Bathymetric Survey of the St. Anthony Channel (Croatia) Using Multibeam Echosounders (MBES)—A New Methodological Semi-Automatic Approach of Point Cloud Post-Processing

Multibeam echosounders (MBES) have become a valuable tool for underwater floor mapping. However, MBES data are often loaded with different measurement errors. This study presents a new user-friendly and methodological semi-automatic approach of point cloud post-processing error removal. The St. Anthony Channel (Croatia) was selected as the research area because it is regarded as one of the most demanding sea or river passages in the world and it is protected as a significant landscape by the Šibenik-Knin County. The two main objectives of this study, conducted within the Interreg Italy–Croatia PEPSEA project, were to: (a) propose a methodological framework that would enable the easier and user-friendly identification and removal of the errors in MBES data; (b) create a high-resolution integral model (MBES and UAV data) of the St. Anthony Channel for maritime safety and tourism promotion purposes. A hydrographic survey of the channel was carried out using WASSP S3 MBES while UAV photogrammetry was performed using Matrice 210 RTK V2. The proposed semi-automatic post-processing of the MBES acquired point cloud was completed in the Open Source CloudCompare software following five steps in which various point filtering methods were used. The reduction percentage in points after the denoising process was 14.11%. Our results provided: (a) a new user-friendly methodological framework for MBES point filtering; (b) a detailed bathymetric map of the St. Anthony Channel with a spatial resolution of 50 cm; and (c) the first integral (MBES and UAV) high-resolution model of the St. Anthony Channel. The generated models can primarily be used for maritime safety and tourism promotion purposes. In future research, ground-truthing methods (e.g., ROVs) will be used to validate the generated models

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

Progress on isprs benchmark on multisensory indoor mapping and positioning

This paper presents the design of the benchmark dataset on multisensory indoor mapping and position (MIMAP) which is sponsored by ISPRS scientific initiatives. The benchmark dataset including point clouds captured by indoor mobile laser scanning system (IMLS) in indoor environments of various complexity. The benchmark aims to stimulate and promote research in the following three fields: (1) SLAM-based indoor point cloud generation; (2) automated BIM feature extraction from point clouds, with an emphasis on theelements, such as floors, walls, ceilings, doors, windows, stairs, lamps, switches, air outlets, that are involved in building managementand navigation tasks ; and (3) low-cost multisensory indoor positioning, focusing on the smartphone platform solution. MIMAP provides a common framework for the evaluation and comparison of LiDAR-based SLAM, BIM feature extraction, and smartphoneindoor positioning methods

Benthic habitat mapping in coastal waters of south–east Australia

The Victorian Marine Mapping Project will improve knowledge on the location, spatial distribution, condition and extent of marine habitats and associated biodiversity in Victorian State waters. This information will guide informed decision making, enable priority setting, and assist in targeted natural resource management planning. This project entails benthic habitat mapping over 500 square kilometers of Victorian State waters using multibeam sonar, towed video and image classification techniques. Information collected includes seafloor topography, seafloor softness and hardness (reflectivity), and information on geology and benthic flora and fauna assemblages collectively comprising habitat. Computerized semi-automated classification techniques are also being developed to provide a cost effective approach to rapid mapping and assessment of coastal habitats.Habitat mapping is important for understanding and communicating the distribution of natural values within the marine environment. The coastal fringe of Victoria encompasses a rich and diverse ecosystem representative of coastal waters of South-east Australia. To date, extensive knowledge of these systems is limited due to the lack of available data. Knowledge of the distribution and extent of habitat is required to target management activities most effectively, and provide the basis to monitor and report on their status in the future.<br /

ISPRS BENCHMARK ON MULTISENSORY INDOOR MAPPING AND POSITIONING

Abstract. In this paper, we present a publicly available benchmark dataset on multisensorial indoor mapping and positioning (MiMAP), which is sponsored by ISPRS scientific initiatives. The benchmark dataset includes point clouds captured by an indoor mobile laser scanning system in indoor environments of various complexity. The benchmark aims to stimulate and promote research in the following three fields: (1) LiDAR-based Simultaneous Localization and Mapping (SLAM); (2) automated Building Information Model (BIM) feature extraction; and (3) multisensory indoor positioning. The MiMAP project provides a common framework for the evaluation and comparison of LiDAR-based SLAM, BIM feature extraction, and smartphone-based indoor positioning methods. This paper describes the multisensory setup, data acquisition process, data description, challenges, and evaluation metrics included in the MiMAP project

Remote Sensing Applications in Coastal Environment

Coastal regions are susceptible to rapid changes, as they constitute the boundary between the land and the sea. The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism. Recent research highlights the strong capabilities for remote sensing applications to monitor, inventory, and analyze the coastal environment. This book contains 12 high-quality and innovative scientific papers that explore, evaluate, and implement the use of remote sensing sensors within both natural and built coastal environments