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Simultaneous Localization And Mapping: A feature-based probabilistic approach

100%

Skrzypczyński P.

International Journal of Applied Mathematics and Computer Science

2009

tom 19

nr 4

575-588

This article provides an introduction to Simultaneous Localization And Mapping (SLAM), with the focus on probabilistic SLAM utilizing a feature-based description of the environment. A probabilistic formulation of the SLAM problem is introduced, and a solution based on the Extended Kalman Filter (EKF-SLAM) is shown. Important issues of convergence, consistency, observability, data association and scaling in EKF-SLAM are discussed from both theoretical and practical points of view. Major extensions to the basic EKF-SLAM method and some recent advances in SLAM are also presented.

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Efficient RGB-D data processing for feature-based self-localization of mobile robots

88%

Kraft M., Nowicki M., Penne R., Schmidt A., Skrzypczyński P.

International Journal of Applied Mathematics and Computer Science

2016

tom 26

nr 1

63-79

The problem of position and orientation estimation for an active vision sensor that moves with respect to the full six degrees of freedom is considered. The proposed approach is based on point features extracted from RGB-D data. This work focuses on efficient point feature extraction algorithms and on methods for the management of a set of features in a single RGB-D data frame. While the fast, RGB-D-based visual odometry system described in this paper builds upon our previous results as to the general architecture, the important novel elements introduced here are aimed at improving the precision and robustness of the motion estimate computed from the matching point features of two RGB-D frames. Moreover, we demonstrate that the visual odometry system can serve as the front-end for a pose-based simultaneous localization and mapping solution. The proposed solutions are tested on publicly available data sets to ensure that the results are scientifically verifiable. The experimental results demonstrate gains due to the improved feature extraction and management mechanisms, whereas the performance of the whole navigation system compares favorably to results known from the literature.

Ograniczanie wyników

2 International Journal of Applied Mathematics and Computer Science

2 Skrzypczyński P.

1 Kraft M.

1 Nowicki M.

1 Penne R.

1 Schmidt A.

1 2016

1 2009

Wyniki wyszukiwania

Simultaneous Localization And Mapping: A feature-based probabilistic approach

Efficient RGB-D data processing for feature-based self-localization of mobile robots