Visibility of Point Clouds and Mapping of Unknown Environments

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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Topological feature recognition and blend feature protection for non-duality point clouds

Optics and Precision Engineering ◽

10.37188/ope.20202810.2301 ◽

2020 ◽

Vol 28 (10) ◽

pp. 2301-2310

Author(s):

Chun-kang ZHANG ◽

◽

Hong-mei LI ◽

Xia ZHANG

Keyword(s):

Feature Recognition ◽

Point Clouds ◽

Topological Feature

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Automatic breakline extraction from LIDAR point clouds

Journal of Computer Applications ◽

10.3724/sp.j.1087.2008.01214 ◽

2008 ◽

Vol 28 (5) ◽

pp. 1214-1216 ◽

Cited By ~ 1

Author(s):

Jing-zhong XU

Keyword(s):

Point Clouds

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Workspace Modeling: Visualization and Pose Estimation of Teleoperated Construction Equipment from Point Clouds

Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC) ◽

10.22260/isarc2020/0108 ◽

2020 ◽

Author(s):

Jing Dao Chen ◽

Pileun Kim ◽

Dong-Ik Sun ◽

Chang-Soo Han ◽

Yong Han Ahn ◽

...

Keyword(s):

Pose Estimation ◽

Point Clouds ◽

Construction Equipment

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USING POINT CLOUDS TO EVALUATE SLOPE PERFORMANCE WITH RESPECT TO ROCKFALL GENERATION

10.1130/abs/2018se-313029 ◽

2018 ◽

Author(s):

Marissa J. Dudek ◽

◽

John Paul Ligush ◽

Colin Hogg ◽

Yonathan Admassu

Keyword(s):

Point Clouds

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Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs13112135 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2135

Author(s):

Jesús Balado ◽

Pedro Arias ◽

Henrique Lorenzo ◽

Adrián Meijide-Rodríguez

Keyword(s):

Laser Scanning ◽

Ambient Noise ◽

Point Clouds ◽

Density Variation ◽

Density Reduction ◽

Manual Intervention ◽

Point Density ◽

Disturbance Analysis ◽

Street Furniture

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

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3D Thermal Monitoring of Jointed Rock Masses through Infrared Thermography and Photogrammetry

Remote Sensing ◽

10.3390/rs13050957 ◽

2021 ◽

Vol 13 (5) ◽

pp. 957

Author(s):

Guglielmo Grechi ◽

Matteo Fiorucci ◽

Gian Marco Marmoni ◽

Salvatore Martino

Keyword(s):

Surface Temperature ◽

Infrared Thermography ◽

Point Cloud ◽

Engineering Geology ◽

Point Clouds ◽

Jointed Rock ◽

Geometric Accuracy ◽

Rock Masses ◽

Natural Systems ◽

Temperature Distributions

The study of strain effects in thermally-forced rock masses has gathered growing interest from engineering geology researchers in the last decade. In this framework, digital photogrammetry and infrared thermography have become two of the most exploited remote surveying techniques in engineering geology applications because they can provide useful information concerning geomechanical and thermal conditions of these complex natural systems where the mechanical role of joints cannot be neglected. In this paper, a methodology is proposed for generating point clouds of rock masses prone to failure, combining the high geometric accuracy of RGB optical images and the thermal information derived by infrared thermography surveys. Multiple 3D thermal point clouds and a high-resolution RGB point cloud were separately generated and co-registered by acquiring thermograms at different times of the day and in different seasons using commercial software for Structure from Motion and point cloud analysis. Temperature attributes of thermal point clouds were merged with the reference high-resolution optical point cloud to obtain a composite 3D model storing accurate geometric information and multitemporal surface temperature distributions. The quality of merged point clouds was evaluated by comparing temperature distributions derived by 2D thermograms and 3D thermal models, with a view to estimating their accuracy in describing surface thermal fields. Moreover, a preliminary attempt was made to test the feasibility of this approach in investigating the thermal behavior of complex natural systems such as jointed rock masses by analyzing the spatial distribution and temporal evolution of surface temperature ranges under different climatic conditions. The obtained results show that despite the low resolution of the IR sensor, the geometric accuracy and the correspondence between 2D and 3D temperature measurements are high enough to consider 3D thermal point clouds suitable to describe surface temperature distributions and adequate for monitoring purposes of jointed rock mass.

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