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. However, there are many important applications of mobile robots where maps need to be built of complex environments, consisting of composite features, from noisy sensor data. 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.