Comparison of local plane fitting methods for range data

Wang, Caihua; Tanahashi, Hideki; Hirayu, Hidekazu; Niwa, Yasumasa; Yamamoto, Kazuhiko

doi:10.1109/cvpr.2001.990538

Cited by 18 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The essence of the plane fitting of point cloud data is to find a fitting plane to approximately replace the selected points, and the existing plane fitting algorithms are least squares, z-function fitting, the eigenvalue method, maximum likelihood estimation method, etc. [26]. This paper is mainly based on the least squares method for plane fitting to minimize the sum of the squares of the distances from all points in the point cloud to the fitting plane.…”

Section: Constructing a Complete Point Cloud 231 Fitting The Bottom S...mentioning

confidence: 99%

Automated Determination of the Volume of Loose Engineering Deposits Using Terrestrial Laser Scanning

Lu,

Zhu,

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

A new method for automatic volume determination of loose engineering deposits (LEDs) using point clouds collected by terrestrial laser scanning is proposed. The method starts with a problem of lacking bottom surface point clouds when scanning LEDs, assuming that the bottom surface is flat, a spatial plane is generated based on the plane fitting to the bottom surface. Then the sample point cloud is projected on this fitted plane to construct the subface point clouds, which, together with the initial point cloud data, are stitched into complete point cloud data with good closure, integrity, and accuracy. The 3D surface model established by introducing the alpha shape algorithm calculates the volume, and the validation experiments on three soil mound models and one field experiment on LEDs with a minimum average error of only 1.69% fully validate the effectiveness and accuracy of the method. The encouraging results show that effective volume calculation can be performed on realistic LEDs using the proposed method.

show abstract

Section: Constructing a Complete Point Cloud 231 Fitting The Bottom S...mentioning

confidence: 99%

Automated Determination of the Volume of Loose Engineering Deposits Using Terrestrial Laser Scanning

Lu,

Zhu,

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The fitting result might be different, when the evaluation criteria or optim ization objective function is different. The common plane fitting methods include least square method (LSM) [22], algebraic least square method (ALSM) [23], re-normalization method (RM) [24], maximum likelihood method (MLM) [25].…”

Section: Calculation Of the Planementioning

confidence: 99%

Assembly accuracy analysis for small components with a planar surface in large-scale metrology

Wang

Huang

et al. 2016

Meas. Sci. Technol.

View full text Add to dashboard Cite

Large complex mechanical products such as aircraft, rockets and submarines are composed of millions of different sized components. For example, in large-scale aircraft assembly, the length and width of large components are in the range of 10 ~ 100 m, but the boundary dimension of small components installed on the aircraft, such as inertial navigation system (INS) and head up display (HUD) system, is only in the range of 200-400 mm. The high-quality standard of the aerospace industry requires the position accuracy of small components to be kept within a tolerance of ±0.3 mm, which in turn places a stringent requirement on the measurement accuracy of the measurement system integrated on the aircraft assembly system. In this paper, the assembly accuracy of small comp onents with a planar surface in the digital assembly system is investigated since the planar surface is usually taken as the key product characteristic to evaluate the assembly accuracy of the small components.Digital assembly technology has been widely applied in aircraft assembly systems [1,2]. Naing studies the capabilities of jigless methodologies and principles with an automatic

show abstract

“…Given a set of noisy points known or hypothesized to lie on a plane, the "optimum" plane can be extracted from them using methods surveyed in [17]. The main methods are that of least-squares [17,19] and renormalization [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…The main methods are that of least-squares [17,19] and renormalization [4,5]. The latter uses a more detailed error-model for the 3D points which results in better estimations with respect to the assumed model.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis for optimum plane extraction from noisy 3D range-sensor point-clouds

Pathak

Vaškevičius

Birk

2009

Intel Serv Robotics

View full text Add to dashboard Cite

We utilize a more accurate range noise model for 3D sensors to derive from scratch the expressions for the optimum plane fitting a set of noisy points and for the combined covariance matrix of the plane's parameters, viz. its normal and its distance to the origin. The range error model used by us is a quadratic function of the true range and also the incidence angle. Closed-form expressions for the Cramér-Rao uncertainty bound are derived and utilized for analyzing four methods of covariance computation: exact maximum likelihood, renormalization, approximate least-squares, and eigenvector perturbation. The effect of the simplifying assumptions inherent in these methods are compared with respect to accuracy, speed, and ease of interpretation of terms. The approximate least-squares covariance matrix is shown to possess a number of desirable properties, e.g., the optimal solution forms its null-space and its components are functions of easily understood terms like the planar-patch's weighted centroid and scatter. It is also fast to compute and accurate enough in practice. Its experimental application to real-time range-image registration and plane fusion is shown by using a commercially available 3D range sensor.

show abstract

Comparison of local plane fitting methods for range data

Cited by 18 publications

References 10 publications

Automated Determination of the Volume of Loose Engineering Deposits Using Terrestrial Laser Scanning

Automated Determination of the Volume of Loose Engineering Deposits Using Terrestrial Laser Scanning

Assembly accuracy analysis for small components with a planar surface in large-scale metrology

Uncertainty analysis for optimum plane extraction from noisy 3D range-sensor point-clouds

Contact Info

Product

Resources

About