PPT-An Inexpensive Method for Evaluating the Localization Performance of a Mobile Robot Navigation

Harsha Kikkeri Gershon Parent Mihai Jalobeanu and Stan Birchfield Microsoft Robotics Motivation Goal Automatically measure performance of mobile robot navigation system Purpose Internal comparison how is my system improving over time. of Computer Science and Engineering National Institute of Technology Tiruchirapalli Dept of Electronics and Communication Engineering National Institute of Technology Tiruchirapalli Dept of Computer Science and Engineering Recon64257gurable and Inte Mechanical Improvements. Software. The software is written in C++ and Python using the Robot Operating System (ROS) framework. The ROS tool, . rviz. , is utilized to visualize the path planning algorithms. Outline. 1. Introduction. 2. The Bayes Filter. 3. Non Parametric Filters. 4. . Gausian. Filters. 5. EKF Map Based Localization. 6. EKF Feature-based SLAM. 7. EKF Pose-based SLAM. 8. Advanced SLAM Concepts. – . some . issues in   controller design and . implementation. L. Huang. School of Engineering and Advanced Technology Massey University. Outlines. Introduction. Target . tracking control schemes based on . Mobile Agent Cloning for . Servicing . Networked . Robots . 2. . STIGMERGICALLY CONTROLLING A POPULATION OF. HETEROGENEOUS MOBILE AGENTS USING CLONING RESOURCE. 4. . ROBOTICS . LABORATORY . www.iitg.ernet.in/cse/robotics/. David Johnson. cs6370. Basic Problem. Go from this to this. [Thrun, Burgard & Fox (2005)]. . Kalman . Filter. [Thrun, Burgard & Fox (2005)]. . Kalman Limitations. Need initial state and confidence. Paper – Stephen Se, David Lowe, Jim Little. Presentation – Nicholas Moya. 1. Decoding the Title. Visual SLAM using SIFT features as landmarks. SLAM: Simultaneous Localization and Mapping. SIFT: Scale-Invariant Feature transform. Matthai Philipose, Kenneth P Fishkin, . Dieter Fox, Dirk Hahnel, Wolfram Burgard. Presenter: Aniket Shah. Outline. Introduction. Related Work. Probabilistic Sensor Model. Mapping. Localization. Experimental Results. Mohan . Muppidi, . Joaquin . Labrado. ACE Laboratory, UTSA. Overview (. cntd. ..). 2. Image Transfer. Robot. Cloud. Start. Introduction. Two . questions . help humans navigate their environment. “. Matthew Thompson, UF. matthewbot@ufl.edu. Prolific Authors. Important Papers. Prolific Institutions. Title. Year. Times Cited. Institutions or. Organizations. Simultaneous map building and localization for an autonomous mobile. Pavel. . Simsa. Avaya. Agenda. Some General Intro. Mobile. Development Issues. Mobile Localization Issues. Mitigations. Q & A. Quick check. How many of you have:. A smartphone?. iPhone?. BlackBerry?. System) . – . IX-100. MS Windows CE 6.0 . core. High sensitive. . GPS . receiver . (. UBLOX 6 . AssistNow. ™ Autonomous. ). Telechips. . TCC8901 . Dual Core . 1.32 . GHz CPU . (. 720Mhz . Yaniv . Shachor. Asaf Brezler. Localization – Introduction & benefits. Localization of the robot is one of the main keys for the functionality of the . robot. It . helps the robot understand . its position and derive the consequences following that.. �� &#x/MCI; 0 ;&#x/MCI; 0 ;NISTIR8243A Test Method for MeasuringResponseRobot Performance UnderAttenuatedRadio ConditionsKenneth E. KimbleRichard CandellKamel S. SaidiIntelligen