Adaptive Mobile Robot Navigation and Mapping
| Autor: | C.M. Smith, John J. Leonard, Hans Jacob S. Feder |
|---|---|
| Rok vydání: | 1999 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Applied Mathematics Mechanical Engineering Robotics 02 engineering and technology Simultaneous localization and mapping Mobile robot navigation k-nearest neighbors algorithm Extended Kalman filter 020901 industrial engineering & automation Artificial Intelligence Feature (computer vision) Modeling and Simulation Metric (mathematics) 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Computer vision Artificial intelligence Electrical and Electronic Engineering business Software |
| Zdroj: | The International Journal of Robotics Research. 18:650-668 |
| ISSN: | 1741-3176 0278-3649 |
| DOI: | 10.1177/02783649922066484 |
| Popis: | The task of building a map of an unknown environment and concurrently using that map to navigate is a central problem in mobile robotics research. This paper addresses the problem of how to perform concurrent mapping and localization (CML) adaptively using sonar. Stochastic mapping is a feature-based approach to CML that generalizes the extended Kalman filter to incorporate vehicle localization and environmental mapping. The authors describe an implementation of stochastic mapping that uses a delayed nearest neighbor data association strategy to initialize new features into the map, match measurements to map features, and delete out-of-date features. The authors introduce a metric for adaptive sensing that is defined in terms of Fisher information and represents the sum of the areas of the error ellipses of the vehicle and feature estimates in the map. Predicted sensor readings and expected dead-reckoning errors are used to estimate the metric for each potential action of the robot, and the action that yields the lowest cost (i.e., the maximum information) is selected. This technique is demonstrated via simulations, in-air sonar experiments, and underwater sonar experiments. Results are shown for (1) adaptive control of motion and (2) adaptive control of motion and scanning. The vehicle tends to explore selectively different objects in the environment. The performance of this adaptive algorithm is shown to be superior to straight-line motion and random motion. |
| Databáze: | OpenAIRE |
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