# A robot s Navigation Problems. Localization. The Localization Problem. Sensor Readings

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 A robot s Navigation Problems Localization Where am I? Localization Where have I been? Map making Where am I going? Mission planning What s the best way there? Path planning 1 2 The Localization Problem Sensor Readings Detection of the position of a robot relative to its environment, using the information about the environment gathered by the robot. The robot typically uses its sensors to gather information about the environment. Types of sensor readings used in localization: Vision, Sonar distance readings, Laser scanner distance readings, GPS signals, Electronic compass readings, WLAN signal strength, 3 4

2 Pose Types of Localization Problems In two dimensions robot s x, y coordinates and heading Position (pose) tracking : Keeping track of the robot s pose using odometry, assuming that the initial position of the robot is known. Global localization : Finding its location in the environment without using a priori information. Kidnapping : The robot is taken from its current location and carried to another location by teleportation without the robot being aware. The robot should realize this, and relocalize itself in its new position. This is possibly the hardest problem among others. 5 6 Active vs Passive Localization Classification of Localization Algorithms Active Localization: The robot actively searches its environment to find landmarks to localize itself better, and decrease its localization error. Passive localization: The robot is busy with its task, and it runs the localization as a background task, using the observations it gets during its main task implementation. [Köse 2006] 7 8

3 Odometry Triangulation Odometry is used by mobile robots to estimate their position relative to a starting location. Uses data from the rotation of wheels or legs to estimate change in position over time. Often very sensitive to error. Rapid and accurate data collection, equipment calibration, and processing are required in most cases for odometry to be used effectively. The simplest localization method Uses the geometric properties of triangles based on the distances and/or angle measurements between the robot and the observed landmarks Triangulation using distance and angle measurements Triangulation using distance and angle measurements (cont.) The position of the robot is calculated using the measured distance between the robot and the multiple reference positions. For the position estimation in two dimensions, distance measurements from three non-collinear points are required: Two angle measurements and one distance measurement can also be used: [Çelik 2005] [Çelik 2005] 11 12

4 Probabilistic Localization Probabilistic Localization Example The robot s location is represented as a probability density over possible robot locations or poses. Belief state: Represents the robot s belief in its current position. It summarizes all the information the robot has about its current position. It is updated for movements of the robot and for information from sensors. Initially, the robot does not know where it is Probabilistic Localization Example (cont.) Probabilistic Localization Example (cont.) The robot observes a landmark. The robot moves

5 Probabilistic Localization Example (cont.) Markov Localization Method It does not make any distribution assumptions, and allows any kind of distribution to be used. It uses odometry measurements and perceptional measurements to estimate the current position of the robot. The robot maintains a belief over its position which is denoted as Bel (t) (L) at time t. The ML method uses a histogram filter for posterior belief. The robot observes another landmark Markov Localization General Remarks Coarse Very robust No distribution assumption so more flexible but more costly High computational complexity Fast recovery from kidnapping Monte Carlo Localization Monte Carlo Localization (MCL) is a version of ML that relies on sample-based representation and the sampling/importance re-sampling algorithm for belief propagation Beliefs are represented by a set of K weighed samples (particles) which are of type ((x, y, θ), w), where w 0 is a non-negative numerical weighting factor such that the sum of all w is one. The weighting factors are called importance weights. Odometric and sensory updates are similar to ML. They are performed within the prediction and correction steps

6 The MCL Algorithm The MCL Algorithm procedure MCL(S, a, o) 1: for i = 0 to n do 2: draw by replacement random sample l from S according to w 1..w n (resampling) 3: draw sample l p(l a, l)(sampling) 4: calculate w=p(l o) (importance sampling) 5: add (l, w) to S 6: end for 7: normalize the importance factors w in S 8: return S MCL represents the probability distribution of location l by a sample set. The samples are located such that, the population size of samples is proportional to the probability of the robot being in that location and the surroundings. After several steps, the samples converge to some place in the environment. The position of the robot is found by taking the average of the positions of the samples, and The standard deviation gives the uncertainty of the robot MCL and Kidnapping Monte Carlo Localization The original MCL algorithm suffers in case of kidnapping. Several MCL extensions were proposed to overcome this problem. These extensions add new samples to the sample set from different parts of the field, and differ in the number of new samples and when to add these samples. Some of these methods are Sensor Resetting Localization (SRL), Mixture MCL (Mix-MCL), Adaptive MCL (A-MCL), and Kullback-Leibler Distance (KLD)-Sampling General Remarks Fails in case of kidnapping Accuracy and robustness depend on number of samples High number of samples ->less bias Less computational complexity than ML Less memory consumption than ML->depends on the number of samples Not as fast as ML in convergence 23 24

7 MCL Steps MCL Steps (cont.) The robot is initially lost The robot moves to right MCL Steps (cont.) MCL Steps (cont.) The robot observes a landmark. The robot keeps moving to the right, observes another landmark

8 Robocup Aibo Field Setup Robocup Nao Field Setup MCL Application Kalman Filter Method It implements belief computation for continuous states. It is similar to Markov Localization. This filter integrates uncertainty into computations by making the assumption of Gaussian distributions to represent all densities including positions, odometric and sensory measurements. Position estimates are updated by odometry and sensing alternately using the property that Gaussian distributions can be combined using multiplication. [Kaplan et al 2005] 31 32

9 R-MCL (Reverse MCL) method R-MCL Algorithm ML is a robust grid based method. Due to large grid cell sizes it is not very accurate. MCL is not so robust and converges more slowly than ML Too many samples are used to get accurate resultexpensive Why not combine these two methods? R-MCL [Köse 2006] 35

### Introduction to Mobile Robotics Bayes Filter Particle Filter and Monte Carlo Localization

Introduction to Mobile Robotics Bayes Filter Particle Filter and Monte Carlo Localization Wolfram Burgard, Maren Bennewitz, Diego Tipaldi, Luciano Spinello 1 Motivation Recall: Discrete filter Discretize

### Mapping and Localization with RFID Technology

Mapping and Localization with RFID Technology Matthai Philipose, Kenneth P Fishkin, Dieter Fox, Dirk Hahnel, Wolfram Burgard Presenter: Aniket Shah Outline Introduction Related Work Probabilistic Sensor

### P r oba bi l i sti c R oboti cs. Yrd. Doç. Dr. SIRMA YAVUZ sirma@ce.yildiz.edu.tr Room 130

P r oba bi l i sti c R oboti cs Yrd. Doç. Dr. SIRMA YAVUZ sirma@ce.yildiz.edu.tr Room 130 Orgazinational Lecture: Thursday 13:00 15:50 Office Hours: Tuesday 13:00-14:00 Thursday 11:00-12:00 Exams: 1 Midterm

### An Introduction to Mobile Robotics

An Introduction to Mobile Robotics Who am I. Steve Goldberg 15 years programming robots for NASA/JPL Worked on MSL, MER, BigDog and Crusher Expert in stereo vision and autonomous navigation Currently Telecommuting

### ROBOTICS 01PEEQW. Basilio Bona DAUIN Politecnico di Torino

ROBOTICS 01PEEQW Basilio Bona DAUIN Politecnico di Torino Probabilistic Fundamentals in Robotics Robot Motion Probabilistic models of mobile robots Robot motion Kinematics Velocity motion model Odometry

### Artificial Intelligence

Artificial Intelligence Robotics RWTH Aachen 1 Term and History Term comes from Karel Capek s play R.U.R. Rossum s universal robots Robots comes from the Czech word for corvee Manipulators first start

### Tracking Algorithms. Lecture17: Stochastic Tracking. Joint Probability and Graphical Model. Probabilistic Tracking

Tracking Algorithms (2015S) Lecture17: Stochastic Tracking Bohyung Han CSE, POSTECH bhhan@postech.ac.kr Deterministic methods Given input video and current state, tracking result is always same. Local

### Robot Perception Continued

Robot Perception Continued 1 Visual Perception Visual Odometry Reconstruction Recognition CS 685 11 Range Sensing strategies Active range sensors Ultrasound Laser range sensor Slides adopted from Siegwart

### Robot Navigation. Johannes Maurer, Institute for Software Technology TEDUSAR Summerschool 2014. u www.tugraz.at

1 Robot Navigation u www.tugraz.at 2 Motivation challenges physical laws e.g. inertia, acceleration uncertainty e.g. maps, observations geometric constraints e.g. shape of a robot dynamic environment e.g.

### E190Q Lecture 5 Autonomous Robot Navigation

E190Q Lecture 5 Autonomous Robot Navigation Instructor: Chris Clark Semester: Spring 2014 1 Figures courtesy of Siegwart & Nourbakhsh Control Structures Planning Based Control Prior Knowledge Operator

### Estimation of Position and Orientation of Mobile Systems in a Wireless LAN

Proceedings of the 46th IEEE Conference on Decision and Control New Orleans, LA, USA, Dec. 12-14, 2007 Estimation of Position and Orientation of Mobile Systems in a Wireless LAN Christof Röhrig and Frank

### Abstract. Background

Adressing CML by Using Line Detection Techniques for More Reliable Sonar Information. Eric M. Meisner, CSC 242Term Project University of Rochester 2003 Abstract The purpose of this paper will be to describe

### EL5223. Basic Concepts of Robot Sensors, Actuators, Localization, Navigation, and1 Mappin / 12

Basic Concepts of Robot Sensors, Actuators, Localization, Navigation, and Mapping Basic Concepts of Robot Sensors, Actuators, Localization, Navigation, and1 Mappin / 12 Sensors and Actuators Robotic systems

### BEST2015 Autonomous Mobile Robots Lecture 3: Mobile Robot Localization

BEST2015 Autonomous Mobile Robots Lecture 3: Mobile Robot Localization Renaud Ronsse renaud.ronsse@uclouvain.be École polytechnique de Louvain, UCLouvain July 2015 1 Introduction c Siegwart et al., 2011,

### Robotics. Chapter 25. Chapter 25 1

Robotics Chapter 25 Chapter 25 1 Outline Robots, Effectors, and Sensors Localization and Mapping Motion Planning Motor Control Chapter 25 2 Mobile Robots Chapter 25 3 Manipulators P R R R R R Configuration

### Mobile Robot FastSLAM with Xbox Kinect

Mobile Robot FastSLAM with Xbox Kinect Design Team Taylor Apgar, Sean Suri, Xiangdong Xi Design Advisor Prof. Greg Kowalski Abstract Mapping is an interesting and difficult problem in robotics. In order

### SIMULATION AND ANALYSIS OF RFID LOCALIZATION ALGORITHMS

SIMULATION AND ANALYSIS OF RFID LOCALIZATION ALGORITHMS A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Engineering By ZUBIN SHAH B.E., Gujarat

### Simulation and Analysis of RFID Localization Algorithms

Wright State University CORE Scholar Browse all Theses and Dissertations Theses and Dissertations 2006 Simulation and Analysis of RFID Localization Algorithms Zubin Shah Wright State University Follow

### REPRESENTATION OF ODOMETRY ERRORS ON OCCUPANCY GRIDS

REPRESENTATION OF ODOMETRY ERRORS ON OCCUPANCY GRIDS Anderson A. S. Souza, Andre M. Santana, Ricardo S. Britto, Luiz M. G. Gonalves, Aderlardo A. D. Medeiros Depatment of Computating Engineering and Automation,

### AN INTERACTIVE USER INTERFACE TO THE MOBILITY OBJECT MANAGER FOR RWI ROBOTS

AN INTERACTIVE USER INTERFACE TO THE MOBILITY OBJECT MANAGER FOR RWI ROBOTS Innocent Okoloko and Huosheng Hu Department of Computer Science, University of Essex Colchester Essex C04 3SQ, United Kingdom

### Robot Sensors. Outline. The Robot Structure. Robots and Sensors. Henrik I Christensen

Robot Sensors Henrik I Christensen Robotics & Intelligent Machines @ GT Georgia Institute of Technology, Atlanta, GA 30332-0760 hic@cc.gatech.edu Henrik I Christensen (RIM@GT) Sensors 1 / 38 Outline 1

### Cerberus 07 Team Description Paper

Cerberus 07 Team Description Paper H. Levent Akın, Çetin Meriçli, Barış Gökçe, Barış Kurt, Can Kavaklıoğlu and Abdullah Akçe Boğaziçi University Department of Computer Engineering 34342 Bebek, Ístanbul,

Tracking and integrated navigation Konrad Schindler Institute of Geodesy and Photogrammetry Tracking Navigation needs predictions for dynamic objects estimate trajectories in 3D world coordinates and extrapolate

### Master s thesis tutorial: part III

for the Autonomous Compliant Research group Tinne De Laet, Wilm Decré, Diederik Verscheure Katholieke Universiteit Leuven, Department of Mechanical Engineering, PMA Division 30 oktober 2006 Outline General

### Real-time Visual Tracker by Stream Processing

Real-time Visual Tracker by Stream Processing Simultaneous and Fast 3D Tracking of Multiple Faces in Video Sequences by Using a Particle Filter Oscar Mateo Lozano & Kuzahiro Otsuka presented by Piotr Rudol

### Wireless Networking Trends Architectures, Protocols & optimizations for future networking scenarios

Wireless Networking Trends Architectures, Protocols & optimizations for future networking scenarios H. Fathi, J. Figueiras, F. Fitzek, T. Madsen, R. Olsen, P. Popovski, HP Schwefel Session 1 Network Evolution

### Mapping and Localization with RFID Technology

Mapping and Localization with RFID Technology Dirk Hähnel Wolfram Burgard University of Freiburg Department of Computer Science Freiburg, Germany Dieter Fox University of Washington Computer Science and

### Multi-ultrasonic sensor fusion for autonomous mobile robots

Multi-ultrasonic sensor fusion for autonomous mobile robots Zou Yi *, Ho Yeong Khing, Chua Chin Seng, and Zhou Xiao Wei School of Electrical and Electronic Engineering Nanyang Technological University

### DP-SLAM: Fast, Robust Simultaneous Localization and Mapping Without Predetermined Landmarks

DP-SLAM: Fast, Robust Simultaneous Localization and Mapping Without Predetermined Landmarks Austin Eliazar and Ronald Parr Department of Computer Science Duke University eliazar, parr @cs.duke.edu Abstract

### Synthetic Sensing: Proximity / Distance Sensors

Synthetic Sensing: Proximity / Distance Sensors MediaRobotics Lab, February 2010 Proximity detection is dependent on the object of interest. One size does not fit all For non-contact distance measurement,

### Mapping and Localization with RFID Technology

Mapping and Localization with RFID Technology Matthai Philipose, Kenneth P Fishkin, Intel Research Seattle Dieter Fox, University of Washington Dirk Hahnel, Wolfram Burgard, University of Freiberg IRS-TR-3-14

### Sensors are for Perception

Exploring Robotics Lecture D Sensing Topics: 1) Perception, Levels of Processing, Simple Sensors. 2) A look at irobot sroomba 3) RCX s Decision Making based on Sensor Inputs Sensors are for Perception

### SIMULTANEOUS LOCALIZATION AND MAPPING FOR EVENT-BASED VISION SYSTEMS

SIMULTANEOUS LOCALIZATION AND MAPPING FOR EVENT-BASED VISION SYSTEMS David Weikersdorfer, Raoul Hoffmann, Jörg Conradt 9-th International Conference on Computer Vision Systems 18-th July 2013, St. Petersburg,

### Wifi Localization with Gaussian Processes

Wifi Localization with Gaussian Processes Brian Ferris University of Washington Joint work with: Dieter Fox Julie Letchner Dirk Haehnel Why Location? Assisted Cognition Project: Indoor/outdoor navigation

### Fast field survey with a smartphone

Fast field survey with a smartphone A. Masiero F. Fissore, F. Pirotti, A. Guarnieri, A. Vettore CIRGEO Interdept. Research Center of Geomatics University of Padova Italy cirgeo@unipd.it 1 Mobile Mapping

### LECTURE 4-1. Common Sensing Techniques for Reactive Robots. Introduction to AI Robotics (Sec )

LECTURE 4-1 Common Sensing Techniques for Reactive Robots Introduction to AI Robotics (Sec. 6.1 6.5) 1 Quote of the Week Just as some newborn race of superintelligent robots are about to consume all humanity,

### What s Left in E11? Technical Writing E11 Final Report

Technical Writing What s Left in E11? Technical Writing E11 Final Report 2 Next Week: Competition! Second Last Week: Robotics S&T, Eng&CS Outlooks, Last Week: Final Presentations 3 There are several common

### Robot Localization and Kalman Filters

UTRECHT UNIVERSITY Robot Localization and Kalman Filters On finding your position in a noisy world by Rudy Negenborn A thesis submitted to the Institute of Information and Computing Sciences in partial

### Multisensor Data Fusion and Applications

Multisensor Data Fusion and Applications Pramod K. Varshney Department of Electrical Engineering and Computer Science Syracuse University 121 Link Hall Syracuse, New York 13244 USA E-mail: varshney@syr.edu

### Autonomous Advertising Mobile Robot for Exhibitions, Developed at BMF

Autonomous Advertising Mobile Robot for Exhibitions, Developed at BMF Kucsera Péter (kucsera.peter@kvk.bmf.hu) Abstract In this article an autonomous advertising mobile robot that has been realized in

### CCNY. BME I5100: Biomedical Signal Processing. Linear Discrimination. Lucas C. Parra Biomedical Engineering Department City College of New York

BME I5100: Biomedical Signal Processing Linear Discrimination Lucas C. Parra Biomedical Engineering Department CCNY 1 Schedule Week 1: Introduction Linear, stationary, normal - the stuff biology is not

### CE801: Intelligent Systems and Robotics Lecture 3: Actuators and Localisation. Prof. Dr. Hani Hagras

1 CE801: Intelligent Systems and Robotics Lecture 3: Actuators and Localisation Prof. Dr. Hani Hagras Robot Locomotion Robots might want to move in water, in the air, on land, in space.. 2 Most of the

### Practical Tour of Visual tracking. David Fleet and Allan Jepson January, 2006

Practical Tour of Visual tracking David Fleet and Allan Jepson January, 2006 Designing a Visual Tracker: What is the state? pose and motion (position, velocity, acceleration, ) shape (size, deformation,

### Robotics. Lecture 3: Sensors. See course website http://www.doc.ic.ac.uk/~ajd/robotics/ for up to date information.

Robotics Lecture 3: Sensors 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: Locomotion Practical

### Multi-Sensor Mobile Robot Localization For Diverse Environments

Multi-Sensor Mobile Robot Localization For Diverse Environments Joydeep Biswas and Manuela Veloso School Of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA {joydeepb,veloso}@cs.cmu.edu

### The Use of Camera Information in Formulating and Solving Sensor Fusion Problems

The Use of Camera Information in Formulating and Solving Sensor Fusion Problems Thomas Schön Division of Automatic Control Linköping University Sweden Oc c The Problem Inertial sensors Inertial sensors

### FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem With Unknown Data Association

FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem With Unknown Data Association Michael Montemerlo 11th July 2003 CMU-RI-TR-03-28 The Robotics Institute Carnegie Mellon

### Real-Time Camera Tracking Using a Particle Filter

Real-Time Camera Tracking Using a Particle Filter Mark Pupilli and Andrew Calway Department of Computer Science University of Bristol, UK {pupilli,andrew}@cs.bris.ac.uk Abstract We describe a particle

### On-line Robot Adaptation to Environmental Change

On-line Robot Adaptation to Environmental Change Scott Lenser CMU-CS-05-165 August, 2005 School of Computer Science Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee:

### Where IS North? Experiment 3 OBJECTIVES

Where IS North? Experiment 3 It depends. Do you mean geographic north or magnetic north? The geographic (true) north pole is the point at 90 o N latitude. It is aligned with the rotational axis of the

### 2.1 Sensors and Processors for Mobile Robots

2 Literature Survey The survey is organized into sections covering prior work on various aspects presented in the subsequent chapters. Since a core component of the material presented in this book is description

### 3D POINT CLOUD CONSTRUCTION FROM STEREO IMAGES

3D POINT CLOUD CONSTRUCTION FROM STEREO IMAGES Brian Peasley * I propose an algorithm to construct a 3D point cloud from a sequence of stereo image pairs that show a full 360 degree view of an object.

### Particle Filters and Their Applications

Particle Filters and Their Applications Kaijen Hsiao Henry de Plinval-Salgues Jason Miller Cognitive Robotics April 11, 2005 1 Why Particle Filters? Tool for tracking the state of a dynamic system modeled

### 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER

REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188 The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions,

### FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem

astslam: A actored Solution to the Simultaneous Localiation and Mapping Problem Michael Montemerlo and Sebastian hrun School of Computer Science Carnegie Mellon University Pittsburgh, PA 1513 mmde@cs.cmu.edu,

### Automotive Applications of 3D Laser Scanning Introduction

Automotive Applications of 3D Laser Scanning Kyle Johnston, Ph.D., Metron Systems, Inc. 34935 SE Douglas Street, Suite 110, Snoqualmie, WA 98065 425-396-5577, www.metronsys.com 2002 Metron Systems, Inc

### Localization in Indoor Environments Using a Panoramic Laser Range Finder

Lehrstuhl für Realzeit-Computersysteme Localization in Indoor Environments Using a Panoramic Laser Range Finder Tobias Einsele Vollständiger Abdruck der von der Fakultät für Elektrotechnik und Informationstechnik

### COS Lecture 8 Autonomous Robot Navigation

COS 495 - Lecture 8 Autonomous Robot Navigation Instructor: Chris Clark Semester: Fall 2011 1 Figures courtesy of Siegwart & Nourbakhsh Control Structure Prior Knowledge Operator Commands Localization

### Lecture 11: Graphical Models for Inference

Lecture 11: Graphical Models for Inference So far we have seen two graphical models that are used for inference - the Bayesian network and the Join tree. These two both represent the same joint probability

### Grade 6 Grade-Level Goals. Equivalent names for fractions, decimals, and percents. Comparing and ordering numbers

Content Strand: Number and Numeration Understand the Meanings, Uses, and Representations of Numbers Understand Equivalent Names for Numbers Understand Common Numerical Relations Place value and notation

### Robot Morphologies, Sensors, and Motors Spring 2014 Prof. Yanco

Robot Morphologies, Sensors, and Motors 91.450 Spring 2014 Prof. Yanco Robot morphology (shape) Some possibilities Humanoid Trashcan robots Reconfigurable Shape shifting Biped Quadruped Why does shape

### Tutorial on variational approximation methods. Tommi S. Jaakkola MIT AI Lab

Tutorial on variational approximation methods Tommi S. Jaakkola MIT AI Lab tommi@ai.mit.edu Tutorial topics A bit of history Examples of variational methods A brief intro to graphical models Variational

### Introduction. Chapter 1

1 Chapter 1 Introduction Robotics and automation have undergone an outstanding development in the manufacturing industry over the last decades owing to the increasing demand for higher levels of productivity

### Pricing and calibration in local volatility models via fast quantization

Pricing and calibration in local volatility models via fast quantization Parma, 29 th January 2015. Joint work with Giorgia Callegaro and Martino Grasselli Quantization: a brief history Birth: back to

### Definitions. A [non-living] physical agent that performs tasks by manipulating the physical world. Categories of robots

Definitions A robot is A programmable, multifunction manipulator designed to move material, parts, tools, or specific devices through variable programmed motions for the performance of a variety of tasks.

### A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking

174 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 50, NO. 2, FEBRUARY 2002 A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking M. Sanjeev Arulampalam, Simon Maskell, Neil

### 3D Vision An enabling Technology for Advanced Driver Assistance and Autonomous Offroad Driving

3D Vision An enabling Technology for Advanced Driver Assistance and Autonomous Offroad Driving AIT Austrian Institute of Technology Safety & Security Department Christian Zinner Safe and Autonomous Systems

### Characterization of Inertial Sensor Measurements for Navigation Performance Analysis

Characterization of Inertial Sensor Measurements for Navigation Performance Analysis John H. Wall and David M. Bevly GPS and Vehicle Dynamics Lab, Department of Mechanical Engineering, Auburn University

### Incremental Mapping of Large Cyclic Environments

Incremental Mapping of Large Cyclic Environments Jens-Steffen Gutmann 1 Kurt Konolige 2 1 Institut für Informatik 2 SRI International Universität Freiburg 333 Ravenswood Avenue D-7911 Freiburg, Germany

### Classification of Fingerprints. Sarat C. Dass Department of Statistics & Probability

Classification of Fingerprints Sarat C. Dass Department of Statistics & Probability Fingerprint Classification Fingerprint classification is a coarse level partitioning of a fingerprint database into smaller

### 2. Colour based particle filter for 3-D tracking

MULTI-CAMERA 3-D TRACKING USING PARTICLE FILTER Pablo Barrera, José M. Cañas, Vicente Matellán, and Francisco Martín Grupo de Robótica, Universidad Rey Juan Carlos Móstoles, Madrid (Spain) {barrera,jmplaza,vmo,fmartin}@gsyc.escet.urjc.es

### UW CSE Technical Report 03-06-01 Probabilistic Bilinear Models for Appearance-Based Vision

UW CSE Technical Report 03-06-01 Probabilistic Bilinear Models for Appearance-Based Vision D.B. Grimes A.P. Shon R.P.N. Rao Dept. of Computer Science and Engineering University of Washington Seattle, WA

### Microcontrollers, Actuators and Sensors in Mobile Robots

SISY 2006 4 th Serbian-Hungarian Joint Symposium on Intelligent Systems Microcontrollers, Actuators and Sensors in Mobile Robots István Matijevics Polytechnical Engineering College, Subotica, Serbia mistvan@vts.su.ac.yu

### Optimal 3D Point Clouds from Mobile Laser Scanning

Optimal 3D Point Clouds from Mobile Laser Scanning The image depicts how our robot Irma3D sees itself in a mirror. The laser looking into itself creates distortions as well as changes in intensity that

### An appearance-based visual compass for mobile robots

An appearance-based visual compass for mobile robots J. Sturm a,, A. Visser b, a Lehrstuhl Autonome Intelligente Systeme, Institut für Informatik, Albert-Ludwigs-Universität Freiburg, Germany b Intelligent

### Derek Schmidlkofer PH 464. Project: IR Sensors

Derek Schmidlkofer PH 464 Project: IR Sensors Robots need devices that allow them to interpret and interact with the world around them. Infrared sensors are an effective method of accomplishing this task.

### A PHOTOGRAMMETRIC APPRAOCH FOR AUTOMATIC TRAFFIC ASSESSMENT USING CONVENTIONAL CCTV CAMERA

A PHOTOGRAMMETRIC APPRAOCH FOR AUTOMATIC TRAFFIC ASSESSMENT USING CONVENTIONAL CCTV CAMERA N. Zarrinpanjeh a, F. Dadrassjavan b, H. Fattahi c * a Islamic Azad University of Qazvin - nzarrin@qiau.ac.ir

### Simultaneous Kriging-based sampling for optimization and uncertainty propagation

Simultaneous Kriging-based sampling for optimization and uncertainty propagation 23.11. NKO workshop, Bern Janis Janusevskis, Rodolphe Le Riche Ecole des Mines de Saint-Etienne (EMSE) 1 Outline 1. Introduction

### Robotic Mapping: A Survey

Robotic Mapping: A Survey Sebastian Thrun February 2002 CMU-CS-02-111 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract This article provides a comprehensive introduction

### CS 2750 Machine Learning. Lecture 1. Machine Learning. http://www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning.

Lecture Machine Learning Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square, x5 http://www.cs.pitt.edu/~milos/courses/cs75/ Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu 539 Sennott

### Some primary musings on uncertainty and sensitivity analysis for dynamic computer codes John Paul Gosling

Some primary musings on uncertainty and sensitivity analysis for dynamic computer codes John Paul Gosling Note that this document will only make sense (maybe) if the reader is familiar with the terminology

### Basics of Statistical Machine Learning

CS761 Spring 2013 Advanced Machine Learning Basics of Statistical Machine Learning Lecturer: Xiaojin Zhu jerryzhu@cs.wisc.edu Modern machine learning is rooted in statistics. You will find many familiar

### Deterministic Sampling-based Switching Kalman Filtering for Vehicle Tracking

Proceedings of the IEEE ITSC 2006 2006 IEEE Intelligent Transportation Systems Conference Toronto, Canada, September 17-20, 2006 WA4.1 Deterministic Sampling-based Switching Kalman Filtering for Vehicle

### ASSESSMENT OF VISUALIZATION SOFTWARE FOR SUPPORT OF CONSTRUCTION SITE INSPECTION TASKS USING DATA COLLECTED FROM REALITY CAPTURE TECHNOLOGIES

ASSESSMENT OF VISUALIZATION SOFTWARE FOR SUPPORT OF CONSTRUCTION SITE INSPECTION TASKS USING DATA COLLECTED FROM REALITY CAPTURE TECHNOLOGIES ABSTRACT Chris Gordon 1, Burcu Akinci 2, Frank Boukamp 3, and

### MSc in Autonomous Robotics Engineering University of York

MSc in Autonomous Robotics Engineering University of York Practical Robotics Module 2015 A Mobile Robot Navigation System: Labs 1a, 1b, 2a, 2b. Associated lectures: Lecture 1 and lecture 2, given by Nick

### Sensors in robotic arc welding to support small series production

Sensors in robotic arc welding to support small series production Gunnar Bolmsjö Magnus Olsson Abstract Sensors to guide robots during arc welding have been around for more than twenty years. However,

### Ensemble-based reservoir history matching for complex geology and seismic data. Yanhui Zhang

Ensemble-based reservoir history matching for complex geology and seismic data Yanhui Zhang Outline 1 Overview 2 Part I: Updating of geologic facies models with ensemble-based methods 3 Part II: Data assimilation

### PHASE ESTIMATION ALGORITHM FOR FREQUENCY HOPPED BINARY PSK AND DPSK WAVEFORMS WITH SMALL NUMBER OF REFERENCE SYMBOLS

PHASE ESTIMATION ALGORITHM FOR FREQUENCY HOPPED BINARY PSK AND DPSK WAVEFORMS WITH SMALL NUM OF REFERENCE SYMBOLS Benjamin R. Wiederholt The MITRE Corporation Bedford, MA and Mario A. Blanco The MITRE

### C4 Computer Vision. 4 Lectures Michaelmas Term Tutorial Sheet Prof A. Zisserman. fundamental matrix, recovering ego-motion, applications.

C4 Computer Vision 4 Lectures Michaelmas Term 2004 1 Tutorial Sheet Prof A. Zisserman Overview Lecture 1: Stereo Reconstruction I: epipolar geometry, fundamental matrix. Lecture 2: Stereo Reconstruction

### Combining information from different survey samples - a case study with data collected by world wide web and telephone

Combining information from different survey samples - a case study with data collected by world wide web and telephone Magne Aldrin Norwegian Computing Center P.O. Box 114 Blindern N-0314 Oslo Norway E-mail:

### North Texas FLL Coaches' Clinics. Advanced Programming October Patrick R. Michaud republicofpi.org

North Texas FLL Coaches' Clinics Advanced Programming October 2014 Patrick R. Michaud pmichaud@pobox.com republicofpi.org Goals Get more consistence performance Learn advanced programming techniques Share

### Position Estimation Using Principal Components of Range Data

Position Estimation Using Principal Components of Range Data James L. Crowley, Frank Wallner and Bernt Schiele Project PRIMA-IMAG, INRIA Rhône-Alpes 655, avenue de l'europe 38330 Montbonnot Saint Martin,

### Indoor Localization Using Step and Turn Detection Together with Floor Map Information

Indoor Localization Using Step and Turn Detection Together with Floor Map Information University of Applied Sciendes Würzburg-Schweinfurt, Pattern Recognition Group, University of Siegen In this work we

### EE 570: Location and Navigation

EE 570: Location and Navigation On-Line Bayesian Tracking Aly El-Osery 1 Stephen Bruder 2 1 Electrical Engineering Department, New Mexico Tech Socorro, New Mexico, USA 2 Electrical and Computer Engineering

### 3D Scanning for Virtual Shopping. Dr. Jürgen Sturm Group Leader RGB-D

3D Scanning for Virtual Shopping Dr. Jürgen Sturm Group Leader RGB-D metaio GmbH Augmented Reality with the Metaio SDK 2 Metaio SDK SDK for developers to create augmented reality apps Supports ios, Android,

### Uncertainty, Stochastics & Sensitivity Analysis

Uncertainty, Stochastics & Sensitivity Analysis Nathaniel Osgood CMPT 858 March 17, 2011 Types of Sensitivity Analyses Variables involved One-way Multi-way Type of component being varied Parameter sensitivity

### The Basics of Robot Mazes Teacher Notes

The Basics of Robot Mazes Teacher Notes Why do robots solve Mazes? A maze is a simple environment with simple rules. Solving it is a task that beginners can do successfully while learning the essentials

### Objectives. Understand various definitions related to sensing/perception. Understand variety of sensing techniques

Sensing/Perception Objectives Understand various definitions related to sensing/perception Understand variety of sensing techniques Understand challenges of sensing and perception in robotics Old View