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Removing Snowfall Noise From point clouds Without Considering the Nearest Neighbors

IEEE SENSORS JOURNAL 2025年第3期25卷 5212-5220页

作者： Nakajima, Kensho Nishio, Nobuhiko Ritsumeikan Univ Dept Informat Sci & Engn Ibaraki Osaka 5678570 Japan

Light detection and ranging (LiDAR) is crucial for recognizing the surrounding environment in autonomous driving. However, snowfall can generate noise in the reflected laser beam of LiDAR, affecting downstream tasks such as object detection in autonomous driving systems. The accuracy and processing speed of existing methods for removing snowfall noise, which primarily adopt approaches based on the nearest neighbors, do not meet the requirements for autonomous driving systems. Considerably, we propose a novel method for snowfall noise removal that does not consider the nearest neighbors;instead, our method establishes threshold values for intensity, distance, and height from the LiDAR by analyzing snowfall noise characteristics. Thus, our method differs from the existing methods. We evaluated the removal accuracy and processing time of our method using the winter adverse driving dataset (WADS). Compared to the existing methods, our method achieves a similar recall but higher precision (by approximately 13.7%) and F-measure (by approximately 7.4%). Furthermore, our method significantly reduces processing time by approximately 93.7%, indicating a substantial improvement in processing speed. Furthermore, to validate its effectiveness, our method was adopted as a preprocessing step for object detection under snowfall conditions, and the Canadian adverse driving conditions (CADC) dataset was employed for evaluation. Results demonstrate that adopting our method led to an approximately 7.6% improvement in the object detection rates (ODRs) compared to detection without our method.

关键词： Adverse weather autonomous driving light detection and ranging (LiDAR) object detection point cloud preprocessing

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PYRF-PCR: A Robust Three-Stage 3D point cloud Registration for Outdoor Scene

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 1270-1281页

作者： Zhang, Junning Huang, Siyuan Liu, Jun Zhu, Xiaoxiu Xu, Feng Natl Univ Def Technol Coll Elect Engn Hefei 230037 Peoples R China PLA Unit 32398 Beijing 100192 Peoples R China Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230026 Peoples R China Fudan Univ Key Lab EWM Informat Shanghai 200437 Peoples R China

point cloud Registration (PCR) has been viewed as an essential part of photogrammetry, remote sensing, and autonomous robot mapping. Existing methods are either sensitive to rotation transformations, or rely on feature learning networks with poor generalization. We propose a novel outdoor point cloud registration algorithm, including preprocessing, yaw angle estimation, coarse registration, and fine registration (in short, PYRF-PCR). Specifically, the preprocessing effectively eliminates the interference of ground point clouds to PCR. The proposed yaw angle estimator solves large yaw-angle matching via a cross-correlation function that converts the focus from the yaw angle estimation to the LiDAR horizontal angular resolution analyses. Then, by using frequency distribution histograms, we improve the fast point feature histogram algorithm to filter the point clouds with a more stable density. For the fine registration, an improved iterative closest point based on target centroid distance is proposed, which reduces the running time and the search range between two point clouds. To validate the widespread applicability of PYRF-PCR, we experimented on both the open-source dataset (KITTI) and the local campus scene dataset. On the KITTI dataset, experimental results illustrate that the PYRF-PCR can achieve state-of-the-art results compared with the existing best methods. On the local scene datasets, the higher quality matching in different types of target point clouds reflects the generalization ability of PYRF-PCR.

关键词： point cloud compression Estimation Feature extraction Histograms Intelligent vehicles Deep learning Three-dimensional displays Cross correlation function frequency distribution histogram point cloud preprocessing point cloud registration yaw angle estimation

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A Trajectory planning technique for grinding and polishing aerospace turbine blades' thermal barrier coatings based on PCL point cloud processing

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2024年

作者： Shan, Zhipeng Cai, Zhenhua Chen, Tingyang Chen, Ziyang Hu, Yu Wuhan Univ Technol Sch Automat Wuhan Peoples R China Wuhan Univ Technol Sch Mat Sci & Engn Wuhan Peoples R China

The core components of an airplane are its aerospace engines, which are intricate in a wide range of applications and specialties. Their high technological barriers make them a crucial barometer of the power of an industrialized nation. The most common component in an engine among these pieces are the aviation blades. Thermal barrier coatings are very beneficial to aviation engine blades because they increase their resistance to high temperatures and corrosion, extending their lifespan in an environment where high temperatures, stress, and corrosion are common. However, the surface roughness of the coating during its application directly impacts engine performance. To reduce the surface roughness, it is necessary to grind and polish the coated surface. Traditional methods involve manual grinding and polishing or CNC machining. The former heavily relies on the skill level of workers, making it challenging to ensure precision, especially for small-sized blades with complex surface curvature. The latter, due to high costs and limited adaptability to various blade types, particularly for small blades, often fails to meet processing requirements. To address these challenges, this paper proposes an automatic grinding and polishing technique based on industrial robots. The method creates robot pathways that adapt to the complex surfaces of the blades by preprocessing scanned point cloud pictures of the blades, segmenting and discretizing the point cloud, and fitting B-spline curves. This approach ensures precision in grinding and polishing while preserving the coating thickness. It also enables quick changes to robot trajectories for various kinds of blades. Based on experimental results, the suggested method can meet the accuracy requirements for industrial blade grinding and polishing procedures by reducing roughness to less than Ra0.7 mu m while preserving the thickness of the blade coating.

关键词： Coating grinding and polishing point cloud preprocessing point cloud segmentation B-spline curve fitting

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DEVELOPING COMPLETE URBAN DIGITAL TWINS IN BUSY ENVIRONMENTS: A FRAMEWORK FOR FACILITATING 3D MODEL GENERATION FROM MULTI-SOURCE point cloud DATA 5

DEVELOPING COMPLETE URBAN DIGITAL TWINS IN BUSY ENVIRONMENTS...

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5th International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS) Geospatial Week (GSW)

作者： Ismail, M. H. Shaker, A. Li, S. Toronto Metropolitan Univ Dept Civil Engn 350 Victoria St Toronto ON M5B 2K3 Canada

The proliferation of affordable LiDAR technology and photogrammetry sensors has revolutionized 3D data acquisition in built environments, enabling comprehensive data capture from citywide scales to interior structures. This data can be transformed into digital twins, providing valuable resources for city planners, architects, engineers, and decision-makers. However, current studies often overlook the limitations of real-world point cloud datasets derived from LiDAR systems, which are voluminous, noisy, incomplete, and lacking information, which hinders monitoring, interpretation, and automated analysis. To address these challenges, methods are required to prepare point cloud data, ensuring accurate and reliable 3D representations. This research proposes a detailed framework for point cloud data preparation in busy urban environments. It includes precise algorithms, software, and parameter guidelines, allowing for the creation of comprehensive point cloud datasets. The framework has been successfully implemented on datasets acquired in Toronto, converting point cloud data from various platforms and parameters into an integrated dataset. Results demonstrate the framework's effectiveness in producing accurate and complete point cloud datasets for applications such as classification, information extraction, 3D model generation, and smart cities' monitoring and management.

关键词： Busy Urban Environments Multi-source point cloud Data Fusion point cloud Data Preparation point cloud preprocessing point cloud Visualization Scan-to-BIM Smart Cities Urban Reconstruction

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Mobile Laser Scanning Data Collected under a Forest Canopy with GNSS/INS-Positioned Systems: Possibilities of Processability Improvements

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REMOTE SENSING 2024年第10期16卷 1734页

作者： Cenava, Juraj Tucek, Jan Chuda, Juliana Koren, Milan Tech Univ Zvolen Fac Forestry Dept Forest Resource Planning & Informat TG Masaryka 24 Zvolen 96001 Slovakia Tech Univ Zvolen Fac Forestry Dept Forest Harvesting Logist & Ameliorat TG Masaryka 24 Zvolen 96001 Slovakia

GNSS/INS-based positioning must be revised for forest mapping, especially inside the forest. This study deals with the issue of the processability of GNSS/INS-positioned MLS data collected in the forest environment. GNSS time-based point clustering processed the misaligned MLS point clouds collected from skid trails under a forest canopy. The points of a point cloud with two misaligned copies of the forest scene were manually clustered iteratively until two partial point clouds with the single forest scene were generated using a histogram of GNSS time. The histogram's optimal bin width was the maximum bin width used to create the two correct point clouds. The influence of GNSS outage durations, signal strength statistics, and point cloud parameters on the optimal bin width were then analyzed using correlation and regression analyses. The results showed no significant influence of GNSS outage duration or GNSS signal strength from the time range of scanning the two copies of the forest scene on the optimal width. The optimal bin width was strongly related to the point distribution in time, especially by the duration of the scanned plot's occlusion from reviewing when the maximum occlusion period influenced the optimal bin width the most (R2 = 0.913). Thus, occlusion of the sub-plot scanning of tree trunks and the terrain outside it improved the processability of the MLS data. Therefore, higher stem density of a forest stand is an advantage in mapping as it increases the duration of the occlusions for a point cloud after it is spatially tiled.

关键词： mobile laser scanning data forest canopy signal strength GNSS outages point cloud preprocessing processability correlation and regression analyses occlusion

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Type-based outlier removal framework for point clouds

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INFORMATION SCIENCES 2021年 580卷 436-459页

作者： Ge, Linlin Feng, Jieqing State Key Lab of CAD&CG Zhejiang University Hangzhou 310058 China

point clouds from scanners or stereo vision inevitably contain outliers which have negative effects on subsequent procedures. Previous works classify outliers according to their characteristics, which guide the design of targeted outlier removal methods. Thus, outlier classification is critical to the corresponding method and outlier removal effect. The proposed type-based outlier removal framework (TBORF) aims to classify outliers more elaborately by considering both the characteristics of the underlying point cloud and the outliers. Therefore, the designed outlier removal methods can be more targeted. To this end, the framework first quantifies the characteristics of the input point cloud using three proposed metrics. According to this quantitative result, the input point clouds are classified into four types. Meanwhile, three new single-criterion methods are proposed to improve the effect on specific types of outlier removal. Based on the point cloud classification and the proposed single-criterion methods, an appropriate combined method is carefully designed for each type of point cloud. Performance evaluations on both outdoor and indoor point cloud datasets demonstrate that TBORF can effectively remove various outliers, facilitating subsequent digital geometry processing operations. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Outlier removal point cloud preprocessing 3D scanner Stereo vision

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Centroid real-time measurement method based on point cloud fusion

Centroid real-time measurement method based on point cloud f...

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Conference on AOPC - Advanced Laser Technology and Applications

作者： Yang, Yubang Huang, Tengchao Shu, Xiaowu Zhejiang Univ State Key Lab Modern Opt Instrumentat 38 Zheda Rd Hangzhou 310027 Peoples R China

ISBN: (数字)9781510649965

ISBN: (纸本)9781510649965;9781510649958

In this paper, we propose a non-contact real-time centroid measurement method for non-cooperative targets, which calculates the coordinates of the centroid point of the motion model in real time at a frequency of 30 Hz. The TOF (Time of flight) camera configuration, working principle and point cloud preprocessing technology including point cloud fusion of the measurement system are presented. Through the XYZ coordinates of the point cloud data, we calculate the centroid point position of the model. Experiments show that the measured centroid point XYZ three-axis accuracy is about 0.1mm.

关键词： Centroid measurement TOF camera point cloud fusion point cloud preprocessing

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Differential evolution algorithm-based range image registration for free-form surface parts quality inspection

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SWARM AND EVOLUTIONARY COMPUTATION 2017年 36卷 106-123页

作者： Li, Taifeng Pan, Quanke Gao, Liang Li, Peigen Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan Hubei Peoples R China

Increasing demands on precision manufacturing of complex free-form surface parts have been observed in the past several years. Although some advanced techniques have been employed to solve the design and machining problems for such parts, quality inspection remains a difficult problem. Registration is a crucial issue in surface inspection;it is used to transform the design model and measurement model into a common coordinate system. The comparison results are then outputted in a report and displayed visually by color gradients. This paper presents a design model-based inspection method with range image registration, in which the measurement model is represented by a series of 3D discrete points. In the model preprocessing, the directed Hausdorff distance (DHD) method is employed for point cloud simplification, and a novel point descriptor is designed to evaluate the property of each point. Subsequently, a differential evolution (DE) algorithm-based optimizer is proposed for error evaluation. Combined with the properties of 3D points, the optimizer can measure the similarity between the design model and the measurement model with a recursive process. The proposed algorithms have been implemented and tested with several sets of simulated and real data. The experiment results illustrate that they are effective and efficient for free-form surface part quality inspection.

关键词： Range image registration Quality inspection point cloud preprocessing Differential evolution algorithm

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A 3D Modeling Method of Indoor Objects Using Kinect Sensor 10

A 3D Modeling Method of Indoor Objects Using Kinect Sensor

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10th International Symposium on Computational Intelligence and Design (ISCID)

作者： Shen, Bowei Yin, Fang Chou, Wusheng Beihang Univ Sch Mech Engn & Automat Beijing Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China

ISBN: (纸本)9781538636756

Indoor 3D object modeling is often used for object recognition, location and other robot manipulating task. In this paper, we put forward a common method to build 3D model of household object for robotic grasping. The point clouds including object from different viewpoints are captured from the Microsoft's Kinect v2 sensor. A pixel filtering approach is used to process depth image and morphology algorithm is implemented to filter noise points in the point clouds of object. FPFH descriptor is extracted from each point. Sample Consensus Initial Alignment and ICP algorithm is used to register two adjacent point cloud accurately. Based on a closed loop optimization method, the cumulative error from continuous registration is reduced. We build some 3D models of indoor objects through the proposed approach. The experiment results shows that the method is convenient and can meet the accuracy requirements.

关键词： 3D modeling point cloud preprocessing sample consensus initial alignment kinect

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