Where am I? 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 Recognize world Stereo cameras Long, near vertical 1000 real-world data Least-squares to [Braunegg, 1993] location with ste- stereo features recognition test, under find the best fit of MITRE Corp. reo vision 10% false negative, zero model to data and false positive evaluate that fit Environment learn- Omnidirectional a ring of 12 sonars Left wall, right Dynamic landmark de- Learn the large- [Mataric, 1990] ing using a distrib- three-wheeled and a compass wall, corridors tection utilizing robot's space structure of MIT uted repre- base motion environment by sentation recording its permanent features Localization in Motorola A ring of 24 sonars Classify objects 0.1 Hz Positions resulting from Each mapping of [Holenstein et al., structured environ- M68020 into edges, corners, all possible mappings two model objects 1992] ment walls, and un- are calculated and then onto two reference Swiss Federal Inst. of known objects analyzed for clusters objects correspond Technology The biggest cluster is to a certain robot assumed to be at the position true robot position Localization using SUN 4 Linear array of Local map:
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 Blanche MC68020 Optical range- 24 line-segment 6 in path following Position up- (1) Least-square for Segments [Cox, 1991] Tricycle-type finder, res.=1 in at environments map date every 8 s data and model match- Assume the dis- NEC Research Instimobile robot 5 ft, 1000 sam- for a 300×200 in for a 180 ing placement between tute ples/rev. in one s. room points image (2) Combine odometry the data and model Odometry and a map of and matching for better is small 24 lines. 2-D position estimate using map. maximum likelihood Range map pose SPARC1+ 1-D Laser range Line segment, cor- Mean error Max under 1.2º Feature-based: 1000 points/rev. [Schaffer et al., estimation finder ner Feature-based: 60 for both 0.32 s Iconic approach matches every range data point 1992] 1000 points/rev Iconic estimator: 40 Iconic: 2 s to the map rather than condensing data into a CMU In a 10×10 m space small set of features to be matched to the map Positioning using A rotatable ring of Line segments 3-5 cm Classification of data Clustering sensor [MacKenzie and model-based maps 12 polaroid sonars Coverge if initial esti- points data points. Dudek, 1994] mate is within 1 me- Weighted voting of cor- Line fitting. McGill University ters of the true posi- rection vectors tion Positioning using INMOS-T805 Infrared scanner Line segment The variance never Kalman filter position When scans were [Borthwick et al., optical range data transputer exceeds 6 cm estimation taken from 1994] Line fitting erronrous pos. University of Oxford Matching, only good matches consismatches are accepted tently fail World modeling A ring of 24 sonars Line segments x=33 mm 0.20º A model for Extracting line segments Matching includes: [Crowley, 1989] and localization covariance: 1 covariance: the uncertainty from adjacent collinear orientation, LIFIT(IMAG) using sonar rang- y=17 mm 17.106 in sonars, and range measurements and collinearity, and ing covariance: 1 the projection matching these line seg- overlap by comparof range mea- ments to a stored model ing one of the pasurement into rameters in segment external Carte- representation sian coordinate 2-D laser range- Sun Sparc Cyclone 2-D laser Local map: line Max. 5 cm On SUN Matching: remove seg- Local map: [Gonzalez et al., finder map build- rangefinder accu- segment map average 3.8 cm Sparc, 80 ms ment already in the clustering 1994] ing racy ±20 cm, range Global map: line for local map global map from local clustering segmen- Universidad de 50 m segments building and map, add new segment tation malaga 135 ms for line fitting global map update 231
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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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Excitation actuator Metglas reed Tu
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CHAPTER 5 ODOMETRY AND OTHER DEAD-R
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R e t r o r e f l e c t i v e m a r
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Page 184 and 185: CHAPTER 8 MAP-BASED POSITIONING Map
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Page 222 and 223: Systems-at-a-Glance Tables Odometry
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Page 236 and 237: REFERENCES 1. Abidi, M. and Chandra
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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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