SlideShare una empresa de Scribd logo
1 de 20
Introduction to RoboticsLocalization and Mapping I March 1, 2010
Last week’s exercise RobotStadium Introduction to SVN (versioning/collaboration tool) Tasks Locomotion: need to get  up and run Perception: need to orient itself Communicate: need to share information Localize: need to reason about space Deliberate: need to plan what to do next Share the load: 1 or 2 tasks per student Present your plan in 2 weeks in class – be specific
Localization
Localization Gyroscope Odometry Control input GPS Landmarks Sensor input with different uncertainties.  What is the overall uncertainty of the estimate?
Uncertainty Models: The Gaussian Distribution
Error Propagation Intuition: the more sensitive the estimated quantity is to perception error, the more this sensor should be weighted Covariance matrix Representing output uncertainties Function relating sensor input to output quantities Covariance matrix representing input uncertainties
Differential Wheel Robot Odometry
Error propagation Wheel-Slip f=
Error Propagation
Belief representation Parametric, single hypothesis Parametric, multi hypothesis Non-parametric, multi hypothesis(particle filter)
Environment Representation Continuous Discrete Topological Vectors Array Graph
Example: Google Maps Continuous, Discrete or Topological?
Belief representation in topological maps
Multi-Hypothesis Belief Representation
From Sensor Data to Topological Maps Exact Decomposition
Voronoi Decomposition Points on lines have the same distance to neighboring obstacles Voronoi edges correspond to the safest path
Adaptive Cell-Size
Exercise: Navigation Algorithms Find the shortest path from A to B Choose the map representation Devise an algorithm to extract path
Reactive vs. Deliberative Planning So far Move randomly Use heuristics (follow wall, spiral, …) Use landmarks (infrared beacons, magnet wire) Use gradients / feedback control (Exercise 2) Today Deliberative planning Reason on abstract representation
Homework Section 5.6 (pages 212-244)

Más contenido relacionado

Destacado

Enterprise and Data Mining Ontology Integration to Extract Actionable Knowled...
Enterprise and Data Mining Ontology Integration to Extract Actionable Knowled...Enterprise and Data Mining Ontology Integration to Extract Actionable Knowled...
Enterprise and Data Mining Ontology Integration to Extract Actionable Knowled...
hamidnazary2002
 
Pal gov.tutorial2.session13 2.gav and lav integration
Pal gov.tutorial2.session13 2.gav and lav integrationPal gov.tutorial2.session13 2.gav and lav integration
Pal gov.tutorial2.session13 2.gav and lav integration
Mustafa Jarrar
 
[ABDO] Data Integration
[ABDO] Data Integration[ABDO] Data Integration
[ABDO] Data Integration
Carles Farré
 
DSBW Final Exam (Spring Sementer 2010)
DSBW Final Exam (Spring Sementer 2010)DSBW Final Exam (Spring Sementer 2010)
DSBW Final Exam (Spring Sementer 2010)
Carles Farré
 
Distributed databases and dbm ss
Distributed databases and dbm ssDistributed databases and dbm ss
Distributed databases and dbm ss
Mohd Arif
 
Database , 17 Web
Database , 17 WebDatabase , 17 Web
Database , 17 Web
Ali Usman
 
Database , 15 Object DBMS
Database , 15 Object DBMSDatabase , 15 Object DBMS
Database , 15 Object DBMS
Ali Usman
 
Database ,2 Background
 Database ,2 Background Database ,2 Background
Database ,2 Background
Ali Usman
 
Database ,18 Current Issues
Database ,18 Current IssuesDatabase ,18 Current Issues
Database ,18 Current Issues
Ali Usman
 
Database , 4 Data Integration
Database , 4 Data IntegrationDatabase , 4 Data Integration
Database , 4 Data Integration
Ali Usman
 
Web Usability (Slideshare Version)
Web Usability (Slideshare Version)Web Usability (Slideshare Version)
Web Usability (Slideshare Version)
Carles Farré
 

Destacado (20)

8 ontology integration and interoperability (onto i op)
8 ontology integration and interoperability (onto i op)8 ontology integration and interoperability (onto i op)
8 ontology integration and interoperability (onto i op)
 
Ontology integration - Heterogeneity, Techniques and more
Ontology integration - Heterogeneity, Techniques and moreOntology integration - Heterogeneity, Techniques and more
Ontology integration - Heterogeneity, Techniques and more
 
Enterprise and Data Mining Ontology Integration to Extract Actionable Knowled...
Enterprise and Data Mining Ontology Integration to Extract Actionable Knowled...Enterprise and Data Mining Ontology Integration to Extract Actionable Knowled...
Enterprise and Data Mining Ontology Integration to Extract Actionable Knowled...
 
Pal gov.tutorial2.session13 2.gav and lav integration
Pal gov.tutorial2.session13 2.gav and lav integrationPal gov.tutorial2.session13 2.gav and lav integration
Pal gov.tutorial2.session13 2.gav and lav integration
 
[ABDO] Data Integration
[ABDO] Data Integration[ABDO] Data Integration
[ABDO] Data Integration
 
DSBW Final Exam (Spring Sementer 2010)
DSBW Final Exam (Spring Sementer 2010)DSBW Final Exam (Spring Sementer 2010)
DSBW Final Exam (Spring Sementer 2010)
 
Distributed databases and dbm ss
Distributed databases and dbm ssDistributed databases and dbm ss
Distributed databases and dbm ss
 
Lecture 09: Localization and Mapping III
Lecture 09: Localization and Mapping IIILecture 09: Localization and Mapping III
Lecture 09: Localization and Mapping III
 
Jarrar: Data Schema Integration
Jarrar: Data Schema Integration Jarrar: Data Schema Integration
Jarrar: Data Schema Integration
 
1 ddbms jan 2011_u
1 ddbms jan 2011_u1 ddbms jan 2011_u
1 ddbms jan 2011_u
 
Database , 17 Web
Database , 17 WebDatabase , 17 Web
Database , 17 Web
 
How to design a linear control system
How to design a linear control systemHow to design a linear control system
How to design a linear control system
 
Ontology-based Data Integration
Ontology-based Data IntegrationOntology-based Data Integration
Ontology-based Data Integration
 
Database , 15 Object DBMS
Database , 15 Object DBMSDatabase , 15 Object DBMS
Database , 15 Object DBMS
 
Database ,2 Background
 Database ,2 Background Database ,2 Background
Database ,2 Background
 
Database ,18 Current Issues
Database ,18 Current IssuesDatabase ,18 Current Issues
Database ,18 Current Issues
 
Database , 4 Data Integration
Database , 4 Data IntegrationDatabase , 4 Data Integration
Database , 4 Data Integration
 
Semi join
Semi joinSemi join
Semi join
 
Web Usability (Slideshare Version)
Web Usability (Slideshare Version)Web Usability (Slideshare Version)
Web Usability (Slideshare Version)
 
A8: Mind Mapping for Effective Content Management
A8: Mind Mapping for Effective Content ManagementA8: Mind Mapping for Effective Content Management
A8: Mind Mapping for Effective Content Management
 

Más de University of Colorado at Boulder

Más de University of Colorado at Boulder (20)

Template classes and ROS messages
Template classes and ROS messagesTemplate classes and ROS messages
Template classes and ROS messages
 
NLP for Robotics
NLP for RoboticsNLP for Robotics
NLP for Robotics
 
Indoor Localization Systems
Indoor Localization SystemsIndoor Localization Systems
Indoor Localization Systems
 
Vishal Verma: Rapidly Exploring Random Trees
Vishal Verma: Rapidly Exploring Random TreesVishal Verma: Rapidly Exploring Random Trees
Vishal Verma: Rapidly Exploring Random Trees
 
Lecture 10: Summary
Lecture 10: SummaryLecture 10: Summary
Lecture 10: Summary
 
Lecture 09: SLAM
Lecture 09: SLAMLecture 09: SLAM
Lecture 09: SLAM
 
Lecture 08: Localization and Mapping II
Lecture 08: Localization and Mapping IILecture 08: Localization and Mapping II
Lecture 08: Localization and Mapping II
 
Lecture 07: Localization and Mapping I
Lecture 07: Localization and Mapping ILecture 07: Localization and Mapping I
Lecture 07: Localization and Mapping I
 
Lecture 06: Features and Uncertainty
Lecture 06: Features and UncertaintyLecture 06: Features and Uncertainty
Lecture 06: Features and Uncertainty
 
Lecture 05
Lecture 05Lecture 05
Lecture 05
 
Lecture 04
Lecture 04Lecture 04
Lecture 04
 
Lecture 02: Locomotion
Lecture 02: LocomotionLecture 02: Locomotion
Lecture 02: Locomotion
 
Lecture 01
Lecture 01Lecture 01
Lecture 01
 
Lectures 11+12: Debates
Lectures 11+12: DebatesLectures 11+12: Debates
Lectures 11+12: Debates
 
Lecture 10: Navigation
Lecture 10: NavigationLecture 10: Navigation
Lecture 10: Navigation
 
Lecture 06: Features
Lecture 06: FeaturesLecture 06: Features
Lecture 06: Features
 
Lecture 05: Vision
Lecture 05: VisionLecture 05: Vision
Lecture 05: Vision
 
Lecture 04: Sensors
Lecture 04: SensorsLecture 04: Sensors
Lecture 04: Sensors
 
Lecture 03: Kinematics
Lecture 03: KinematicsLecture 03: Kinematics
Lecture 03: Kinematics
 
Lecture 02: Locomotion
Lecture 02: LocomotionLecture 02: Locomotion
Lecture 02: Locomotion
 

Último

Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
Joaquim Jorge
 

Último (20)

Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
HTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation StrategiesHTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation Strategies
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdfGenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
 
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 

Lecture 07: Localization and Mapping I