Resumen:
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[EN] In this paper, we present a set of robust and
efficient algorithms with O(N) cost for the solution of the
Simultaneous Localization And Mapping (SLAM) problem of
a mobile robot. First, we introduce a novel object ...[+]
[EN] In this paper, we present a set of robust and
efficient algorithms with O(N) cost for the solution of the
Simultaneous Localization And Mapping (SLAM) problem of
a mobile robot. First, we introduce a novel object detection
method, which is mainly based on multiple line fitting method
for landmark detection with regular constrained angles. Second,
a line-based pose estimation method is proposed, based on LeastSquares (LS). This method performs the matching of lines,
providing the global pose estimation under assumption of known
Data-Association. Finally, we extend the FastSLAM (FActored
Solution To SLAM) algorithm for mobile robot self-localisation
and mapping by considering the asynchronous sampling of
sensors and actuators. In this sense, multi-rate asynchronous
holds are used to interface signals with different sampling rates.
Moreover, an asynchronous fusion method to predict and update
mobile robot pose and map is also presented. In addition to
this, FastSLAM 1.0 has been also improved by considering the
estimated pose with the LS-approach to re-allocate each particle
of the posterior distribution of the robot pose. This approach has
a lower computational cost than the original Extended Kalman
Filtering (EKF) approach in FastSLAM 2.0. All these methods
have been combined in order to perform an efficient and robust
self-localization and map building process. Additionally, these
methods have been validated with experimental real data, in
mobile robot moving on an unknown environment for solving
the SLAM problem.
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Descripción:
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