Techniques for extracting data from LiDAR point clouds can be refined for increased accuracy. In this paper, the authors elaborate on an innovative approach for registering ground-based LiDAR point clouds using overlapping scans based on 3D line features. The proposed working scheme consists of three major kernels: a 3D line feature extractor, a 3D line feature matching mechanism, and a mathematical model for simultaneously registering ground-based LiDAR point clouds of multi-scans on a 3D line feature basis. All processing chains in this study are featured efficiently and come close to meeting the needs of practical usage. Experiments conducted show the proposed method of employing 3D line features to be a useful alternative or complement to point, surface and other features for LiDAR (Light Detection And Ranging) point clouds registration. It is especially effective in areas rich in man-made structures.
Wetlands and other critical habitat Bock et al. (2005) Urban areas Weeks et al. (2007) Cleve et al. (2008) Durieux et al. (2008) Land use and land cover Maxwell (2010) Public health Kelly et al. (2011) Disease vector habitats Koch et al. (2007) Troyo et al. (2009) Public health infrastructure (e.g., refugee camps) Lang and Blaschke (2006) Hazard vulnerability and disaster aftermath Al-Khudhairy et al. (2005) Gusella et al. (2005)
Three-dimensional (3D) feature-matching techniques, which are essential for progress towards an automated feature-based procedure, have attracted considerable research attention in both the photogrammetry and computer vision communities. This study introduces a novel matching approach, called RSTG, that comprises four major phases: rotation alignment; scale estimation; translation alignment; and geometry checks. These steps efficiently determine a feature-based correspondence and frame transformation between datasets. RSTG analyses the similarity and relative geometry of features by employing feature observations and their uncertainty; this allows different types of features to be matched exclusively or simultaneously. This study validates the proposed method with both simulated and real datasets, demonstrating its effectiveness with satisfactory matching rates in a diverse range of feature-based point cloud registration tasks. ). Retrieval precision, however, is reduced when object shapes contain abundant selfsimilarities. Studies have attempted to simplify matching problems by first eliminating rotation and translation discrepancies with principal component analysis and centres of gravity. These methods, however, work under extremely specific conditions and only obtain a coarse estimation of the transformation because of geometric and point-to-point constraints (Kim et al., 2011). At the computational level, least squares techniques that minimise spatial distances between homologous primitives are frequently implemented to acquire matching solutions. Consequently, a non-linear calculating procedure is required which calls for additional processes, resulting in a more complex operation (Xu and Li, 2000;Gruen and Akca, 2005). To the authors' knowledge, most existing feature-based methods concentrate only on pairing a specific kind of geometric primitive (Heuel and F€ orstner, 2001;Eden and Cooper, 2008;Kamgar-Parsi and Kamgar-Parsi, 2011).This study proposes a matching approach called RSTG (rotation; scale; translation; geometry) to retrieve corresponding 3D feature counterparts and frame transformations from unsorted datasets. Scenes in urban areas contain various objects, such as buildings, signs, cars, trees and poles, which are generally composed of basic primitive shapes. The feasible features in RSTG comprise points, straight lines and planes. Each feature can be used exclusively, or in combination, as long as the minimum number of features for the matching solution is satisfied. The versatility of feature usage offers high flexibility and facilitates dealing with scene complexity. Moreover, RSTG weights feature observations, allowing multiple features to be managed in an improved manner throughout the matching processes. A hierarchical matching strategy is also administered to increase the success rate and reliability of matching, and to reduce computational complexity.A valid procedure for acquiring features is a prerequisite to any feature-based technique. In this study, geometric features are automatica...
Light detection and ranging (LiDAR) has become a mainstream technique for rapid acquisition of 3-D geometry. Current LiDAR platforms can be mainly categorized into spaceborne LiDAR system (SLS), airborne LiDAR system (ALS), mobile LiDAR system (MLS), and terrestrial LiDAR system (TLS). Point cloud registration between different scans of the same platform or different platforms is essential for establishing a complete scene description and improving geometric consistency. The discrepancies in data characteristics should be manipulated properly for precise transformation estimation. This paper proposes a multi-feature registration scheme suitable for utilizing point, line, and plane features extracted from raw point clouds to realize the registrations of scans acquired within the same LIDAR system or across the different platforms. By exploiting the full geometric strength of the features, different features are used exclusively or combined with others. The uncertainty of feature observations is also considered within the proposed method, in which the registration of multiple scans can be simultaneously achieved. The simulated test with an ideal geometry and data simplification was performed to assess the contribution of different features towards point cloud registration in a very essential fashion. On the other hand, three real cases of registration between LIDAR scans from single platform and between those acquired by different platforms were demonstrated to validate the effectiveness of the proposed method. In light of the experimental results, it was found that the proposed model with simultaneous and weighted adjustment rendered satisfactory registration results and showed that not only features inherited in the scene can be more exploited to increase the robustness and reliability for transformation estimation, but also the weak geometry of poorly overlapping scans can be better treated than utilizing only one single type of feature. The registration errors of multiple scans in all tests were all less than point interval or positional error, whichever dominating, of the LiDAR data.
The 2019 International Symposium on Remote Sensing (ISRS-2019) took place in Taipei, Taiwan from 17 to 19 April 2019. ISRS is one of the distinguished conferences on the photogrammetry, remote sensing and spatial information sciences, especially in East Asia. More than 220 papers were presented in 37 technical sessions organized at the conference. This Special Issue publishes a limited number of featured peer-reviewed papers extended from their original contributions at ISRS-2019. The selected papers highlight a variety of topics pertaining to innovative concepts, algorithms and applications with geospatial sensors, systems, and data, in conjunction with emerging technologies such as artificial intelligence, machine leaning and advanced spatial analysis algorithms. The topics of the selected papers include the following: the on-orbit radiometric calibration of satellite optical sensors, environmental characteristics assessment with remote sensing, machine learning-based photogrammetry and image analysis, and the integration of remote sensing and spatial analysis. The selected contributions also demonstrate and discuss various sophisticated applications in utilizing remote sensing, geospatial data, and technologies to address different environmental and societal issues. Readers should find the Special Issue enlightening and insightful for understanding state-of-the-art remote sensing and spatial information science research, development and applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.