3D Point Cloud Surface Reconstruction Based on Divide-and-Conquer Method in Laser Scanner

Purpose This study aims to develop an optimized 3D laser point reconstruction using Descent Gradient algorithm. Precise and accurate reconstruction of 3D laser point cloud of the complex environment/object is a key solution for many industries such as construction, gaming, automobiles, aerial navigation, architecture and automation. A 2D laser scanner along with a servo motor/pan tilt/inertial measurement unit is used for generating 3D point cloud (either environment/object or both) by acquiring the real-time data from sensors. However, while generating the 3D laser point cloud, various problems related to time synchronization problem between laser and servomotor and torque variation in servomotors arise, which causes misalignment in stacking the 2D laser scan for generating the 3D point cloud of the environment. Because of the misalignment in stacking, the 2D laser scan corresponding to the erroneous angular and position information by the servomotor and the 3D laser point cloud become distorted in terms of inconsistency for measuring the dimension of the objects. Design/methodology/approach This paper addresses a modified 3D laser system assembled from a 2D laser scanner coupled with a servomotor (dynamixel motor) for developing an efficient 3D laser point cloud with the implementation of an optimization technique: descent gradient filter (DGT). The proposed approach reduces the cost function (error) in the angular and position coordinates of the servo motor caused because of torque variation and time synchronization, which resulted in enhancing the accuracy in 3D point cloud mapping for the accurate measurement of the object’s dimensions. Findings Various real-world experiments are performed with the proposed DGT filter linked with laser scanner and servomotor and an improvement of 6.5 per cent in measuring the accurate dimension of object is obtained while comparing with conventional approaches for generating a 3D laser point cloud. Originality/value This proposed technique may be applicable for various industrial applications that are based on robotics arms (such as painting, welding and cutting) in the automobile industry, the optimized measurement of object, efficient mobile robot navigation, precise 3D reconstruction of environment/object in construction, architecture applications, airborne applications and aerial navigation.

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MatchMesh: Knowledge-based 3D Point Cloud Meshing Using Divide-and-conquer Deformation

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) ◽

10.1109/icnsc48988.2020.9238103 ◽

2020 ◽

Author(s):

Ying Tang ◽

Shengtao Sun ◽

Ben Wu

Keyword(s):

Point Cloud ◽

Divide And Conquer ◽

3D Point Cloud ◽

Knowledge Based

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3D Face Recognition Based on Local Curvature Feature Matching

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.609 ◽

2011 ◽

Vol 121-126 ◽

pp. 609-616

Author(s):

Dao Qing Sheng ◽

Guo Yue Chen ◽

Kazuki Saruta ◽

Yuki Terata

Keyword(s):

Face Recognition ◽

Surface Reconstruction ◽

Point Cloud ◽

High Performance ◽

Feature Matching ◽

3D Face Recognition ◽

3D Point Cloud ◽

Local Curvature ◽

3D Face ◽

B Splines

In this paper, an approach based on local curvature feature matching for 3D face recognition is proposed. K-L transformation is employed to adjust coordinate system and coarsely align 3D point cloud. Based on B-splines approximation, 3D facial surface reconstruction is implemented. Through analyzing curvature features of the fitted surface, local rigid facial patches are extracted. According to the extracted local patches, feature vectors are constructed to execute final recognition. Experimental results demonstrate high performance of the presented method and also show that the method is fairly effective for 3D face recognition.

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ACCURACY OF 3D RECONSTRUCTION IN AN ILLUMINATION DOME

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b5-69-2016 ◽

2016 ◽

Vol XLI-B5 ◽

pp. 69-76 ◽

Cited By ~ 2

Author(s):

Lindsay MacDonald ◽

Isabella Toschi ◽

Erica Nocerino ◽

Mona Hess ◽

Fabio Remondino ◽

...

Keyword(s):

Surface Reconstruction ◽

High Frequency ◽

Point Cloud ◽

Laser Scanner ◽

Low Frequency ◽

Photometric Stereo ◽

Point Cloud Data ◽

Cloud Data ◽

Digital Elevation ◽

Elevation Model

The accuracy of 3D surface reconstruction was compared from image sets of a Metric Test Object taken in an illumination dome by two methods: photometric stereo and improved structure-from-motion (SfM), using point cloud data from a 3D colour laser scanner as the reference. Metrics included pointwise height differences over the digital elevation model (DEM), and 3D Euclidean differences between corresponding points. The enhancement of spatial detail was investigated by blending high frequency detail from photometric normals, after a Poisson surface reconstruction, with low frequency detail from a DEM derived from SfM.

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ACCURACY OF 3D RECONSTRUCTION IN AN ILLUMINATION DOME

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b5-69-2016 ◽

2016 ◽

Vol XLI-B5 ◽

pp. 69-76

Author(s):

Lindsay MacDonald ◽

Isabella Toschi ◽

Erica Nocerino ◽

Mona Hess ◽

Fabio Remondino ◽

...

Keyword(s):

Surface Reconstruction ◽

High Frequency ◽

Point Cloud ◽

Laser Scanner ◽

Low Frequency ◽

Photometric Stereo ◽

Point Cloud Data ◽

Cloud Data ◽

Digital Elevation ◽

Elevation Model

The accuracy of 3D surface reconstruction was compared from image sets of a Metric Test Object taken in an illumination dome by two methods: photometric stereo and improved structure-from-motion (SfM), using point cloud data from a 3D colour laser scanner as the reference. Metrics included pointwise height differences over the digital elevation model (DEM), and 3D Euclidean differences between corresponding points. The enhancement of spatial detail was investigated by blending high frequency detail from photometric normals, after a Poisson surface reconstruction, with low frequency detail from a DEM derived from SfM.

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HIGH ACCURATE POINTWISE (GEO-)REFERENCING OF A K-TLS BASED MULTI-SENSOR-SYSTEM

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-81-2018 ◽

2018 ◽

Vol IV-4 ◽

pp. 81-88 ◽

Cited By ~ 4

Author(s):

J. Hartmann ◽

P. Trusheim ◽

H. Alkhatib ◽

J.-A. Paffenholz ◽

D. Diener ◽

...

Keyword(s):

Kalman Filter ◽

Point Cloud ◽

Laser Scanning ◽

Laser Scanner ◽

High Accuracy ◽

Laser Tracker ◽

3D Point Cloud ◽

Filter Model ◽

Measurement Frequency ◽

Set Up

Abstract. In recent years, the requirements in the industrial production, e.g., ships or planes, have been increased. In addition to high accuracy requirements with a standard deviation of 1mm, an efficient 3D object capturing is required. In terms of efficiency, kinematic laser scanning (k-TLS) has been proven its worth in recent years. It can be seen as an alternative to the well established static terrestrial laser scanning (s-TLS). However, current k-TLS based multi-sensor-systems (MSS) are not able to fulfil the high accuracy requirements. Thus, a new k-TLS based MSS and suitable processing algorithms have to be developed. In this contribution a new k-TLS based MSS will be presented. The main focus will lie on the (geo-)referencing process. Due to the high accuracy requirements, a novel procedure of external (geo-)referencing is used here. Hereby, a mobile platform, which is equipped with a profile laser scanner, will be tracked by a laser tracker. Due to the fact that the measurement frequency of the laser scanner is significantly higher than the measurement frequency of the laser tracker a direct point wise (geo-)referencing is not possible. To enable this a Kalman filter model is set up and implemented. In the prediction step each point is shifted according to the determined velocity of the platform. Because of the nonlinear motion of the platform an iterative extended Kalman filter (iEKF) is used here. Furthermore, test measurements of a panel with the k-TLS based MSS and with s-TLS were carried out. To compare the results, the 3D distances with the M3C2-algorithm between the s-TLS 3D point cloud and the k-TLS 3D point cloud are estimated. It can be noted, that the usage of a system model for the (geo-)referencing is essential. The results show that the mentioned high accuracy requirements have been achieved.

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