Sensors and Methods for Mobile Robot Positioning

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Systems-at-a-Glance Tables Landmark Positioning Name Computer Onboard Features used Accuracy - Accuracy - Sampling Features Effective Reference Components position [mm] o orientation [ ] Rate [Hz] Range, Notes Model based vision TRC LabMate 512×512 gray-level Corners of the room 100 mm ±3º o 3-D orientation error
Systems-at-a-Glance Tables Landmark Positioning Name Computer Onboard Features used Accuracy - Accuracy - Sampling Features Effective Reference Components position [mm] o orientation [ ] Rate [Hz] Range, Notes Scanning laser 0.5%-5% 1 to 10 kHz or External, local, $10K- 300 m [Parish and Grabble, rangefinder analog 100K 1993], Omnitech Robotics, Inc. Scanning IR range- 1%-10% 100-1000 or External, local, $5K- 5-50 m [Parish and Grabble, finder analog 20K 1993], Omnitech Robotics, Inc. Scanning (or ar- 1%-10% 1-100 External, local, $100- 1-10 m [Parish and Grabble, rayed) ultrasonic 5K 1993], Omnitech rangefinder Robotics, Inc. Visual 1%-20% 0.1-100 External, local, $500- 1-10000 [Parish and Grabble, 50K 1993], Omnitech Robotics, Inc. Navigation by TRC Labmate Cohu CCD camera, Integrates position esti- [D'Orazio et al., multi-sensory inte- f=16 mm mates from vision sys- 1993] gration dead reckoning tem with odometry us- CNR-IESI ing Kalman filter framework Laserradar and Tricycle robot 24 sonars. four la- Utilizes heterogeneous [Buchberger et al., sonar based world ser rangefinders, info from laser radar 1993] modeling o rotate at 360 /s, and sonars Kaiserslautern Uni- each scan 720 versity range points Vision directed Sun Sparc for Vision camera Doors, columns ±5.0 cm 2.0º 2 Hz 3-D University of Waternavigation vision, Micro- Convex and loo [Wong and Gao, VAX as host, concave poly- 1992] ROBMAC100 gons tricycle type vehicle Robot localization Sun-3 for local- One rotating sonar Geometric beacon - 1 Hz EKF utilizes matches [Leonard and by tracking geo- ization or six fixed sonars naturally occurring between observed geo- Durrant-Whyte, metric beacons Sun-4 vehicle environment fea- metric beacons and a 1991] control ture priori map of beacon locations University of Oxford Position estimation Differential-drive 756×581 CCD Vertical edges and 40 mm 0.5º 2-D - Realistic odom- Extended Kalman [Chenavier and using vision and vehicle camera stored map etry model and its un- filter to correct the Crowley, 1992] odometry 386 PC f=12.5 mm certainty is used to de- vehicle pose from LETI-DSYS tect and calculate posi- the error between tion update fused with observation the observed and estimate angle to each landmark 229
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7KH8QLYHUVLW\RI0LFKLJDQ Where am I?
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Acknowledgments This research was s
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2.4.2.2 Watson Gyrocompass ........
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6.4 Summary .......................
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INTRODUCTION Leonard and Durrant-Wh
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Part I Sensors for Mobile Robot Pos
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14 Part I Sensors for Mobile Robot
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CHAPTER 2 HEADING SENSORS Heading s
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6 42 Part I Sensors for Mobile Robo
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CHAPTER 5 ODOMETRY AND OTHER DEAD-R
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Page 178 and 179: R e t r o r e f l e c t i v e m a r
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Page 236 and 237: REFERENCES 1. Abidi, M. and Chandra
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Page 262 and 263: 262 Index AC ..............47, 59,
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278 Index Weiman ..................
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280 Index Video Index This CD-ROM c
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282 Index Paper 52 Paper 53 Paper 5
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