SLAM Techniques Application for Mobile Robot in Rough Terrain by Andrii Kudriashov & Tomasz Buratowski & Mariusz Giergiel & Piotr Małka

Author:Andrii Kudriashov & Tomasz Buratowski & Mariusz Giergiel & Piotr Małka
Language: eng
Format: epub
ISBN: 9783030489816
Publisher: Springer International Publishing

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Thus, into particle filter solution like MCL [10] into each of the samples, the EKF’s belief updated with landmarks is added. The advantages of this approach is in a faster computing time (EKF – ), explained by linearization issues excluding and multimodal distribution that can be applied for both grids and landmark-based maps. However, the biggest disadvantage of this method is in losing path history information during resampling step that makes this solution application in systems where state is depended from its history harder.

The Rao–Blackwellized approach in particle filters is represented by a wide spectrum of different implementations like Unscented FastSLAM [37], DP-SLAM, that is recommended by authors for explorations [38], CoreSLAM, which is based on only laser scan mapping, TinySLAM [39]—the shortest SLAM algorithm and optimized for grids maps GMapping [40]. Last algorithm was proposed by Grisetti et al. in [41] and was used as the basis or reference in few of the book related researches [42, 43]. The map created in by GMapping algorithm with additional pose estimation by EKF combined the wheel odometry and IMU localization techniques, as is shown on Fig. 3.17.

Fig. 3.17An example SLAM based on particle filter usage (GMapping additionally enhanced by EKF for pose estimation): a Test environment simulated in V-REP and b its result map calculated by particle filter approach [43]

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