Data Fusion in Robotics and Machine Intelligence
This book addresses the techniques for modelling and integration of data provided by different sensors within robotics, and knowledge sources within machine intelligence. Professionals within robotics and machine intelligence aim to capture state-of-the-art technology in data/sensor fusion and try to give a unified vision of the field, presented from both the theoretical and practical angles.
- Hardback | 560 pages
- 152.4 x 228.6 x 25.4mm | 628.22g
- 01 Dec 1992
- Elsevier Science Publishing Co Inc
- Academic Press Inc
- San Diego, United States
Table of contents
Data fusion and sensor integration - state-of-the-art 1990s, R.C. Luo and M.G. Gay; multi-source spatial fusion using Bayesian reasoning, A. Elfes; multi-sensor strategies using Dempster/Shafer belief accumulation, S.A. Hutchinson and A.C. Kak; data fusion techniques using robust statistics, R. McKendall and M. Mintz; recursive fusion operators - desirable properties and illustrations, Y. Chen and R.L. Kashyap; distributed data fusion using Kalman filtering - a robotics application, C. Brown, et al; kinematic and satistical models for data fusion using Kalman filtering, T.J. Broida and S.S. Blackman; least-squares fusion of multi-sensory data, R.O. Eason and R.C. Gonzalez; fusion of multi-dimensional data using regularization, M.A. Abidi; geometric fusion - minimizing uncertainty ellipsoid volumes, Y. Nakamura; combination of fuzzy information in the framework of possibility theory, D. Dubois and H. Prade; data fusion - a neural networks implementation, T.L. Huntsberger.