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) for the archaeological mapping of Dana Island, Turkey. Data processing strategies are also introduced for the detection and visualization of underground structures. More specifically, a strategy has been developed for the robust identification of ground/terrain surface in a site characterized by steep slopes and dense vegetation, as well as the presence of numerous underground structures. The derived terrain surface is then used for the automated detection/localization of underground structures, which are then visualized through a web portal. The proposed strategy has shown a promising detection ability with an F1-score of approximately 92%.

Stockpile volume estimation plays a critical role in several industrial/commercial bulk material management applications. LiDAR systems are commonly used for this task. Thanks to Global Navigation Satellite System (GNSS) signal availability in outdoor environments, Uncrewed Aerial Vehicles (UAV) equipped with LiDAR are frequently adopted for the derivation of dense point clouds, which can be used for stockpile volume estimation. For indoor facilities, static LiDAR scanners are usually used for the acquisition of point clouds from multiple locations. Acquired point clouds are then registered to a common reference frame. Registration of such point clouds can be established through the deployment of registration targets, which is not practical for scalable implementation. For scans in facilities bounded by planar walls/roofs, features can be automatically extracted/matched and used for the registration process. However, monitoring stockpiles stored in dome facilities remains to be a challenging task. This study introduces an image-aided fine registration strategy of acquired sparse point clouds in dome facilities, where roof and roof stringers are extracted, matched, and modeled as quadratic surfaces and curves. These features are then used in a Least Squares Adjustment (LSA) procedure to derive well-aligned LiDAR point clouds. Planar features, if available, can also be used in the registration process. Registered point clouds can then be used for accurate volume estimation of stockpiles. The proposed approach is evaluated using datasets acquired by a recently developed camera-assisted LiDAR mapping platform—Stockpile Monitoring and Reporting Technology (SMART). Experimental results from three datasets indicate the capability of the proposed approach in producing well-aligned point clouds acquired inside dome facilities, with a feature fitting error in the 0.03–0.08 m range.

Transportation agencies in northern environments spend a considerable amount of their budget on salt for winter operations. For example, in the state of Indiana, there are approximately 140 salt storage facilities distributed throughout the state and the state expends between USD 30 M and USD 60 M on inventory and delivery each year. Historical techniques of relying on visual estimates of salt stockpiles can be inaccurate and do not scale well for managing the supply chain during the winter or planning for re-supply during summer months. This paper describes the implementation of a portable pole mounted LiDAR system that can be used to inventory a large barn in under 15 min and describes how this system has been deployed over 90 times at 30 facilities. A quick and easy accuracy test, based upon conservation of volume, was used to provide an independent check on the system performance by repositioning portions of the salt pile. Those tests indicated stockpile volumes can be estimated with an accuracy of approximately 0.1%. The paper concludes by discussing how this technology can be permanently installed near the roof for systematic monitoring throughout the year.

LiDAR data acquired by various platforms provide unprecedented data for forest inventory and management. Among its applications, individual tree detection and segmentation are critical and prerequisite steps for deriving forest structural metrics, especially at the stand level. Although there are various tree detection and localization approaches, a comparative analysis of their performance on LiDAR data with different characteristics remains to be explored. In this study, a new trunk-based tree detection and localization approach (namely, height-difference-based) is proposed and compared to two state-of-the-art strategies—DBSCAN-based and height/density-based approaches. Leaf-off LiDAR data from two unmanned aerial vehicles (UAVs) and Geiger mode system with different point densities, geometric accuracies, and environmental complexities were used to evaluate the performance of these approaches in a forest plantation. The results from the UAV datasets suggest that DBSCAN-based and height/density-based approaches perform well in tree detection (F1 score > 0.99) and localization (with an accuracy of 0.1 m for point clouds with high geometric accuracy) after fine-tuning the model thresholds; however, the processing time of the latter is much shorter. Even though our new height-difference-based approach introduces more false positives, it obtains a high tree detection rate from UAV datasets without fine-tuning model thresholds. However, due to the limitations of the algorithm, the tree localization accuracy is worse than that of the other two approaches. On the other hand, the results from the Geiger mode dataset with low point density show that the performance of all approaches dramatically deteriorates. Among them, the proposed height-difference-based approach results in the greatest number of true positives and highest F1 score, making it the most suitable approach for low-density point clouds without the need for parameter/threshold fine-tuning.

Stockpile quantity monitoring is vital for agencies and businesses to maintain inventory of bulk material such as salt, sand, aggregate, lime, and many other materials commonly used in agriculture, highways, and industrial applications. Traditional approaches for volumetric assessment of bulk material stockpiles, e.g., truckload counting, are inaccurate and prone to cumulative errors over long time. Modern aerial and terrestrial remote sensing platforms equipped with camera and/or light detection and ranging (LiDAR) units have been increasingly popular for conducting high-fidelity geometric measurements. Current use of these sensing technologies for stockpile volume estimation is impacted by environmental conditions such as lack of global navigation satellite system (GNSS) signals, poor lighting, and/or featureless surfaces. This study addresses these limitations through a new mapping platform denoted as Stockpile Monitoring and Reporting Technology (SMART), which is designed and integrated as a time-efficient, cost-effective stockpile monitoring solution. The novel mapping framework is realized through camera and LiDAR data-fusion that facilitates stockpile volume estimation in challenging environmental conditions. LiDAR point clouds are derived through a sequence of data collections from different scans. In order to handle the sparse nature of the collected data at a given scan, an automated image-aided LiDAR coarse registration technique is developed followed by a new segmentation approach to derive features, which are used for fine registration. The resulting 3D point cloud is subsequently used for accurate volume estimation. Field surveys were conducted on stockpiles of varying size and shape complexity. Independent assessment of stockpile volume using terrestrial laser scanners (TLS) shows that the developed framework had close to 1% relative error.

Forensic crash investigation often requires developing detailed profiles showing the location and extent of vehicle damage to identify impact areas, impact direction, deformation, and estimated vehicle speeds at impact. Traditional damage profiling techniques require extended and comprehensive setups for mapping and measurement that are quite labor- and time-intensive. Due to the time involved, this damage profiling is usually done in a remote holding area after the crash scene is cleared. Light detection and ranging (LiDAR) scanning technology in consumer handheld electronic devices, such as smartphones and tablets, holds significant potential for conducting this damage profile mapping in just a few minutes, allowing the mapping to be conducted at the scene before the vehicle(s) are moved. However, there is limited research and even scarcer published literature on field procedures and/or accuracy for these emerging smartphones and tablets with LiDAR. This paper proposes a methodology and subsequent measurement accuracy comparisons for survey-grade terrestrial laser scanning (TLS) and handheld alternatives. The maximum root mean square error (RMSE) obtained for profile distance between handheld (iPad) and survey-grade TLS LiDAR scans for a damaged vehicle was observed to be 3 cm, a level of accuracy that is likely sufficient and acceptable for most forensic studies.

Collecting precise as-built data is essential for tracking construction progress. Three-dimensional models generated from such data capture the as-is conditions of the structures, providing valuable information for monitoring existing infrastructure over time. As-built data can be acquired using a wide range of remote sensing technologies, among which mobile LiDAR is gaining increasing attention due to its ability to collect high-resolution data over a relatively large area in a short time. The quality of mobile LiDAR data depends not only on the grade of onboard LiDAR scanners but also on the accuracy of direct georeferencing information and system calibration. Consequently, millimeter-level accuracy is difficult to achieve. In this study, the performance of mapping-grade and surveying-grade mobile LiDAR systems for bridge monitoring is evaluated against static laser scanners. Field surveys were conducted over a concrete bridge where grinding was required to achieve desired smoothness. A semi-automated, feature-based fine registration strategy is proposed to compensate for the impact of georeferencing and system calibration errors on mobile LiDAR data. Bridge deck thickness is evaluated using surface segments to minimize the impact of inherent noise in the point cloud. The results show that the two grades of mobile LiDAR delivered thickness estimates that are in agreement with those derived from static laser scanning in the 1 cm range. The mobile LiDAR data acquisition took roughly five minutes without having a significant impact on traffic, while the static laser scanning required more than three hours.