A histogram‐based sampling method for point cloud registration

Accurate and efficient registration of unmanned aerial vehicle light detection and ranging (UAV‐lidar) and terrestrial lidar (T‐lidar) data is crucial for forest structure parameter extraction. This study proposes a novel method based on a starburst pattern for the automatic registration of UAV‐lidar and T‐lidar data in forest scenes. It employs density‐based spatial clustering of applications with noise (DBSCAN) for individual tree identification, constructs starburst patterns separately from both lidar sources, and utilises polar coordinate rotation and matching to achieve coarse registration. Fine registration is achieved using the iterative closest point (ICP) algorithm. Experimental results demonstrate that the starburst‐pattern‐based method achieves the desired registration accuracy (average coarse registration error of 0.157 m). Further optimisation with ICP yields slight improvements with an average fine registration error of 0.149 m. Remarkably, the proposed method is insensitive to the individual tree detection number when exceeding 10, and the tree position error has minimal impact on registration accuracy. Furthermore, our proposed method outperforms two existing methods in T‐lidar and UAV‐lidar registration over forest environments.

Section: Accuracy Verificationmentioning

confidence: 99%

A novel method based on a starburst pattern to register UAV and terrestrial lidar point clouds in forest environments

Feng,

Nie,

Wang

et al. 2024

“…After more than 30 years of development, many mature variants of ICP have emerged. These variants improve one or several steps of the original ICP framework, including subset sampling (e.g., random sampling, octree sampling [Schnabel & Klein, 2006], Voxelgrid filtering [Rusu & Cousins, 2011], histogram sampling [Ervan & Temeltas, 2023]), distance metrics (e.g., pointto-line [Censi, 2008], point-to-plane [Chen & Medioni, 1992], symmetric point-to-plane [Rusinkiewicz, 2019], plane-to-plane [Koide et al, 2021b;Segal et al, 2009]), outlier rejection (e.g., sparse norm [Bouaziz et al, 2013;Li, Hu, & Ai, 2020a], anisotropy ICP [Maier-Hein et al, 2011], M-estimation [Chetverikov et al, 2005;Li, Hu, Ai, & Wang, 2021;Zhang et al, 2022]), and computational efficiency (e.g., Fast ICP [Zhang et al, 2022], EfficientVarICP [Rusinkiewicz & Levoy, 2001], and Anderson-accelerated ICP [Pavlov et al, 2018]).…”

Section: Scan Matchingmentioning

confidence: 99%

3D LiDAR SLAM: A survey

Zhang,

Shi,

2024

Simultaneous localization and mapping (SLAM) is a very challenging yet fundamental problem in the field of robotics and photogrammetry, and it is also a prerequisite for intelligent perception of unmanned systems. In recent years, 3D LiDAR SLAM technology has made remarkable progress. However, to the best of our knowledge, almost all existing surveys focus on visual SLAM methods. To bridge the gap, this paper provides a comprehensive review that summarizes the scientific connotation, key difficulties, research status, and future trends of 3D LiDAR SLAM, aiming to give readers a better understanding of LiDAR SLAM technology, thereby inspiring future research. Specifically, it summarizes the contents and characteristics of the main steps of LiDAR SLAM, introduces the key difficulties it faces, and gives the relationship with existing reviews; it provides an overview of current research hotspots, including LiDAR‐only methods and multi‐sensor fusion methods, and gives milestone algorithms and open‐source tools in each category; it summarizes common datasets, evaluation metrics and representative commercial SLAM solutions, and provides the evaluation results of mainstream methods on public datasets; it looks forward to the development trend of LiDAR SLAM, and considers the preliminary ideas of multi‐modal SLAM, event SLAM, and quantum SLAM.

“…To reconstruct the geometric information of the scene in the image, several reconstruction frameworks are used, such as CMPMVS (Schonberger & Frahm, 2016), multiview environment structure from motion (SFM) (Schonberger & Frahm, 2016)/MVS, OpenMVS (Cernea, 2020), patch‐based multiview stereo frame (PMVS) (Furukawa & Ponce, 2007), and the shading‐aware multiview stereo (SMVS) framework (Langguth et al., 2016). The surface reconstruction algorithm (Labatut et al., 2009) mainly includes Poisson or Delauny network construction, and mesh texture mapping (Allene et al., 2008) assigns two‐dimensional (2D) space point information (such as colour and brightness) to the 3D space points in the object space through a certain mapping relationship (Ervan & Temeltas, 2023). The photometric consistency of the model is maintained by requiring uniform light between blocks (Luo, 2015).…”

Section: Related Workmentioning

confidence: 99%

Rapid 3D modelling: Clustering method based on dynamic load balancing strategy

Ge,

Guo,

et al. 2023

Three‐dimensional (3D) reconstruction is a pivotal research area within computer vision and photogrammetry, offering a valuable foundation of data for the development of smart cities. However, existing methods for constructing 3D models from unmanned aerial vehicle (UAV) images often suffer from slow processing speeds and low central processing unit (CPU)/graphics processing unit (GPU) utilization rates. Furthermore, the utilization of cluster‐based distributed computing for 3D modelling frequently results in inefficient resource allocation across nodes. To address these challenges, this paper presents a novel approach to 3D modelling in clusters, incorporating a dynamic load‐balancing strategy. The method divides the 3D reconstruction process into multiple stages to lay the groundwork for distributing tasks across multiple nodes efficiently. Instead of traditional traversal‐based communication, this approach employs gossip communication techniques to reduce the network overhead. To boost the modelling efficiency, a dynamic load‐balancing strategy is introduced that prevents nodes from becoming overloaded, thus optimizing resource usage during the modelling process and alleviating resource waste issues in multidevice clusters. The experimental results indicate that in small‐scale data environments, this approach improves CPU/GPU utilization by 35.8%/23.4% compared with single‐machine utilization. In large‐scale data environments for cluster‐based 3D modelling tests, this method enhances the average efficiency by 61.4% compared with traditional 3D modelling software while maintaining a comparable model accuracy. In computer vision and photogrammetry, research enhances 3D reconstruction for smart cities. To address slow UAV‐based methods, the study employs dynamic load balancing and ‘gossip’ communication to minimize network overhead. In small data tests, the approach improves CPU and GPU efficiency by 20.7% and 40.3%, respectively. In large data settings, it outperforms existing methods by 61.38% while maintaining accuracy.