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ISRR
2001
Springer

Towards Robust Data Association and Feature Modeling for Concurrent Mapping and Localization

10 years 5 months ago
Towards Robust Data Association and Feature Modeling for Concurrent Mapping and Localization
One of the most challenging aspects of concurrent mapping and localization (CML) is the problem of data association. Because of uncertainty in the origins of sensor measurements, it is difficult to determine the correspondence between measured data and features of the scene or object being observed, while rejecting spurious measurements. This paper reviews several new approaches to data association and feature modeling for CML that share the common theme of combining information from multiple uncertain vantage points while rejecting spurious data. Our results include: (1) feature-based mapping from laser data using robust segmentation, (2) map-building with sonar data using a novel application of the Hough transform for perception grouping, and (3) a new stochastic framework for making delayed decisions for combination of data from multiple uncertain vantage points. Experimental results are shown for CML using laser and sonar data from a B21 mobile robot.
John J. Leonard, Paul M. Newman, Richard J. Rikosk
Added 30 Jul 2010
Updated 30 Jul 2010
Type Conference
Year 2001
Where ISRR
Authors John J. Leonard, Paul M. Newman, Richard J. Rikoski, José Neira, Juan D. Tardós
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