How the location of the range sensor affects EKF-based localization

The article proposes a solution to map-based self-localization for an autonomous robot operating in cluttered and crowded environments. To detect features for localization, 2D laser range-finders traditionally scan a plane parallel to the floor. This work hypothesizes the existence of a \u201clow frequency cross-section\u201d of the 3D Workspace where cluttered and dynamic environments become \u201cmore regular\u201d and \u201cless dynamic\u201d. The contribution of the article is twofold. First, an \u201cunevenness index\u201d U is introduced to quantitatively measure the complexity of the environment as it would be perceived if the laser range-finder were located at different heights from the floor. The article shows that, by choosing the laser scanning plane to statistically minimize U (in most cases, above the heads of people), it is possible to deal more efficiently with non-linearities in the measurement model, moving objects and occluded features. Second, it is demonstrated that, when adopting an extended Kalman filter for position tracking (a very common and widely used technique in real-world scenarios), the a posteriori covariance of the estimated robot pose converges faster, on average, when U is lower, which leads to better localization performance. Experimental results show hours of continuous robot operation in real-world, cluttered and crowded environments