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 6dof pose estimation using a color camera and distance sensors
Download
12626132.pdf
Date
2021-2-26
Author
Ölmez, Burhan
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
505
views
647
downloads
Cite This
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 measurements obtained from the distance sensor to estimate the metric scale. Moreover, the scale parameter is modeled as a Gaussian random variable and updated with the calculated scale using a Kalman filter for a more stable result. Additionally, multiple distance sensors are added to estimate the scale more accurately. It is observed that the scale accuracy can significantly be improved in the case of multiple sensors. As another novel approach, the estimation of the roll and pitch angles for the camera platform is considered. This is achieved with respect to the ground plane using three distance sensors placed with a specific geometry and their corresponding 3D point cloud matches. This angle information does not drift in time thanks to direct metric measurements from distance sensors. Finally, with four special distance senv sors, which can leave marks on the environment, direct 6DoF pose estimation with respect to the pattern is found. A novel heuristic pattern and pattern recognition algorithm are proposed. Several simulations are performed on a MAV equipped with a camera and distance sensors in an advanced SITL environment and the performance of the proposed approaches are shown to be better than the previous works in different scenarios.
Subject Keywords
Visual odometry
,
3D point cloud
,
Scale estimation
,
6DoF Ppse estimation
URI
https://hdl.handle.net/11511/89633
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Metric scale and angle estimation in monocular visual odometry with multiple distance sensors
Ölmez, Burhan; Tuncer, Temel Engin (2021-10-01)
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...
MULTI-RESOLUTION MOTION ESTIMATION FOR MOTION COMPENSATED FRAME INTERPOLATION
Guenyel, Bertan; Alatan, Abdullah Aydın (2010-09-29)
A multi-resolution motion estimation scheme is proposed for tracking of the true 2D motion in video sequences for motion compensated image interpolation. The proposed algorithm utilizes frames with different resolutions and adaptive block dimensions for efficient representation of motion. Firstly, motion vectors for each block are assigned as a result of predictive search in each pass. Then, the outlier motion vectors are detected and corrected at the end of each pass. Simulation results with respect to dif...
Correlation tracking based on wavelet domain information
Ipek, HL; Yilmaz, I; Yardimci, YC; Cetin, AE (2003-08-07)
Tracking moving objects in video can be carried out by correlating a template containing object pixels with pixels of the current frame. This approach may produce erroneous results under noise. We determine a set of significant pixels on the object by analyzing the wavelet transform of the template and correlate only these pixels with the current frame to determine the next position of the object. These significant pixels are easily trackable features of the image and increase the performance of the tracker.
GIBBS RANDOM FIELD MODEL BASED 3-D MOTION ESTIMATION BY WEAKENED RIGIDITY
Alatan, Abdullah Aydın (1994-01-01)
3-D motion estimation from a video sequence remains a challenging problem. Modelling the local interactions between the 3-D motion parameters is possible by using Gibbs random fields. An energy function which gives the joint probability distribution of the motion vectors, is constructed. The most probable motion vector set is found by maximizing the probability, represented by this distribution. Since the 3-D motion estimation problem is ill-posed, the regularization is achieved by an initial rigidity assum...
Multi-view structure-from-motion for hybrid camera scenarios
BAŞTANLAR, YALIN; Temizel, Alptekin; Yardimci, Y.; Sturm, P. (2012-08-01)
We describe a pipeline for structure-from-motion (SfM) with mixed camera types, namely omnidirectional and perspective cameras. For the steps of this pipeline, we propose new approaches or adapt the existing perspective camera methods to make the pipeline effective and automatic. We model our cameras of different types with the sphere camera model. To match feature points, we describe a preprocessing algorithm which significantly increases scale invariant feature transform (SIFT) matching performance for hy...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
B. Ölmez, “Metric scale and 6dof pose estimation using a color camera and distance sensors,” M.S. - Master of Science, Middle East Technical University, 2021.