Two-step adaptive extraction method for ground points and breaklines from lidar point clouds

Wang, Jinhu; Huang, Ronggang; Zang, Yufu; Li, Jianping

doi:10.1016/j.isprsjprs.2016.07.002

Cited by 83 publications

(50 citation statements)

References 25 publications

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“…Ground segmentation from ground and airborne laser point cloud is a well-researched topic [10,[42][43][44]. Curb-based road surface segmentation is a popular method for ground point extraction from MMS data [45].…”

Section: Ground Segmentation Of the Mms Point Cloudmentioning

confidence: 99%

Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

Javanmardi

et al. 2017

Remote Sensing

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Abstract:Various applications have utilized a mobile mapping system (MMS) as the main 3D urban remote sensing platform. However, the accuracy and precision of the three-dimensional data acquired by an MMS is highly dependent on the performance of the vehicle's self-localization, which is generally performed by high-end global navigation satellite system (GNSS)/inertial measurement unit (IMU) integration. However, GNSS/IMU positioning quality degrades significantly in dense urban areas with high-rise buildings, which block and reflect the satellite signals. Traditional landmark updating methods, which improve MMS accuracy by measuring ground control points (GCPs) and manually identifying those points in the data, are both labor-intensive and time-consuming. In this paper, we propose a novel and comprehensive framework for automatically georeferencing MMS data by capitalizing on road features extracted from high-resolution aerial surveillance data. The proposed framework has three key steps: (1) extracting road features from the MMS and aerial data; (2) obtaining Gaussian mixture models from the extracted aerial road features; and (3) performing registration of the MMS data to the aerial map using a dynamic sliding window and the normal distribution transform (NDT). The accuracy of the proposed framework is verified using field data, demonstrating that it is a reliable solution for high-precision urban mapping.

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Section: Ground Segmentation Of the Mms Point Cloudmentioning

confidence: 99%

Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

Javanmardi

et al. 2017

Remote Sensing

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“…The latest approach (Yang et al, 2016) utilizes this idea and achieves an excellent performance. However, handling ALS point clouds with 3D geometric information by converting to 2D image with relatively poor spatial information is confused.…”

Section: Ground Breakline Extractionmentioning

confidence: 99%

“…To generate high quality DEMs (Digital Elevation Models), breaklines should also be considered as constraints in interpolating the grid DEMs or fixed edges in the TINs (Yang et al, 2016). Breaklines are classified into three types, i.e., jump breaklines, crease breaklines, and curvature breaklines (Brugelmann, 2000).…”

Section: Introductionmentioning

confidence: 99%

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Applications of 3d-Edge Detection for Als Point Cloud

Lin

Zhang

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4KEY WORDS: 3D-Edge, Straight Line Segment, Filtering, Airborne Laser Scanning Point Cloud, Random Sample Consensus, Ground Breaklines ABSTRACT:Edge detection has been one of the major issues in the field of remote sensing and photogrammetry. With the fast development of sensor technology of laser scanning system, dense point clouds have become increasingly common. Precious 3D-edges are able to be detected from these point clouds and a great deal of edge or feature line extraction methods have been proposed. Among these methods, an easy-to-use 3D-edge detection method, AGPN (Analyzing Geometric Properties of Neighborhoods), has been proposed. The AGPN method detects edges based on the analysis of geometric properties of a query point's neighbourhood. The AGPN method detects two kinds of 3D-edges, including boundary elements and fold edges, and it has many applications. This paper presents three applications of AGPN, i.e., 3D line segment extraction, ground points filtering, and ground breakline extraction. Experiments show that the utilization of AGPN method gives a straightforward solution to these applications.

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“…Vehicle recognition from an aerial Lidar point cloud is a fundamental task for many Lidar applications, such as digital elevation model generation (Chen et al 2017, Yang et al 2016, urban parking management (Liu et al 2016), traffic management and smart city modeling (Lafarge et al 2012, Xiao et al 2016.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Recognition in Aerial Lidar Point Cloud Based on Dynamic Time Warping

Zhang¹,

Vosselman²,

Elberink³

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:A two-step vehicle recognition method from an aerial Lidar point cloud is proposed in this paper. First, the Lidar point cloud is segmented using the region-growing algorithm with vehicle size limitation. Then the vehicle is recognized according to the profile shape based on dynamic time warping. The proposed method can detect vehicles parking under trees in an urban scene, and classifies the vehicles into different classes. The vehicle location, orientation, parking direction and size can also be determined. The experimental result based on a real urban Lidar point cloud shows that the proposed method can correctly recognize 95.1% of vehicles.

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Two-step adaptive extraction method for ground points and breaklines from lidar point clouds

Cited by 83 publications

References 25 publications

Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

Applications of 3d-Edge Detection for Als Point Cloud

Vehicle Recognition in Aerial Lidar Point Cloud Based on Dynamic Time Warping

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