Offline Simultaneous Localization and Mapping (SLAM) using Miniature Robots
|
|
- Jessie Cobb
- 6 years ago
- Views:
Transcription
1 Offline Simultaneous Localization and Mapping (SLAM) using Miniature Robots Objectives SLAM approaches SLAM for ALICE EKF for Navigation Mapping and Network Modeling Test results Philipp Schaer and Adrian Waegli June 29, 2007
2 ALICE -ROBOT Alice As cheap and small as possible 2 SWATCH motors with wheels and tires (max speed: 40mm/s) 4 active IR proximity sensors (reflection measurement) Microcontroller with 8Kwords Flash program memory Radio communication module Objectives 60 4cm 20mm 1/2 22mm
3 Test Environment - Centimeter range labyrinth Objectives 2/2
4 General SLAM Approaches Localization Kalman filter (EKF, UKF) Particle filter IA methods (Fuzzy logic, neural networks ) Mapping Grid maps Feature recognition / scan matches Tree-based network optimizers General SLAM Approaches
5 SLAM for ALICE EKF for navigation 3 approaches for mapping: Grid mapping Boundary detection based on range measurements Network reconstruction SLAM for ALICE 1/7
6 EKF for Navigation SLAM for ALICE 2/7
7 System Process Model x y θ y l r k+ 1 k k l r k+ 1 k k k+ 1 = x d + d + 2 = y d + d + 2 dl dr = θk + D D d l θ sinθ d r cosθ x Functional model: Stochastic model: P Q ww σ 2 x 2 = σ y 2 σ θ 2 σ dl = 2 σ dr dl + dr 1 0 cosθ 2 dl + dr F = 0 1 sinθ sinθ sinθ 2 2 cosθ cosθ Γ = D D SLAM for ALICE 3/7
8 Measurement Model ( ˆk ) h x y 0.5 x = y θ SLAM x 1 = y θ = 0.4 SLAM 0 θ = 1320 x k= y 0.5 k H pos H dir = = [ 0 0 1] R pos R dir 2 σ x = 2 σ y 2 = σ θ θslam = 90 θ = 110 k SLAM for ALICE 0 x 4/
9 Grid Modeling Shape recognition (PCA) SLAM for ALICE 5/7
10 Environment Modeling Robots programmed with follow & avoidance behavior Online calibration of the wheel data not feasible y SLAM for ALICE l x 6/7
11 Network Modeling Estimate network nodes and segments Appropriate for Labyrinth mapping Poor IR sensor performance (unusable for Navigation) Map matching for navigation SLAM for ALICE 7/7
12 Test Setup Construction of Labyrinth environment 3 Runs of ca 2min with 3 different robots Robots programmed with follow & avoidance behavior Odometry and range data transmission every 15 sec by wireless communication Decoding of HEX-data SLAM implemented in MATLAB Test Results 1/6
13 Raw odometry Data / Calibration issues Odometry data: Registered ticks on left and right wheel conversion of ticks into distance Calibration: tick conversion values are not exactly known Transmission errors in data packages need to correct and cut data Length of one data block Max. possible speed (40mm/s Test Results 2/6 Left Uncorrected tick = right data tick Left tick Corrected = 0.98* data right tick
14 Raw sensor Data Raw IR sensor data: Conversion of ADC value into range value Computation of target coordinates (georeferencing) Feature detection edges, walls x t (i),y t (i) α sensor γ(i) Test Results r(i) left xi X i cos( λ i ) sin( λ i ) r sin( front α ) * sensor = + a sensor + ri yi Yi sin( λi) cos( λi) cos( α sensor ) right for sensor = left,front,right,back back y a sensor x x c (i),y c (i) 3/6
15 Grid Modelling (Occupancy Grid Map) Map robot position and georeferenced target directly on grid Labyrinth reconstruction requires feature recognition Feature recognition difficult because of the quality of the Odometer data Range measurements Scan matching impossible Random measurements in corners Data gaps Test Results 4/6
16 Boundary Detection Edges are not directly observable Fit lines by least squares while robot in follow wall behavior Evaluate turn rate and identify type of turn: Left turn (90 ) Right turn (90 ) U-turn (180 ) Attitude update after each turn Test Results Problem: position uncertainty still growing Track Estimated std Left scan Right scan 5/6
17 Network Modelling Approach Localization: It is sufficient to know in which corridor robot is Mapping: Construct topological network of the environment Identify known network points position update possible Attitude update after each turn Test Results Position uncertainty reduced Track Estimated std new / matched network point New / matched network segment 6/6
18 Conclusion Grid estimation approach not applicable: Feature recognition and scan matching almost impossible Boundary detection possible, but cumbersome: Implicit boundary detection by follow wall behavior (robots runs on straight line) Poor quality of georeferencing in turns Difficult to implement position updates Network estimation seems to be the most appropriate method for labyrinth mapping for the given sensors: More robust in turns Easier topology recognition Loop closure at every matched network point Map matching along line segments Conclusion 1/2
19 Outlook After several passes through the same network, fewer assumptions might be required: Store and verify topological beliefs Direction and position updates later, but no assumption with respect to the network angles necessary Turn 1 Left Straight Right Back Conclusion Turn 2 L S R B L S R B L S R B L S R B plr ( ) = 45% Trajectory Network guesses pls ( ) = 35% pll ( ) = 15% plb ( ) = 5% 2/2
Robotics. Lecture 8: Simultaneous Localisation and Mapping (SLAM)
Robotics Lecture 8: Simultaneous Localisation and Mapping (SLAM) See course website http://www.doc.ic.ac.uk/~ajd/robotics/ for up to date information. Andrew Davison Department of Computing Imperial College
More informationAutonomous Mobile Robot Design
Autonomous Mobile Robot Design Topic: EKF-based SLAM Dr. Kostas Alexis (CSE) These slides have partially relied on the course of C. Stachniss, Robot Mapping - WS 2013/14 Autonomous Robot Challenges Where
More informationData Association for SLAM
CALIFORNIA INSTITUTE OF TECHNOLOGY ME/CS 132a, Winter 2011 Lab #2 Due: Mar 10th, 2011 Part I Data Association for SLAM 1 Introduction For this part, you will experiment with a simulation of an EKF SLAM
More informationFinal Exam Practice Fall Semester, 2012
COS 495 - Autonomous Robot Navigation Final Exam Practice Fall Semester, 2012 Duration: Total Marks: 70 Closed Book 2 hours Start Time: End Time: By signing this exam, I agree to the honor code Name: Signature:
More informationGregory Bock, Brittany Dhall, Ryan Hendrickson, & Jared Lamkin Project Advisors: Dr. Jing Wang & Dr. In Soo Ahn Department of Electrical and Computer
Gregory Bock, Brittany Dhall, Ryan Hendrickson, & Jared Lamkin Project Advisors: Dr. Jing Wang & Dr. In Soo Ahn Department of Electrical and Computer Engineering April 26 th, 2016 Outline 2 I. Introduction
More informationOverview. EECS 124, UC Berkeley, Spring 2008 Lecture 23: Localization and Mapping. Statistical Models
Introduction ti to Embedded dsystems EECS 124, UC Berkeley, Spring 2008 Lecture 23: Localization and Mapping Gabe Hoffmann Ph.D. Candidate, Aero/Astro Engineering Stanford University Statistical Models
More informationRobotics. Lecture 7: Simultaneous Localisation and Mapping (SLAM)
Robotics Lecture 7: Simultaneous Localisation and Mapping (SLAM) See course website http://www.doc.ic.ac.uk/~ajd/robotics/ for up to date information. Andrew Davison Department of Computing Imperial College
More informationKalman Filter Based. Localization
Autonomous Mobile Robots Localization "Position" Global Map Cognition Environment Model Local Map Path Perception Real World Environment Motion Control Kalman Filter Based Localization & SLAM Zürich Autonomous
More informationParticle Filters. CSE-571 Probabilistic Robotics. Dependencies. Particle Filter Algorithm. Fast-SLAM Mapping
CSE-571 Probabilistic Robotics Fast-SLAM Mapping Particle Filters Represent belief by random samples Estimation of non-gaussian, nonlinear processes Sampling Importance Resampling (SIR) principle Draw
More informationProbabilistic Robotics
Probabilistic Robotics FastSLAM Sebastian Thrun (abridged and adapted by Rodrigo Ventura in Oct-2008) The SLAM Problem SLAM stands for simultaneous localization and mapping The task of building a map while
More informationProbabilistic Robotics. FastSLAM
Probabilistic Robotics FastSLAM The SLAM Problem SLAM stands for simultaneous localization and mapping The task of building a map while estimating the pose of the robot relative to this map Why is SLAM
More informationECE276A: Sensing & Estimation in Robotics Lecture 11: Simultaneous Localization and Mapping using a Particle Filter
ECE276A: Sensing & Estimation in Robotics Lecture 11: Simultaneous Localization and Mapping using a Particle Filter Lecturer: Nikolay Atanasov: natanasov@ucsd.edu Teaching Assistants: Siwei Guo: s9guo@eng.ucsd.edu
More information5. Tests and results Scan Matching Optimization Parameters Influence
126 5. Tests and results This chapter presents results obtained using the proposed method on simulated and real data. First, it is analyzed the scan matching optimization; after that, the Scan Matching
More informationIntroduction to Mobile Robotics
Introduction to Mobile Robotics Olivier Aycard Associate Professor University of Grenoble Laboratoire d Informatique de Grenoble http://membres-liglab.imag.fr/aycard 1/29 Some examples of mobile robots
More informationLocalization and Map Building
Localization and Map Building Noise and aliasing; odometric position estimation To localize or not to localize Belief representation Map representation Probabilistic map-based localization Other examples
More informationImplementation of Odometry with EKF for Localization of Hector SLAM Method
Implementation of Odometry with EKF for Localization of Hector SLAM Method Kao-Shing Hwang 1 Wei-Cheng Jiang 2 Zuo-Syuan Wang 3 Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung,
More informationProbabilistic Robotics
Probabilistic Robotics Bayes Filter Implementations Discrete filters, Particle filters Piecewise Constant Representation of belief 2 Discrete Bayes Filter Algorithm 1. Algorithm Discrete_Bayes_filter(
More informationMobile robot localisation and navigation using multi-sensor fusion via interval analysis and UKF
Mobile robot localisation and navigation using multi-sensor fusion via interval analysis and UKF Immanuel Ashokaraj, Antonios Tsourdos, Peter Silson and Brian White. Department of Aerospace, Power and
More informationIntroduction to robot algorithms CSE 410/510
Introduction to robot algorithms CSE 410/510 Rob Platt robplatt@buffalo.edu Times: MWF, 10-10:50 Location: Clemens 322 Course web page: http://people.csail.mit.edu/rplatt/cse510.html Office Hours: 11-12
More informationCalibration of a rotating multi-beam Lidar
The 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems October 18-22, 2010, Taipei, Taiwan Calibration of a rotating multi-beam Lidar Naveed Muhammad 1,2 and Simon Lacroix 1,2 Abstract
More informationPractical Robotics (PRAC)
Practical Robotics (PRAC) A Mobile Robot Navigation System (1) - Sensor and Kinematic Modelling Nick Pears University of York, Department of Computer Science December 17, 2014 nep (UoY CS) PRAC Practical
More informationIntegrated Laser-Camera Sensor for the Detection and Localization of Landmarks for Robotic Applications
28 IEEE International Conference on Robotics and Automation Pasadena, CA, USA, May 19-23, 28 Integrated Laser-Camera Sensor for the Detection and Localization of Landmarks for Robotic Applications Dilan
More informationLocalization and Map Building
Localization and Map Building Noise and aliasing; odometric position estimation To localize or not to localize Belief representation Map representation Probabilistic map-based localization Other examples
More informationAnalysis, optimization, and design of a SLAM solution for an implementation on reconfigurable hardware (FPGA) using CńaSH
December 12, 2016 MASTER THESIS Analysis, optimization, and design of a SLAM solution for an implementation on reconfigurable hardware (FPGA) using CńaSH Authors: Robin Appel Hendrik Folmer Faculty: Faculty
More informationAnalysis of Different Reference Plane Setups for the Calibration of a Mobile Laser Scanning System
Analysis of Different Reference Plane Setups for the Calibration of a Mobile Laser Scanning System 18. Internationaler Ingenieurvermessungskurs Graz, Austria, 25-29 th April 2017 Erik Heinz, Christian
More informationLocalization algorithm using a virtual label for a mobile robot in indoor and outdoor environments
Artif Life Robotics (2011) 16:361 365 ISAROB 2011 DOI 10.1007/s10015-011-0951-7 ORIGINAL ARTICLE Ki Ho Yu Min Cheol Lee Jung Hun Heo Youn Geun Moon Localization algorithm using a virtual label for a mobile
More informationMatching Evaluation of 2D Laser Scan Points using Observed Probability in Unstable Measurement Environment
Matching Evaluation of D Laser Scan Points using Observed Probability in Unstable Measurement Environment Taichi Yamada, and Akihisa Ohya Abstract In the real environment such as urban areas sidewalk,
More informationA Genetic Algorithm for Simultaneous Localization and Mapping
A Genetic Algorithm for Simultaneous Localization and Mapping Tom Duckett Centre for Applied Autonomous Sensor Systems Dept. of Technology, Örebro University SE-70182 Örebro, Sweden http://www.aass.oru.se
More informationSurvey: Simultaneous Localisation and Mapping (SLAM) Ronja Güldenring Master Informatics Project Intellgient Robotics University of Hamburg
Survey: Simultaneous Localisation and Mapping (SLAM) Ronja Güldenring Master Informatics Project Intellgient Robotics University of Hamburg Introduction EKF-SLAM FastSLAM Loop Closure 01.06.17 Ronja Güldenring
More informationToday MAPS AND MAPPING. Features. process of creating maps. More likely features are things that can be extracted from images:
MAPS AND MAPPING Features In the first class we said that navigation begins with what the robot can see. There are several forms this might take, but it will depend on: What sensors the robot has What
More informationZürich. Roland Siegwart Margarita Chli Martin Rufli Davide Scaramuzza. ETH Master Course: L Autonomous Mobile Robots Summary
Roland Siegwart Margarita Chli Martin Rufli Davide Scaramuzza ETH Master Course: 151-0854-00L Autonomous Mobile Robots Summary 2 Lecture Overview Mobile Robot Control Scheme knowledge, data base mission
More informationSingle Sound Source SLAM
Single Sound Source SLAM Rodrigo Munguía and Antoni Grau Department of Automatic Control, UPC, c/ Pau Gargallo, 5 E-08028 Barcelona, Spain {rodrigo.munguia,antoni.grau}@upc.edu http://webesaii.upc.es Abstract.
More informationF1/10 th Autonomous Racing. Localization. Nischal K N
F1/10 th Autonomous Racing Localization Nischal K N System Overview Mapping Hector Mapping Localization Path Planning Control System Overview Mapping Hector Mapping Localization Adaptive Monte Carlo Localization
More informationDEALING WITH SENSOR ERRORS IN SCAN MATCHING FOR SIMULTANEOUS LOCALIZATION AND MAPPING
Inženýrská MECHANIKA, roč. 15, 2008, č. 5, s. 337 344 337 DEALING WITH SENSOR ERRORS IN SCAN MATCHING FOR SIMULTANEOUS LOCALIZATION AND MAPPING Jiří Krejsa, Stanislav Věchet* The paper presents Potential-Based
More informationGaussian Processes, SLAM, Fast SLAM and Rao-Blackwellization
Statistical Techniques in Robotics (16-831, F11) Lecture#20 (November 21, 2011) Gaussian Processes, SLAM, Fast SLAM and Rao-Blackwellization Lecturer: Drew Bagnell Scribes: Junier Oliva 1 1 Comments on
More informationOpenStreetSLAM: Global Vehicle Localization using OpenStreetMaps
OpenStreetSLAM: Global Vehicle Localization using OpenStreetMaps Georgios Floros, Benito van der Zander and Bastian Leibe RWTH Aachen University, Germany http://www.vision.rwth-aachen.de floros@vision.rwth-aachen.de
More informationProbabilistic Robotics
Probabilistic Robotics Sebastian Thrun Wolfram Burgard Dieter Fox The MIT Press Cambridge, Massachusetts London, England Preface xvii Acknowledgments xix I Basics 1 1 Introduction 3 1.1 Uncertainty in
More informationSensor Modalities. Sensor modality: Different modalities:
Sensor Modalities Sensor modality: Sensors which measure same form of energy and process it in similar ways Modality refers to the raw input used by the sensors Different modalities: Sound Pressure Temperature
More informationEE565:Mobile Robotics Lecture 3
EE565:Mobile Robotics Lecture 3 Welcome Dr. Ahmad Kamal Nasir Today s Objectives Motion Models Velocity based model (Dead-Reckoning) Odometry based model (Wheel Encoders) Sensor Models Beam model of range
More informationSimultaneous Localization and Mapping
Sebastian Lembcke SLAM 1 / 29 MIN Faculty Department of Informatics Simultaneous Localization and Mapping Visual Loop-Closure Detection University of Hamburg Faculty of Mathematics, Informatics and Natural
More informationSimulation of a mobile robot with a LRF in a 2D environment and map building
Simulation of a mobile robot with a LRF in a 2D environment and map building Teslić L. 1, Klančar G. 2, and Škrjanc I. 3 1 Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana,
More informationGeometrical Feature Extraction Using 2D Range Scanner
Geometrical Feature Extraction Using 2D Range Scanner Sen Zhang Lihua Xie Martin Adams Fan Tang BLK S2, School of Electrical and Electronic Engineering Nanyang Technological University, Singapore 639798
More informationCSE 527: Introduction to Computer Vision
CSE 527: Introduction to Computer Vision Week 10 Class 2: Visual Odometry November 2nd, 2017 Today Visual Odometry Intro Algorithm SLAM Visual Odometry Input Output Images, Video Camera trajectory, motion
More informationSystem 1 Overview. System 1 Overview. Geometric Model-based Object Recognition. This Lecture: Geometric description Next Lecture: Model matching
System 1 Overview How to discriminate between and also estimate image positions System 1 Overview Geometric Model-based Object Recognition This Lecture: Geometric description Next Lecture: Model matching
More informationEfficient L-Shape Fitting for Vehicle Detection Using Laser Scanners
Efficient L-Shape Fitting for Vehicle Detection Using Laser Scanners Xiao Zhang, Wenda Xu, Chiyu Dong, John M. Dolan, Electrical and Computer Engineering, Carnegie Mellon University Robotics Institute,
More informationIntroduction to Mobile Robotics SLAM Landmark-based FastSLAM
Introduction to Mobile Robotics SLAM Landmark-based FastSLAM Wolfram Burgard, Cyrill Stachniss, Maren Bennewitz, Diego Tipaldi, Luciano Spinello Partial slide courtesy of Mike Montemerlo 1 The SLAM Problem
More informationLecture 20: The Spatial Semantic Hierarchy. What is a Map?
Lecture 20: The Spatial Semantic Hierarchy CS 344R/393R: Robotics Benjamin Kuipers What is a Map? A map is a model of an environment that helps an agent plan and take action. A topological map is useful
More informationL17. OCCUPANCY MAPS. NA568 Mobile Robotics: Methods & Algorithms
L17. OCCUPANCY MAPS NA568 Mobile Robotics: Methods & Algorithms Today s Topic Why Occupancy Maps? Bayes Binary Filters Log-odds Occupancy Maps Inverse sensor model Learning inverse sensor model ML map
More informationLocalization, Mapping and Exploration with Multiple Robots. Dr. Daisy Tang
Localization, Mapping and Exploration with Multiple Robots Dr. Daisy Tang Two Presentations A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping, by Thrun, Burgard
More informationRobotics. Lecture 5: Monte Carlo Localisation. See course website for up to date information.
Robotics Lecture 5: Monte Carlo Localisation See course website http://www.doc.ic.ac.uk/~ajd/robotics/ for up to date information. Andrew Davison Department of Computing Imperial College London Review:
More informationA Sparse Hybrid Map for Vision-Guided Mobile Robots
A Sparse Hybrid Map for Vision-Guided Mobile Robots Feras Dayoub Grzegorz Cielniak Tom Duckett Department of Computing and Informatics, University of Lincoln, Lincoln, UK {fdayoub,gcielniak,tduckett}@lincoln.ac.uk
More informationBuilding Reliable 2D Maps from 3D Features
Building Reliable 2D Maps from 3D Features Dipl. Technoinform. Jens Wettach, Prof. Dr. rer. nat. Karsten Berns TU Kaiserslautern; Robotics Research Lab 1, Geb. 48; Gottlieb-Daimler- Str.1; 67663 Kaiserslautern;
More informationCSE-571 Robotics. Sensors for Mobile Robots. Beam-based Sensor Model. Proximity Sensors. Probabilistic Sensor Models. Beam-based Scan-based Landmarks
Sensors for Mobile Robots CSE-57 Robotics Probabilistic Sensor Models Beam-based Scan-based Landmarks Contact sensors: Bumpers Internal sensors Accelerometers (spring-mounted masses) Gyroscopes (spinning
More informationUAV Autonomous Navigation in a GPS-limited Urban Environment
UAV Autonomous Navigation in a GPS-limited Urban Environment Yoko Watanabe DCSD/CDIN JSO-Aerial Robotics 2014/10/02-03 Introduction 2 Global objective Development of a UAV onboard system to maintain flight
More informationIROS 05 Tutorial. MCL: Global Localization (Sonar) Monte-Carlo Localization. Particle Filters. Rao-Blackwellized Particle Filters and Loop Closing
IROS 05 Tutorial SLAM - Getting it Working in Real World Applications Rao-Blackwellized Particle Filters and Loop Closing Cyrill Stachniss and Wolfram Burgard University of Freiburg, Dept. of Computer
More informationCOS Lecture 13 Autonomous Robot Navigation
COS 495 - Lecture 13 Autonomous Robot Navigation Instructor: Chris Clark Semester: Fall 2011 1 Figures courtesy of Siegwart & Nourbakhsh Control Structure Prior Knowledge Operator Commands Localization
More informationHumanoid Robotics. Monte Carlo Localization. Maren Bennewitz
Humanoid Robotics Monte Carlo Localization Maren Bennewitz 1 Basis Probability Rules (1) If x and y are independent: Bayes rule: Often written as: The denominator is a normalizing constant that ensures
More informationDevelopment of a Test Field for the Calibration and Evaluation of Kinematic Multi Sensor Systems
Development of a Test Field for the Calibration and Evaluation of Kinematic Multi Sensor Systems DGK-Doktorandenseminar Graz, Austria, 26 th April 2017 Erik Heinz Institute of Geodesy and Geoinformation
More informationVehicle Localization. Hannah Rae Kerner 21 April 2015
Vehicle Localization Hannah Rae Kerner 21 April 2015 Spotted in Mtn View: Google Car Why precision localization? in order for a robot to follow a road, it needs to know where the road is to stay in a particular
More informationEKF Localization and EKF SLAM incorporating prior information
EKF Localization and EKF SLAM incorporating prior information Final Report ME- Samuel Castaneda ID: 113155 1. Abstract In the context of mobile robotics, before any motion planning or navigation algorithm
More informationProject 1 : Dead Reckoning and Tracking
CS3630 Spring 2012 Project 1 : Dead Reckoning and Tracking Group : Wayward Sons Sameer Ansari, David Bernal, Tommy Kazenstein 2/8/2012 Wayward Sons CS3630 Spring 12 Project 1 Page 2 of 12 CS 3630 (Spring
More informationProbabilistic Robotics
Probabilistic Robotics Probabilistic Motion and Sensor Models Some slides adopted from: Wolfram Burgard, Cyrill Stachniss, Maren Bennewitz, Kai Arras and Probabilistic Robotics Book SA-1 Sensors for Mobile
More informationMachine learning based automatic extrinsic calibration of an onboard monocular camera for driving assistance applications on smart mobile devices
Technical University of Cluj-Napoca Image Processing and Pattern Recognition Research Center www.cv.utcluj.ro Machine learning based automatic extrinsic calibration of an onboard monocular camera for driving
More informationSeminar Dept. Automação e Sistemas - UFSC Scan-to-Map Matching Using the Hausdorff Distance for Robust Mobile Robot Localization
Seminar Dept. Automação e Sistemas - UFSC Scan-to-Map Matching Using the Hausdorff Distance for Robust Mobile Robot Localization Work presented at ICRA 2008, jointly with ANDRES GUESALAGA PUC Chile Miguel
More informationCover Page. Abstract ID Paper Title. Automated extraction of linear features from vehicle-borne laser data
Cover Page Abstract ID 8181 Paper Title Automated extraction of linear features from vehicle-borne laser data Contact Author Email Dinesh Manandhar (author1) dinesh@skl.iis.u-tokyo.ac.jp Phone +81-3-5452-6417
More informationRobotic Perception and Action: Vehicle SLAM Assignment
Robotic Perception and Action: Vehicle SLAM Assignment Mariolino De Cecco Mariolino De Cecco, Mattia Tavernini 1 CONTENTS Vehicle SLAM Assignment Contents Assignment Scenario 3 Odometry Localization...........................................
More informationME 597/747 Autonomous Mobile Robots. Mid Term Exam. Duration: 2 hour Total Marks: 100
ME 597/747 Autonomous Mobile Robots Mid Term Exam Duration: 2 hour Total Marks: 100 Instructions: Read the exam carefully before starting. Equations are at the back, but they are NOT necessarily valid
More informationAccurate Motion Estimation and High-Precision 3D Reconstruction by Sensor Fusion
007 IEEE International Conference on Robotics and Automation Roma, Italy, 0-4 April 007 FrE5. Accurate Motion Estimation and High-Precision D Reconstruction by Sensor Fusion Yunsu Bok, Youngbae Hwang,
More informationParticle Filter for Robot Localization ECE 478 Homework #1
Particle Filter for Robot Localization ECE 478 Homework #1 Phil Lamb pjl@pdx.edu November 15, 2012 1 Contents 1 Introduction 3 2 Implementation 3 2.1 Assumptions and Simplifications.............................
More informationTechnical Documentation AB Mail Robot
Technical Documentation AB Mail Robot Version 1.0 Author: Martin Melin Date: December 16, 2010 AB Mail Robot II Status Reviewed Karl Granström 2010-12-15 André Carvalho Bittencourt Approved Karl Granström
More informationOptimal Trajectory Generation for Nonholonomic Robots in Dynamic Environments
28 IEEE International Conference on Robotics and Automation Pasadena, CA, USA, May 19-23, 28 Optimal Trajectory Generation for Nonholonomic Robots in Dynamic Environments Yi Guo and Tang Tang Abstract
More informationOn the Use of Inverse Scaling in Monocular SLAM
On the Use of Inverse Scaling in Monocular SLAM Daniele Marzorati 1, Matteo Matteucci 2, Davide Migliore 2, Domenico G. Sorrenti 1 1 Università degli Studi di Milano - Bicocca 2 Politecnico di Milano SLAM
More informationLocalization, Where am I?
5.1 Localization, Where am I?? position Position Update (Estimation?) Encoder Prediction of Position (e.g. odometry) YES matched observations Map data base predicted position Matching Odometry, Dead Reckoning
More informationW4. Perception & Situation Awareness & Decision making
W4. Perception & Situation Awareness & Decision making Robot Perception for Dynamic environments: Outline & DP-Grids concept Dynamic Probabilistic Grids Bayesian Occupancy Filter concept Dynamic Probabilistic
More informationCONNECT-THE-DOTS IN A GRAPH AND BUFFON S NEEDLE ON A CHESSBOARD: TWO PROBLEMS IN ASSISTED NAVIGATION
1 CONNECT-THE-DOTS IN A GRAPH AND BUFFON S NEEDLE ON A CHESSBOARD: TWO PROBLEMS IN ASSISTED NAVIGATION MINGHUI JIANG and VLADIMIR KULYUKIN Department of Computer Science, Utah State University, Logan,
More informationComputer Vision. Coordinates. Prof. Flávio Cardeal DECOM / CEFET- MG.
Computer Vision Coordinates Prof. Flávio Cardeal DECOM / CEFET- MG cardeal@decom.cefetmg.br Abstract This lecture discusses world coordinates and homogeneous coordinates, as well as provides an overview
More informationSpring Localization II. Roland Siegwart, Margarita Chli, Martin Rufli. ASL Autonomous Systems Lab. Autonomous Mobile Robots
Spring 2016 Localization II Localization I 25.04.2016 1 knowledge, data base mission commands Localization Map Building environment model local map position global map Cognition Path Planning path Perception
More informationBinocular Stereo Vision. System 6 Introduction Is there a Wedge in this 3D scene?
System 6 Introduction Is there a Wedge in this 3D scene? Binocular Stereo Vision Data a stereo pair of images! Given two 2D images of an object, how can we reconstruct 3D awareness of it? AV: 3D recognition
More informationPersonal Navigation and Indoor Mapping: Performance Characterization of Kinect Sensor-based Trajectory Recovery
Personal Navigation and Indoor Mapping: Performance Characterization of Kinect Sensor-based Trajectory Recovery 1 Charles TOTH, 1 Dorota BRZEZINSKA, USA 2 Allison KEALY, Australia, 3 Guenther RETSCHER,
More informationRobot Mapping. SLAM Front-Ends. Cyrill Stachniss. Partial image courtesy: Edwin Olson 1
Robot Mapping SLAM Front-Ends Cyrill Stachniss Partial image courtesy: Edwin Olson 1 Graph-Based SLAM Constraints connect the nodes through odometry and observations Robot pose Constraint 2 Graph-Based
More informationIntroduction to Mobile Robotics SLAM Grid-based FastSLAM. Wolfram Burgard, Cyrill Stachniss, Maren Bennewitz, Diego Tipaldi, Luciano Spinello
Introduction to Mobile Robotics SLAM Grid-based FastSLAM Wolfram Burgard, Cyrill Stachniss, Maren Bennewitz, Diego Tipaldi, Luciano Spinello 1 The SLAM Problem SLAM stands for simultaneous localization
More informationPiecewise-Planar 3D Reconstruction with Edge and Corner Regularization
Piecewise-Planar 3D Reconstruction with Edge and Corner Regularization Alexandre Boulch Martin de La Gorce Renaud Marlet IMAGINE group, Université Paris-Est, LIGM, École Nationale des Ponts et Chaussées
More informationAn Automatic Method for Adjustment of a Camera Calibration Room
An Automatic Method for Adjustment of a Camera Calibration Room Presented at the FIG Working Week 2017, May 29 - June 2, 2017 in Helsinki, Finland Theory, algorithms, implementation, and two advanced applications.
More informationDevelopment of 3D Positioning Scheme by Integration of Multiple Wiimote IR Cameras
Proceedings of the 5th IIAE International Conference on Industrial Application Engineering 2017 Development of 3D Positioning Scheme by Integration of Multiple Wiimote IR Cameras Hui-Yuan Chan *, Ting-Hao
More informationME 456: Probabilistic Robotics
ME 456: Probabilistic Robotics Week 5, Lecture 2 SLAM Reading: Chapters 10,13 HW 2 due Oct 30, 11:59 PM Introduction In state esemaeon and Bayes filter lectures, we showed how to find robot s pose based
More informationMath 26: Fall (part 1) The Unit Circle: Cosine and Sine (Evaluating Cosine and Sine, and The Pythagorean Identity)
Math : Fall 0 0. (part ) The Unit Circle: Cosine and Sine (Evaluating Cosine and Sine, and The Pthagorean Identit) Cosine and Sine Angle θ standard position, P denotes point where the terminal side of
More informationTightly-Integrated Visual and Inertial Navigation for Pinpoint Landing on Rugged Terrains
Tightly-Integrated Visual and Inertial Navigation for Pinpoint Landing on Rugged Terrains PhD student: Jeff DELAUNE ONERA Director: Guy LE BESNERAIS ONERA Advisors: Jean-Loup FARGES Clément BOURDARIAS
More informationInvestigating Simultaneous Localization and Mapping for AGV systems
Investigating Simultaneous Localization and Mapping for AGV systems With open-source modules available in ROS Master s thesis in Computer Systems and Networks ALBIN PÅLSSON MARKUS SMEDBERG Department of
More informationMulti-Robot SLAM using M-Space Feature Representation
Università degli Studi di Siena Facoltà di Ingegneria Corso di Laurea Specialistica in Ingegneria Informatica Multi-Robot SLAM using M-Space Feature Representation Relatore Prof. Andrea Garulli Correlatore
More informationReal Time Multi-Sensor Data Acquisition and Processing for a Road Mapping System
Real Time Multi-Sensor Data Acquisition and Processing for a Road Mapping System by Xiang Luo A thesis submitted for the degree of Master of Engineering (Research) Faculty of Engineering and Information
More informationSpring Localization II. Roland Siegwart, Margarita Chli, Juan Nieto, Nick Lawrance. ASL Autonomous Systems Lab. Autonomous Mobile Robots
Spring 2018 Localization II Localization I 16.04.2018 1 knowledge, data base mission commands Localization Map Building environment model local map position global map Cognition Path Planning path Perception
More informationNatural landmark extraction for mobile robot navigation based on an adaptive curvature estimation
Robotics and Autonomous Systems 56 (2008) 247 264 www.elsevier.com/locate/robot Natural landmark extraction for mobile robot navigation based on an adaptive curvature estimation P. Núñez, R. Vázquez-Martín,
More informationPacSLAM Arunkumar Byravan, Tanner Schmidt, Erzhuo Wang
PacSLAM Arunkumar Byravan, Tanner Schmidt, Erzhuo Wang Project Goals The goal of this project was to tackle the simultaneous localization and mapping (SLAM) problem for mobile robots. Essentially, the
More informationRobot Localization based on Geo-referenced Images and G raphic Methods
Robot Localization based on Geo-referenced Images and G raphic Methods Sid Ahmed Berrabah Mechanical Department, Royal Military School, Belgium, sidahmed.berrabah@rma.ac.be Janusz Bedkowski, Łukasz Lubasiński,
More informationSimultaneous local and global state estimation for robotic navigation
Simultaneous local and global state estimation for robotic navigation The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation As
More informationLinear algebra deals with matrixes: two-dimensional arrays of values. Here s a matrix: [ x + 5y + 7z 9x + 3y + 11z
Basic Linear Algebra Linear algebra deals with matrixes: two-dimensional arrays of values. Here s a matrix: [ 1 5 ] 7 9 3 11 Often matrices are used to describe in a simpler way a series of linear equations.
More informationMTRX4700: Experimental Robotics
Stefan B. Williams April, 2013 MTR4700: Experimental Robotics Assignment 3 Note: This assignment contributes 10% towards your final mark. This assignment is due on Friday, May 10 th during Week 9 before
More informationBayesian Train Localization Method Extended By 3D Geometric Railway Track Observations From Inertial Sensors
www.dlr.de Chart 1 Fusion 2012 > Oliver Heirich Extended Bayesian Train Localization > 10.7.2012 Bayesian Train Localization Method Extended By 3D Geometric Railway Track Observations From Inertial Sensors
More informationTracking Multiple Moving Objects with a Mobile Robot
Tracking Multiple Moving Objects with a Mobile Robot Dirk Schulz 1 Wolfram Burgard 2 Dieter Fox 3 Armin B. Cremers 1 1 University of Bonn, Computer Science Department, Germany 2 University of Freiburg,
More informationAutonomous Mobile Robots, Chapter 6 Planning and Navigation Where am I going? How do I get there? Localization. Cognition. Real World Environment
Planning and Navigation Where am I going? How do I get there?? Localization "Position" Global Map Cognition Environment Model Local Map Perception Real World Environment Path Motion Control Competencies
More information