Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Metric scale and angle estimation in monocular visual odometry with multiple distance sensors
Date
2021-10-01
Author
Ölmez, Burhan
Tuncer, Temel Engin
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
573
views
0
downloads
Cite This
In this paper, a novel approach is presented to estimate the metric scale (MSC) and roll and pitch angles of a platform by using distance sensors in a monocular visual odometry setup. A state-of-the-art visual odometry algorithm Semi-Direct Visual Odometry (SVO) [1] is used to obtain sparse three dimensional (3D) point cloud which is then matched with the measurements obtained from the distance sensors for the estimation process. Metric scale with Kalman (MSCwK) filter approach is presented where the metric scale parameter is modeled as a Gaussian random variable and updated with a Kalman filter to improve robustness and accuracy. Maximum Likelihood (ML) method is presented to include multiple distance sensors for a better metric scale estimation. The estimation of the roll and pitch angles for the camera platform is considered. This is achieved with respect to the ground plane using at least three distance sensors placed in a specific geometry to overcome ambiguity and obtain a unique solution. Proposed approach can handle terrain irregularities and does not have drift. Several simulations are performed and the performances of the proposed approaches are compared with the previous works and SVO. The experiments also include real data to show the practical relevance. It is shown that the proposed approaches improve both the metric scale and roll and pitch angles significantly.
Subject Keywords
3D point cloud
,
Angle estimation
,
Monocular visual odometry
,
Scale estimation
,
Visual navigation
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108778262&origin=inward
https://hdl.handle.net/11511/91251
Journal
Digital Signal Processing: A Review Journal
DOI
https://doi.org/10.1016/j.dsp.2021.103148
Collections
Department of Electrical and Electronics Engineering, Article
Suggestions
OpenMETU
Core
Metric scale and 6dof pose estimation using a color camera and distance sensors
Ölmez, Burhan; Tuncer, Temel Engin; Department of Electrical and Electronics Engineering (2021-2-26)
Monocular color cameras are widely used for 6DoF pose estimation and sparse creation of 3D point cloud of the environment over decades with SfM, VO, and V-SLAM algorithms. In this thesis, a novel algorithm is presented to estimate the metric scale information of a monocular visual odometry algorithm using a distance sensor. This method uses a state-of-the-art visual odometry algorithm Semi-Direct Visual Odometry (SVO) [1] for obtaining sparse 3D point cloud and then matches these points with the measurement...
Differential Sensitivity Analysis for the Orthorectification of Small Satellite Images
Bettemir, Oe. H. (2009-06-13)
By using differential sensitivity analysis, horizontal and vertical accuracy of orthorectification of monoscopic images taken by small satellites without using Ground Control Points (GCP) is predicted. The analysis is performed by differentiating the colinearity equation of orthorectification procedure with respect to the satellite's interior and exterior parameters, and elevation obtained from digital elevation model (DEM). Square of the differential equations with respect to parameters are multiplied with...
Robust Attitude Estimation Using IMU-Only Measurements
Candan, Batu; Söken, Halil Ersin (2021-01-01)
© 1963-2012 IEEE.This article proposes two novel covariance-tuning methods to form a robust Kalman filter (RKF) algorithm for attitude (i.e., roll and pitch) estimation using the measurements of only an inertial measurement unit (IMU). KF-based and complementary filtering (CF)-based approaches are the two common methods for solving the attitude estimation problem. Efficiency and optimality of the KF-based attitude filters are correlated with appropriate tuning of the covariance matrices. Manual tuning proce...
Uniform and Non-Uniform V-shaped Arrays for 2-D DOA Estimation
Filik, Tansu; Tuncer, Temel Engin; Yasar, T. Kaya (2008-04-22)
In this study, a new method for optimum design of uniform and non-uniform V-shaped arrays is presented for azimuth and elevation angle estimation. The proposed design method finds an optimum angle between the linear sub-arrays of the V-array by using the Cramer-Rao Bound (CRB) where statistical coupling effect between azimuth and elevation angle estimation is considered This method can be used to obtain directional and isotropic angle performances for uniform and non-uniform V-arrays. For non-uniform isotro...
Design of V-shaped array geometry
Filik, Tansu; Tuncer, Temel Engin; Yasar, T. Kaya (2007-06-13)
in this study, a new design method is proposed for V-shaped planar array geometry for two-dimensional azimuth and elevation angle estimation. The proposed method is similar to filter design in signal processing. This method determines the V-shaped array geometry which gives the best 2-D angle estimation performances for the specified design parameters. The best geometry is determined by using Cramer-Rao Bound (CRB) and performance comparison is done with other planar array geometries.
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
B. Ölmez and T. E. Tuncer, “Metric scale and angle estimation in monocular visual odometry with multiple distance sensors,”
Digital Signal Processing: A Review Journal
, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108778262&origin=inward.