Shape Inspection Using N-View Error Accumulation on Low-Fidelity Point Clouds

2019 ◽  
pp. 605-617
Author(s):  
Rafael Radkowski
Keyword(s):  
Point Clouds ◽  
Error Accumulation ◽  
Shape Inspection

scholarly journals GLOBAL REGISTRATION OF KINECT POINT CLOUDS USING AUGMENTED EXTENDED INFORMATION FILTER AND MULTIPLE FEATURES

2016 ◽  
Vol XLII-2/W2 ◽  
pp. 155-160
Author(s):  
Z. Kang ◽  
M. Chang
Keyword(s):  
Point Clouds ◽  
Normal Vector ◽  
Feature Points ◽  
Multiple Features ◽  
Registration Method ◽  
Plane Normal ◽  
Global Registration ◽  
Kinect Camera ◽  
Information Filter ◽  
Error Accumulation

Because the Infra-Red (IR) Kinect sensor only provides accurate depths up to 5 m for a limited field of view (60°), the problem of registration error accumulation becomes inevitable in indoor mapping. Therefore, in this paper, a global registration method is proposed based on augmented extended Information Filter (AEIF). The point cloud registration is regarded as a stochastic system so that AEIF is used to produces the accurate estimates of rigid transformation parameters through eliminating the error accumulation suffered by the pair-wise registration. Moreover, because the indoor scene normally contains planar primitives, they can be employed to control the registration of multiple scans. Therefore, the planar primitives are first fitted based on optimized BaySAC algorithm and simplification algorithm preserving the feature points. Besides the constraint of corresponding points, we then derive the plane normal vector constraint as an additional observation model of AEIF to optimize the registration parameters between each pair of adjacent scans. The proposed approach is tested on point clouds acquired by a Kinect camera from an indoor environment. The experimental results show that our proposed algorithm is proven to be capable of improving the accuracy of multiple scans aligning by 90%.

Path integration is not always limited by error accumulation

2006 ◽  
Author(s):  
Xiaoang Irene Wan ◽  
Ranxiao Frances Wang ◽  
James A. Crowell
Keyword(s):  
Path Integration ◽  
Error Accumulation

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

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.

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

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

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

2018 ◽  
Author(s):  
Marissa J. Dudek ◽  
◽  
John Paul Ligush ◽  
Colin Hogg ◽  
Yonathan Admassu
Keyword(s):  
Point Clouds

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

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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