Point sets, Maps and Navigation - II D.A. Forsyth Robustness is a - PowerPoint PPT Presentation

Oct 19, 2022 •33 likes •153 views

Point sets, Maps and Navigation - II D.A. Forsyth Robustness is a serious problem Robustness is a serious problem Key issue: Squaring a large number produces a huge number A few wildly mismatch points can throw off R, t Fixes:

Point sets, Maps and Navigation - II D.A. Forsyth
Robustness is a serious problem
Robustness is a serious problem
Key issue: • Squaring a large number produces a huge number • A few wildly mismatch points can throw off R, t • Fixes: • remove matches with “large” distances • actually, quite good • but what happens if new such pairs emerge? • apply an M-estimator • deals with new pairs You should have watched the IRLS movie for regression by now

Download Presentation

Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Point sets, Maps and Navigation D.A. Forsyth Issues Where am I? Simplest: register

Point sets, Maps and Navigation D.A. Forsyth Issues Where am I? Simplest: register observations and motion to a map correspondence and robustness Build a map Register observations to one another global consistency

513 views • 26 slides

Point sets, Maps and Navigation - III D.A. Forsyth Localization We can now robustly register

Point sets, Maps and Navigation - III D.A. Forsyth Localization We can now robustly register a point set to a point set This is localization Look at LIDAR register to map I know where I am Q: how to make the map?

298 views • 7 slides

Maps, Sets and Lists CS 2334: Lab 5 Collections, Maps, Sets and Lists in Java The abstract

Maps, Sets and Lists CS 2334: Lab 5 Collections, Maps, Sets and Lists in Java The abstract concepts of collections, maps, sets and lists are (or should be) easy to understand But: There are many different ways to implement these

332 views • 10 slides

Point sets in AG ( n , q ) (not) determining certain directions Jan De Beule Department of

Introduction point sets of AG ( 2 , q ) point sets in AG ( n , q ) Point sets in AG ( n , q ) (not) determining certain directions Jan De Beule Department of Mathematics Ghent University June 12, 2010 Baer Colloquium Summer 2010, Gent

254 views • 22 slides

Building Java Programs Chapter 11 Sets and Maps reading: 11.2 - 11.3 2 Sets (11.2) set : A

Building Java Programs Chapter 11 Sets and Maps reading: 11.2 - 11.3 2 Sets (11.2) set : A collection of unique values (no duplicates allowed) that can perform the following operations efficiently: add, remove, search (contains) We

74 views • 6 slides

On Path-Centric Navigation and Search Techniques for Personal Knowledge Stored in Topic Maps

On Path-Centric Navigation and Search Techniques for Personal Knowledge Stored in Topic Maps Outline Introduction Preconditions and the Model Navigation inside Topic Maps Search based on Topic Selection 2 Jens Heider TMRA 07

557 views • 29 slides

Sets and Maps Department of Computer Science University of Maryland, College Park Sets

CMSC 132: Object-Oriented Programming II Sets and Maps Department of Computer Science University of Maryland, College Park Sets Properties Set A Collection of elements without duplicates No ordering (i.e., no front or back)

453 views • 11 slides

How to proceed from here ... The process up to this point The idea behind the concept

e-Navigation underway 2013 e-Navigation How to proceed from here ... The process up to this point The idea behind the concept emerged around 2005 A strategy for the development and implementation of e-Navigation was approved by

132 views • 9 slides

Point Location Geometry Motivation Point Location and Trapezoidal Maps A Randomized

Yazd Univ. Computational Point Location Geometry Motivation Point Location and Trapezoidal Maps A Randomized Incremental 1397-2 Algorithm Search Structure 1 / 38 Motivation: Point location in a map Point location query: Given a map and

1.18k views • 88 slides

Linear-size Universal Point Sets for One-Bend Drawings Csaba D. T oth Maarten L offler

Linear-size Universal Point Sets for One-Bend Drawings Csaba D. T oth Maarten L offler Utrecht University Cal State Northridge Utrecht, The Netherlands Los Angeles, CA, USA Universal point sets Universal point sets Def.: A point set S

805 views • 56 slides

Sets, Maps, Bit Sets Section 2.3 Dr. Mayfield and Dr. Lam Department of Computer Science James

Sets, Maps, Bit Sets Section 2.3 Dr. Mayfield and Dr. Lam Department of Computer Science James Madison University Oct 02, 2015 C/C++ pointers For those unfamiliar with pointers . . . int i; // integer int *p; // integer pointer p =

413 views • 15 slides

Autonomous Integrity Monitoring of Navigation Maps on board Vehicles Philippe Bonnifait

Autonomous Integrity Monitoring of Navigation Maps on board Vehicles Philippe Bonnifait Professor at the Universit de Technologie de Compigne Heudiasyc UMR 7253 CNRS, France In collaboration with Clment Zinoune and Javier Ibanez-Guzman

1.05k views • 65 slides

Predication: High- Performance Concurrent Sets and Maps for STM Nathan G. Bronson, Jared Casper,

Transactional Predication: High- Performance Concurrent Sets and Maps for STM Nathan G. Bronson, Jared Casper, Hassan Chafi, Kunle Olukotun Stanford CS PODC - 26 July 2010 1 Thread-safe shared maps transactional map + atomic block map +

295 views • 25 slides

No Smooth Julia Sets for Complex H enon Maps Eric Bedford Stony Brook U. joint with John

No Smooth Julia Sets for Complex H enon Maps Eric Bedford Stony Brook U. joint with John Smillie and Kyounghee Kim Dynamics of invertible polynomial maps of C 2 If we want invertible polynomial maps, we must move to dimension 2. One

541 views • 36 slides

Session overview Complex maps and Julia sets Reminder: project topics and teams due now

Session overview Complex maps and Julia sets Reminder: project topics and teams due now on Angel. May 1, 2008 CSSE/MA 325 Lecture #28 1 Complex maps Consider the dynamical system F( z ) = z 2 , where z is a complex number

419 views • 25 slides

Robot Navigation with Model Predictive Equilibrium Point Control (MPEPC) Jong Jin Park, Collin

Robot Navigation with Model Predictive Equilibrium Point Control (MPEPC) Jong Jin Park, Collin Johnson and Benjamin Kuipers University of Michigan, USA 1 Robot Navigation Faces Dynamic and Uncertain Environments Tight rectilinear spaces

513 views • 29 slides

Motion Estimation (I) Ce Liu celiu@microsoft.com Microsoft Research New England We live in a

Motion Estimation (I) Ce Liu celiu@microsoft.com Microsoft Research New England We live in a moving world Perceiving, understanding and predicting motion is an important part of our daily lives Motion estimation: a core problem of

643 views • 62 slides

Gaussian Processes Seung-Hoon Na Chonbuk National University Gaussian Process Regression

Gaussian Processes Seung-Hoon Na Chonbuk National University Gaussian Process Regression Predictions using noisy observations The case of a single test input: Gaussian Process Regression Computational and numerical issues It is

444 views • 29 slides

Enzyme Inhibitors Petr Kuzmi , Ph.D. BioKin, Ltd. TOPICS: 1. Fitting model : Four-parameter

Society for Biomolecular Screening 10th Annual Conference, Orlando, FL, September 11-15, 2004 Advanced Methods in Dose-Response Screening of Enzyme Inhibitors Petr Kuzmi , Ph.D. BioKin, Ltd. TOPICS: 1. Fitting model : Four-parameter logistic

251 views • 12 slides

Certificate in Digital Information Management: A Cross-Disciplinary Functional Approach Bruce

Certificate in Digital Information Management: A Cross-Disciplinary Functional Approach Bruce Fulton Acting Program Manager Communications and Outreach Librarian School of Information Resources and Library Science University of Arizona

555 views • 19 slides

Generalized Linear Models (GLIMs) Probabilistic Graphical Models Sharif University of Technology

Exponential family & Generalized Linear Models (GLIMs) Probabilistic Graphical Models Sharif University of Technology Spring 2018 Soleymani Outline Exponential family Many standard distributions are in this family Similarities

661 views • 27 slides

Inverse problems with L 1 data fitting Christian Clason, Bangti JIN, Karl Kunisch Institute for

Mathematical Optimization and INSTITUTE OF MATHEMATICS Applications in Biomedical Sciences AND SCIENTIFIC COMPUTING Inverse problems with L 1 data fitting Christian Clason, Bangti JIN, Karl Kunisch Institute for Mathematics and Scientific

876 views • 40 slides

Lecture 9: Nave Bayes Classifier (contd.) Logistic Regression Discriminative vs.

Lecture 9: Nave Bayes Classifier (contd.) Logistic Regression Discriminative vs. Generative Classification Linear Discriminant Functions Aykut Erdem October 2016 Hacettepe University Last time Nave Bayes Classifier

822 views • 63 slides

Reference Classes Lee Edlefsen, Ph.D. Chief Scientist Sue Ranney, Ph.D. Chief Data Scientist

Parallel External Memory Algorithms Using Reference Classes Lee Edlefsen, Ph.D. Chief Scientist Sue Ranney, Ph.D. Chief Data Scientist Prepared for DSC 2014, June 2014 1 Introduction In many fields, the size of data sets is increasing

285 views • 24 slides