Homework | Reprojection Error

Slides:



Advertisements
Similar presentations
Biomedical Signal Processing
Advertisements

Principles of the Global Positioning System Lecture 12 Prof. Thomas Herring Room A;
E(X 2 ) = Var (X) = E(X 2 ) – [E(X)] 2 E(X) = The Mean and Variance of a Continuous Random Variable In order to calculate the mean or expected value of.
Use of Kalman filters in time and frequency analysis John Davis 1st May 2011.
Microspectrophotometry Validation. Reasons for Changing Instruments Reduced reliability. Limited efficiency. Limited availability and cost of replacement.
Statistical properties of Random time series (“noise”)
Analyzing the Results of a Simulation and Estimating Errors Jason Cooper.
Nov 4, Detection, Classification and Tracking of Targets in Distributed Sensor Networks Presented by: Prabal Dutta Dan Li, Kerry Wong,
HMM-BASED PATTERN DETECTION. Outline  Markov Process  Hidden Markov Models Elements Basic Problems Evaluation Optimization Training Implementation 2-D.
3D Position Determination Hasti AhleHagh Professor. W.R. Michalson.
Adam Rachmielowski 615 Project: Real-time monocular vision-based SLAM.
Probabilistic video stabilization using Kalman filtering and mosaicking.
1 Adaptive computer-based spatial -filtering method for more accurate estimation of the surface velocity of debris flow APPLIED OPTICS M. Shorif, Hiroyuki.
Lecture 4 Measurement Accuracy and Statistical Variation.
Chapter 7. Random Process – Spectral Characteristics
(1) A probability model respecting those covariance observations: Gaussian Maximum entropy probability distribution for a given covariance observation.
8/9/2015 Frequency Domain Methods. Time domain worldFrequency domain worldFourier Transform: F Inverse Fourier Transform: F --1 Oscilloscope Spectrum.
Inputs to Signal Generation.vi: -Initial Distance (m) -Velocity (m/s) -Chirp Duration (s) -Sampling Info (Sampling Frequency, Window Size) -Original Signal.
EE513 Audio Signals and Systems Statistical Pattern Classification Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.
Special Relativity Quiz 9.4 and a few comments on quiz 8.24.
3D SLAM for Omni-directional Camera
1 Part 5 Response of Linear Systems 6.Linear Filtering of a Random Signals 7.Power Spectrum Analysis 8.Linear Estimation and Prediction Filters 9.Mean-Square.
Speech Enhancement Using Spectral Subtraction
1 Workshop First look, calibrations, reference sources IAP – 24 November CU6 structure 2.Aims of the workshop / open questions.
Methods Validation with Simulated Data 1.Generate random linear objects in the model coordinate system. 2.Generate a random set of points on each linear.
MultiModality Registration Using Hilbert-Schmidt Estimators By: Srinivas Peddi Computer Integrated Surgery II April 27 th, 2001 Final Presentation.
Automatic Joint Parameter Estimation from Magnetic Motion CaptureData James F.O”Brien Robert E. Bodenheimer Gabriel J Brostow Jessica K. Hodgins Presented.
Validating an Access Cost Model for Wide Area Applications Louiqa Raschid University of Maryland CoopIS 2001 Co-authors V. Zadorozhny, T. Zhan and L. Bright.
A Passive Approach to Sensor Network Localization Rahul Biswas and Sebastian Thrun International Conference on Intelligent Robots and Systems 2004 Presented.
Example: Bioassay experiment Problem statement –Observations: At each level of dose, 5 animals are tested, and number of death are observed.
Principles of the Global Positioning System Lecture 12 Prof. Thomas Herring Room ;
Z bigniew Leonowicz, Wroclaw University of Technology Z bigniew Leonowicz, Wroclaw University of Technology, Poland XXIX  IC-SPETO.
Dr. Galal Nadim.  The root-MUltiple SIgnal Classification (root- MUSIC) super resolution algorithm is used for indoor channel characterization (estimate.
DFT Applications Technology to calculate observables Global properties Spectroscopy DFT Solvers Functional form Functional optimization Estimation of theoretical.
MONALISA: The precision of absolute distance interferometry measurements Matthew Warden, Paul Coe, David Urner, Armin Reichold Photon 08, Edinburgh.
Error Modeling Thomas Herring Room ;
1 MONALISA Compact Straightness Monitor Simulation and Calibration Week 8 Report By Patrick Gloster.
MultiModality Registration Using Hilbert-Schmidt Estimators By: Srinivas Peddi Computer Integrated Surgery II April 6 th, 2001.
Comparison of Image Registration Methods David Grimm Joseph Handfield Mahnaz Mohammadi Yushan Zhu March 18, 2004.
Genetic algorithms: A Stochastic Approach for Improving the Current Cadastre Accuracies Anna Shnaidman Uri Shoshani Yerach Doytsher Mapping and Geo-Information.
Colorado Center for Astrodynamics Research The University of Colorado 1 STATISTICAL ORBIT DETERMINATION Kalman Filter with Process Noise Gauss- Markov.
SYSTEMS Identification Ali Karimpour Assistant Professor Ferdowsi University of Mashhad Reference: “System Identification Theory.
Copyright 2011 controltrix corpwww. controltrix.com Global Positioning System ++ Improved GPS using sensor data fusion
Introduction to Medical Imaging Regis Introduction to Medical Imaging Registration Alexandre Kassel Course
EE 495 Modern Navigation Systems
Date of download: 6/22/2016 Copyright © 2016 SPIE. All rights reserved. Fixed pattern noise (FPN) versus blackbody temperature using the two-point algorithm.
Sensor Error Characteristics By: Hector Rotstein.
Date of download: 7/7/2016 Copyright © ASME. All rights reserved. From: On the Observability of Loosely Coupled Global Positioning System/Inertial Navigation.
Date of download: 7/11/2016 Copyright © 2016 SPIE. All rights reserved. Relationship among the intrinsic parameters and the rotation angle; *, the results.
Optimization of Monte Carlo Integration
Probability Theory and Parameter Estimation I
Using Sensor Data Effectively
Lecture 19 Figures from Gonzalez and Woods, Digital Image Processing, Second Edition, 2002.
Department of Civil and Environmental Engineering
Velocity Estimation from noisy Measurements
ECE 539 Project Aditya Ghule
Self-similar Distributions
Identifying Confusion from Eye-Tracking Data
Homework Assignment 1: Use the following data set to test the performance difference of three clustering algorithms: K-means, AP clustering and Spectral.
Optimization with Meta-Heuristics
UWB Receiver Design Simplification through Channel Shortening
EE513 Audio Signals and Systems
Correlation, Energy Spectral Density and Power Spectral Density
Fixed-point Analysis of Digital Filters
Precision, Accuracy, And Validity
Distributed & Scalable IMU
2011 International Geoscience & Remote Sensing Symposium
10.3 The Inverse z-Transform
Unit 1-2 Test Guide Matter Review Density
Probabilistic Surrogate Models
Presentation transcript:

Homework | Reprojection Error What is re-projection error? What is photometric error? Which parameters can be optimized to minimize the re-projection error? How does this differ from the optimization in bundle adjustment? What are the four coordinate frames associated with calculating re-projection error?

Implement a 2D scan registration algorithm and test using this data. Homework |Scan Registration Implement a 2D scan registration algorithm and test using this data. http://wavelab.uwaterloo.ca/slam/2017-SLAM/data/scans.mat

Homework |IMU Noise Characterization What are the definitions of these terms? Quantization Noise Angle / Velocity Random Walk Noise Correlated Noise Bias Instability Noise Rate / Acceleration Random Walk Noise Simulate an IMU using the standard noise model Plot Fourier Transform and Power Spectral Density of simulated IMU Extract the IMU Noise characteristics using Allan Variance

Discussion |Landmark Based VIO Discussion topics Algorithm choices often seem empirical Is there something to emulate here? Should we value KITTI benchmark results?

Discussion | Calibration Discussion topics Why is calibration so challenging? How do we evaluate calibration? How accurate do these have to be?