Development of a novel data acquisition and processing methodology applied to the boresight alignment of marine mobile LiDAR systems

Le système LiDAR mobile (SLM) est une technologie d'acquisition de données de pointe qui permet de cartographier les scènes du monde réel en nuages de points 3D. Les applications du SLM sont très vastes, de la foresterie à la modélisation 3D des villes, en passant par l'évaluation de l'inventaire routier et la cartographie des infrastructures portuaires. Le SLM peut également être monté sur diverses plateformes, telles que des plateformes aériennes, terrestres, marines, etc. Indépendamment de l'application et de la plateforme, pour s'assurer que le SLM atteigne sa performance optimale et sa meilleure précision, il est essentiel de traiter correctement les erreurs systématiques du système, spécialement l'erreur des angles de visée à laquelle on s'intéresse particulièrement dans cette thèse. L'erreur des angles de visée est définie comme le désalignement rotationnel des deux parties principales du SLM, le système de positionnement et d'orientation et le scanneur LiDAR, introduit par trois angles de visée. En fait, de petites variations angulaires dans ces paramètres peuvent causer des problèmes importants d'incertitude géométrique dans le nuage de points final et il est vital d'employer une méthode d'alignement pour faire face à la problématique de l'erreur des angles de visée de ces systèmes. La plupart des méthodes existantes d'alignement des angles de visée qui ont été principalement développées pour les SLM aériens et terrestres, tirent profit d'éléments in-situ spécifiques et présents sur les sites de levés et adéquats pour ces méthodes. Par exemple, les éléments linéaires et planaires extraits des toits et des façades des maisons. Cependant, dans les environnements sans présence de ces éléments saillants comme la forêt, les zones rurales, les ports, où l'accès aux éléments appropriées pour l'alignement des angles de visée est presque impossible, les méthodes existantes fonctionnent mal, voire même pas du tout. Par conséquent, cette recherche porte sur l'alignement des angles de visée d'un SLM dans un environnement complexe. Nous souhaitons donc introduire une procédure d'acquisition et traitement pour une préparation adéquate des données, qui servira à la méthode d'alignement des angles de visée du SLM. Tout d'abord, nous explorons les différentes possibilités des éléments utilisés dans les méthodes existantes qui peuvent aider à l'identification de l'élément offrant le meilleur potentiel pour l'estimation des angles de visée d'un SLM. Ensuite, nous analysons, parmi un grand nombre de possibles configurations d'éléments (cibles) et patrons de lignes de balayage, celle qui nous apparaît la meilleure. Cette analyse est réalisée dans un environnement de simulation dans le but de générer différentes configurations de cibles et de lignes de balayage pour l'estimation des erreurs des angles de visée afin d'isoler la meilleure configuration possible. Enfin, nous validons la configuration proposée dans un scénario réel, soit l'étude de cas du port de Montréal. Le résultat de la validation révèle que la configuration proposée pour l'acquisition et le traitement des données mène à une méthode rigoureuse d'alignement des angles de visée qui est en même temps précise, robuste et répétable. Pour évaluer les résultats obtenus, nous avons également mis en œuvre une méthode d'évaluation de la précision relative, qui démontre l'amélioration de la précision du nuage de points après l'application de la procédure d'alignement des angles de visée.A Mobile LiDAR system (MLS) is a state-of-the-art data acquisition technology that maps real-world scenes in the form of 3D point clouds. The MLS's list of applications is vast, from forestry to 3D city modeling and road inventory assessment to port infrastructure mapping. The MLS can also be mounted on various platforms, such as aerial, terrestrial, marine, and so on. Regardless of the application and the platform, to ensure that the MLS achieves its optimal performance and best accuracy, it is essential to adequately address the systematic errors of the system, especially the boresight error. The boresight error is the rotational misalignment offset of the two main parts of the MLS, the positioning and orientation system (POS) and the LiDAR scanner. Minor angular parameter variations can cause important geometric accuracy issues in the final point cloud. Therefore, it is vital to employ an alignment method to cope with the boresight error problem of such systems. Most of the existing boresight alignment methods, which have been mainly developed for aerial and terrestrial MLS, take advantage of the in-situ tie-features in the environment that are adequate for these methods. For example, tie-line and tie-plane are extracted from building roofs and facades. However, in low-feature environments like forests, rural areas, ports, and harbors, where access to suitable tie-features for boresight alignment is nearly impossible, the existing methods malfunction or do not function. Therefore, this research addresses the boresight alignment of a marine MLS in a low-feature maritime environment. Thus, we aim to introduce an acquisition procedure for suitable data preparation, which will serve as input for the boresight alignment method of a marine MLS. First, we explore various tie-features introduced in the existing ways that eventually assist in the identification of the suitable tie-feature for the boresight alignment of a marine MLS. Second, we study the best configuration for the data acquisition procedure, i.e., tie-feature(s) characteristics and the necessary scanning line pattern. This study is done in a simulation environment to achieve the best visibility of the boresight errors on the selected suitable tie-feature. Finally, we validate the proposed configuration in a real-world scenario, which is the port of Montreal case study. The validation result reveals that the proposed data acquisition and processing configuration results in an accurate, robust, and repeatable rigorous boresight alignment method. We have also implemented a relative accuracy assessment to evaluate the obtained results, demonstrating an accuracy improvement of the point cloud after the boresight alignment procedure

A Pseudo-rigorous LiDAR System Calibration Approach and a Strategy for Stability Analysis

With LiDAR systems being a crucial technology for near real-time mapping and spatial analysis, the user community needs standardized LiDAR system calibration procedures that are robust for the wide range of users and scenarios. More specifically, a comprehensive calibration approach should entail rigor in automation for matching and handling the irregularity of LiDAR data, as well as generality in terms of type of terrain used and of raw measurement availability. Most times, the sensor model and raw measurements are unavailable to the end user, and therefore rigorous LiDAR system calibration is not possible. For this scenario, pseudo-rigorous methods have been developed that synthesize the raw measurements from the point cloud (and in some cases the trajectory) using certain assumptions (e.g. parallel flight lines). This work introduces a new pseudo-rigorous calibration approach called the Quasi-Rigorous/Quasi-Simplified. The existing pseudo-rigorous approaches include the Simplified and Quasi-Rigorous. The Quasi-Rigorous/Quasi-Simplified approach requires less raw measurements than the Quasi-Rigorous and it can be used for any type of terrain and can incorporate control unlike the Simplified approach. In addition to this new calibration approach, there is a performance analysis to test the robustness of the new and existing pseudo-rigorous approaches in non-ideal conditions, as well as a stability analysis strategy to analyze LiDAR system calibration results from two different dates. The stability analysis strategy quantifies the variation in system parameters over time and serves as an important Quality Assurance tool for consistently producing accurate point clouds throughout the lifespan of a LiDAR mapping system. The experimental results show the successful implementation of the new Quasi-Rigorous/Quasi-Simplified approach with real and simulated data and compares the results with existing rigorous and pseudo-rigorous approaches. After inspecting the point cloud alignment and adjusted coordinates, it was shown that the Quasi-Rigorous/Quasi-Simplified approach is successful in significantly reducing the impact of systematic errors even though it makes several assumptions. Also, when compared to the existing Simplified and Quasi-Rigorous pseudo-rigorous approaches, the Quasi-Rigorous/Quasi-Simplified approach provides maximum capability while maintaining minimal assumptions and no requirements for raw measurements. In the performance analysis, it was shown that the Quasi-Rigorous/Quasi-Simplified and existing pseudo-rigorous calibration approaches are robust under non-ideal conditions, and a 52-100 Percent Improvement was observed even in the extreme cases. Using simulated data, the stability analysis results show how to implement the strategy as a Quality Assurance tool given a stable and an unstable stability analysis outcome. In addition to this, the new calibration approach, and the previous pseudo-rigorous calibration approaches, were successfully used to calibrate a multi-beam spinning LiDAR (VLP-16). This has not previously been done since the pseudo-rigorous calibration methods are developed specifically for single-beam linear scanning LiDAR systems

KINEMATIC CALIBRATION USING LOW-COST LiDAR SYSTEM FOR MAPPING AND AUTONOMOUS DRIVING APPLICATIONS

More recently, mapping sensors for land-based Mobile Mapping Systems (MMSs) have combined cameras and laser scanning measurements defined as Light Detection and Ranging (LiDAR), or laser scanner together. These mobile laser scanning systems (MLS) can be used in dynamic environments and are able of being adopted in traffic-related applications, such as the collection of road network databases, inventory of traffic sign and surface conditions, etc. However, most LiDAR systems are expensive and not easy to access. Moreover, due to the increasing demand of the autonomous driving system, the low-cost LiDAR systems, such as Velodyne or SICK, have become more and more popular these days. These kinds of systems do not provide the total solution. Users need to integrate with Inertial Navigation System/ Global Navigation Satellite System (INS/GNSS) or camera by themselves to meet their requirement. The transformation between LiDAR and INS frames must be carefully computed ahead of conducting direct geo-referencing. To solve these issues, this research proposes the kinematic calibration model for a land-based INS/GNSS/LiDAR system. The calibration model is derived from the direct geo-referencing model and based on the conditioning of target points where lie on planar surfaces. The calibration parameters include the boresight and lever arm as well as the plane coefficients. The proposed calibration model takes into account the plane coefficients, laser and INS/GNSS observations, and boresight and lever arm. The fundamental idea is the constraint where geo-referenced point clouds should lie on the same plane through different directions during the calibration. After the calibration process, there are two evaluations using the calibration parameters to enhance the performance of proposed applications. The first evaluation focuses on the direct geo-referencing. We compared the target planes composed of geo- referenced points before and after the calibration. The second evaluation concentrates on positioning improvement after taking aiding measurements from LiDAR- Simultaneously Localization and Mapping (SLAM) into INS/GNSS. It is worth mentioning that only one or two planes need to be adopted during the calibration process and there is no extra arrangement to set up the calibration field. The only requirement for calibration is the open sky area with the clear plane construction, such as wall or building. Not only has the contribution in MMSs or mapping, this research also considers the self-driving applications which improves the positioning ability and stability

Integrated Sensor Orientation on Micro Aerial Vehicles

Mapping with Micro Aerial Vehicles (MAVs whose weight does not exceed 5 kg) is gaining importance in applications, such as corridor mapping, road and pipeline inspections, or mapping of large areas with homogeneous surface structure, e.g. forest or agricultural fields. When cm-level accuracy is required, the classical approach of sensor orientation does not deliver satisfactory results unless a large number of ground control points (GCPs) is regularly distributed in the mapped area. This may not be a feasible method either due to the associated costs or terrain inaccessibility. This thesis addresses such issues by presenting a development of MAV platforms with navigation and imaging sensors that are able to perform integrated sensor orientation (ISO). This method combines image measurements with GNSS or GNSS/IMU (Global Navigation Satellite System/Inertial Measurement Unit) observations. This innovative approach allows mapping with cm-level accuracy without the support of GCPs, even in geometrically challenging scenarios, such as corridors. The presented solution also helps in situations where automatic image observations cannot be generated, e.g. over water, sand, or other surfaces with low variations of texture. The application of ISO to MAV photogrammetry is a novel solution and its implementation brings new engineering and research challenges due to a limited payload capacity and quality of employed sensors on-board. These challenges are addressed using traditional as well as novel methods of treating observations within the developed processing software. The capability of the constructed MAV platforms and processing tools is tested in real mapping scenarios. It is empirically confirmed that accurate aerial control combined with a state-of-the-art calibration and processing can deliver cm-level ground accuracy, even in the most demanding projects. This thesis also presents an innovative way of mission planning in challenging environments. Indeed, a thorough pre-flight analysis is important not only for obtaining satisfactory mapping quality, but photogrammetric missions must be carried out in compliance with state regulations

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera

The objective of this investigation was to develop and investigate methods for point cloud generation by image matching using aerial image data collected by quadrocopter type micro unmanned aerial vehicle (UAV) imaging systems. Automatic generation of high-quality, dense point clouds from digital images by image matching is a recent, cutting-edge step forward in digital photogrammetric technology. The major components of the system for point cloud generation are a UAV imaging system, an image data collection process using high image overlaps, and post-processing with image orientation and point cloud generation. Two post-processing approaches were developed: one of the methods is based on Bae Systems’ SOCET SET classical commercial photogrammetric software and another is built using Microsoft®’s Photosynth™ service available in the Internet. Empirical testing was carried out in two test areas. Photosynth processing showed that it is possible to orient the images and generate point clouds fully automatically without any a priori orientation information or interactive work. The photogrammetric processing line provided dense and accurate point clouds that followed the theoretical principles of photogrammetry, but also some artifacts were detected. The point clouds from the Photosynth processing were sparser and noisier, which is to a large extent due to the fact that the method is not optimized for dense point cloud generation. Careful photogrammetric processing with self-calibration is required to achieve the highest accuracy. Our results demonstrate the high performance potential of the approach and that with rigorous processing it is possible to reach results that are consistent with theory. We also point out several further research topics. Based on theoretical and empirical results, we give recommendations for properties of imaging sensor, data collection and processing of UAV image data to ensure accurate point cloud generation