Image Processing Reference
In-Depth Information
is essential to rate the data in terms of accuracy and certainty. If the quality of the data
is insufficient, the perception and control strategies of the robot have to be adapted.
4.5. Bibliography
[ABI 92] A BIDI M.A., G ONZALEZ R.C., Data Fusion in Robotics and Machine Intelligence ,
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[BAR 88] B AR -S HALOM Y. , F ORTMANN T., Tracking and Data Association , vol. 179, Aca-
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[CHA 98]
C HAVAND F., C OLLE E., Perception de l'environnement en robotique ,
Paris, 1998.
[CHE 96]
C HECCHIN P., Segmentation d'images de profondeur,
PhD Thesis, Blaise Pascal
University, Clermont-Ferrand, 19 December 1996.
[FRA 00] F RANKEL C., B EDWORTH M.D., “Control, Estimation and Abstraction in Fusion
Architectures: Lessons from Human Information Processing”, International Conference on
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[KHA 99] K HALIL W., Modélisation, identification et commande des robots , Coll. Robotique,
2 nd Ed., Lavoisier, 1999.
[KOK 94] K OKAR M., K IM K., “Centralized Multisensor Fusion Algorithms for Tracking
Applications”, Control engineering practice , vol. 2, no. 5, p. 803-809, 1994.
[KOS 93] K OSAKA A., M ENG M., K AK A.C., “Vision-Guided Mobile Robot Navigation
Using Retroactive Updating of Position Uncertainty”, IEEE International Conference on
Robotics and Automation , Atlanta USA, 1993.
[NIM 00] N IMIER V. , B ASTIÈRE A., C OLIN N., M ORUZZIS M., “MILORD, an Application
of Multifeature Fusion for Radar NCTR”, ISF, Ed., International conference on informa-
tion fusion , Paris, p. WeD1-17-WeD1-24, 2000.
[ROB 02] “Les nouveaux robots”, special edition, La Recherche , February 2002.
[STE 98] S TEINBERG A.N., B OWMAN C.L., W HITE F.E., “Revision to the JDL Data Fusion
Model”, Joint NATO/IRIS Conference , Québec, Canada, 1998.
[TRA 93] T RASSOUDAINE L., Solutions multisensorielles temps réel pour la détection
d'obstacles sur route, PhD Thesis, Blaise Pascal University, Clermont-Ferrand, 16 February
[ZHA 97] Z HAO Y. , Vehicle Location and Navigation Systems, Intelligent Transportation Sys-
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