Target-free ULS-TLS point-cloud registration for alpine forest lands

Liu, Qianwei; Wang, Jinliang; Ma, Weifeng; Zhang, Jianpeng; Deng, Yuncheng; Dajiang, Shao; Xu, Dongfan; Liu, Yicheng

doi:10.1016/j.compag.2021.106460

Cited by 9 publications

(6 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, registration of multi‐platform lidar data is challenging due to the complexity of forest scenes and the lack of apparent regular features. Despite the previous studies on preliminary explorations of multi‐platform lidar data registration (Dai et al., 2019; Guan et al., 2019; Liu et al., 2021; Zhang et al., 2021), the accurate and automatic registration of UAV‐lidar and T‐lidar data in forest scenes is still a big challenge. This paper proposes a novel method based on the starburst pattern to automatically register UAV‐lidar and T‐lidar point clouds over forest environments considering the constant spatial relationship between individual trees during the lidar data acquisition period.…”

Section: Discussionmentioning

confidence: 99%

“…In this case, registering forest point clouds between different platforms becomes challenging. Existing studies employed UAV-lidar and T-lidar data to extract the geometric information such as individual tree position (Guan et al, 2019), canopy density (Dai et al, 2019), fast point feature histogram (FPFH) (Zhang et al, 2021) and digital surface model (DSM) (Liu et al, 2021) to achieve the registration of these two pieces of data in forests. For example, Guan et al (2019) and T-lidar data, followed by fine registration via ICP, and achieved coarse and fine registration errors of 0.148 and 0.069 m, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Guan et al (2019) and T-lidar data, followed by fine registration via ICP, and achieved coarse and fine registration errors of 0.148 and 0.069 m, respectively. Zhang et al (2021) performed coarse registration based on FPFH, followed by ICP fine registration with a registration error of 0.948 m. Liu et al (2021) employed T-lidar and UAV-lidar-derived DSMs for coarse registration and achieved a 0.46 m coarse registration error. Pohjavirta et al (2022) proposed a discrete overlap search method to achieve the automatic registration of wide-baseline point clouds in forests, however, this method is prone to fail in T-lidar and UAV-lidar data registration due to low overlap and errors in UAV-lidar point clouds.…”

Section: Introductionmentioning

confidence: 99%

“…(2021) performed coarse registration based on FPFH, followed by ICP fine registration with a registration error of 0.948 m. Liu et al. (2021) employed T‐lidar and UAV‐lidar‐derived DSMs for coarse registration and achieved a 0.46 m coarse registration error. Pohjavirta et al.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

The Photogrammetric Record

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

The Photogrammetric Record

Self Cite

View full text Add to dashboard Cite

show abstract

“…A new alignment and overlap measurement registration algorithm is proposed [9]. A "coarse to fine" registration framework and multi-description voting method is established to solve the fuzzy localization problem by using the seam structure perceptual algorithm Iterated Closest Point (ICP) [10].In addition, Liu et al used tree-high registration (TR) point cloud registration in forest environments using ULS-TLS [11]. Ji et al have implemented a spatial registration method based on multi-sensor time synchronization in different coordinate point clouds [12].…”

Section: Point Cloud Data Fusionmentioning

confidence: 99%

A Method of Making DEM by Data Fusion of Multi-Device Point Cloud

Song

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

When planning to build greenways in jungles, accurate topographic information of the target area is significant. The traditional topographic survey is usually manually conducted with several tools, which is not only labour-intensive and time-consuming, but also has limitations in accuracy and efficiency. Therefore, this paper presents a method to generate a digital elevation model based on unmanned aerial vehicle (UAV) and ground equipment supplements the ground area covered by vegetation where UAV-based equipment cannot reach. Iterated Closest Point (ICP) is first used to register the point cloud data from UAV and ground mobile equipment. Then, point cloud classification is used to non-ground point cloud. Finally, the digital elevation model and contour line are obtained. A real experiment is conducted to validate the presented method. The results show that this method not only overcomes the UAV’s limitation of having difficulty in collecting point cloud data from the covered area, but also realizes large-area point cloud data collection that ground mobile equipment cannot. Compared with the traditional base station and multi-person assistance measurement, the presented method is more efficient and effective.

show abstract