NDLI: Revisiting uncertainty analysis for optimum planes extracted from 3D range sensor point-clouds

Content Provider	IEEE Xplore Digital Library
Author	Pathak, K. Vaskevicius, N. Birk, A.
Copyright Year	2009
Description	Author affiliation: Dept. of Computer Science, Jacobs University Bremen, 28759, Germany (Pathak, K.; Vaskevicius, N.; Birk, A.)
Abstract	In this work, we utilize a recently studied more accurate range noise model for 3D sensors to derive from scratch the expressions for the optimum plane which best fits a point-cloud and for the combined covariance matrix of the plane's parameters. The parameters in question are the plane's normal and its distance to the origin. The range standard-deviation model used by us is a quadratic function of the true range and is a function of the incidence angle as well. We show that for this model, the maximum-likelihood plane is biased, whereas the least-squares plane is not. The plane-parameters' covariance matrix for the least-squares plane 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 center and scatter.We verify our covariance expression with that obtained by the eigenvector perturbation method. We further compare our method to that of renormalization with respect to the theoretically best covariance matrix in simulation. The application of our approach to real-time range-image registration and plane fusion is shown by an example using a commercially available 3D range sensor. Results show that our method has good accuracy, is fast to compute, and is easy to interpret intuitively.
Starting Page	1631
Ending Page	1636
File Size	1468655
Page Count	6
File Format	PDF
ISBN	9781424427888
ISSN	10504729
DOI	10.1109/ROBOT.2009.5152502
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-12
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Covariance matrix Maximum likelihood estimation Perturbation methods Robotics and automation Computer science Jacobian matrices Scattering Computational modeling Sensor fusion Plane Fusion Plane-fitting Plane uncertainty estimation 3D Mapping
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Revisiting Uncertainty Analysis for Optimum Planes Extracted from 3D

Revisiting uncertainty analysis for optimum planes extracted from 3d range sensor point-clouds.

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

Robust covariance estimation in sensor data fusion

Extraction of quadrics from noisy point-clouds using a sensor noise model

Robust maximum-likelihood estimation of structured covariance matrices

Locally optimal maximum-likelihood completion of a partially specified Toeplitz covariance matrix

Intelligent Sensor Modeling and Data Fusion via Neural Network and Maximum Likelihood Estimation

On persymmetric covariance matrices in adaptive detection

Revisiting uncertainty analysis for optimum planes extracted from 3D range sensor point-clouds

Similar Documents

Revisiting Uncertainty Analysis for Optimum Planes Extracted from 3D

Revisiting uncertainty analysis for optimum planes extracted from 3d range sensor point-clouds.

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

Robust covariance estimation in sensor data fusion

Extraction of quadrics from noisy point-clouds using a sensor noise model

Robust maximum-likelihood estimation of structured covariance matrices

Locally optimal maximum-likelihood completion of a partially specified Toeplitz covariance matrix

Intelligent Sensor Modeling and Data Fusion via Neural Network and Maximum Likelihood Estimation

On persymmetric covariance matrices in adaptive detection

Revisiting uncertainty analysis for optimum planes extracted from 3D range sensor point-clouds