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Journal article
Sensor fusion for mobile robot navigation
Proceedings of the IEEE, v 85(1), pp 108-119
Jan 1997
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We review techniques for sensor fusion in robot navigation, emphasizing algorithms for self-location. These find use when the sensor suite of a mobile robot comprises several different sensors, some complementary and some redundant. Integrating the sensor readings, the robot seeks to accomplish tasks such as constructing a map of its environment, locating itself in that map, and recognizing objects that should be avoided or sought. The review describes integration techniques in two categories: low-level fusion is used for direct integration of sensory data, resulting in parameter and state estimates; high-level fusion is used for indirect integration of sensory data in hierarchical architectures, through command arbitration and integration of control signals suggested by different modules. The review provides an arsenal of tools for addressing this (rather ill-posed) problem in machine intelligence, including Kalman filtering, rule-based techniques, behavior based algorithms, and approaches that borrow from information theory, Dempster-Shafer reasoning, fuzzy logic and neural networks.
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Details
- Title
- Sensor fusion for mobile robot navigation
- Creators
- M Kam - Drexel UniversityXiaoxun Zhu - Drexel UniversityP Kalata - Drexel University
- Publication Details
- Proceedings of the IEEE, v 85(1), pp 108-119
- Publisher
- IEEE
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- [Retired Faculty]
- Web of Science ID
- WOS:A1997WB96600009
- Scopus ID
- 2-s2.0-0030785214
- Other Identifier
- 991019168110604721
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- Web of Science research areas
- Engineering, Electrical & Electronic