Affiliations: Dipartimento di Elettronica e Informazione,
Politecnico di Milano, P.za Leonardo da Vinci, 32, 20133 Milano, Italy. Fax:
+39 02 23993411; E-mail: Vincenzo.Caglioti@polimi.it
Abstract: This paper addresses the self-localization of a mobile robot,
equipped with a laser range scanner, navigating within a curvilinear
environment. Specifically, we present a technique for updating a current pose
estimate (e.g., deriving from dead-reckoning) by means of a single range
measure. Current approaches to this problem, based on the Extended Kalman (EK)
estimator, often yield unreliable results due to linearization errors, inherent
in curvilinear environments. Therefore, a second-order method is proposed, in
which the measurement result is approximated in terms of a two-degree function
of the pose estimate error (instead of a linear one). Experiments conducted on
a real robot navigating within a curvilinear environment are reported, showing
that the errors in the updated estimate, provided by the proposed method, are
significantly lower than the errors in the EK estimate.
Keywords: Mobile robots, self-localization, pose estimate updating, curvilinear environments, range measures, extended kalman filtering, nonlinear pose estimation
