Abstract
We propose a hybrid filter based SLAM (Simultaneous Localization and Mapping) for a mobile robot to compensate for the EKF (Extended Kalman Filter) based SLAM error inherently caused by the linearization process. A mobile robot autonomously explores the environment by interpreting the scene, building an appropriate map, and localizing itself relative to this map. A probabilistic approach has dominated the solution to the SLAM problem. This solution is a fundamental requirement for robot navigation. The EKF algorithm with a RBF (Radial Basis Function) has some advantages in handling a robotic system having nonlinear dynamics because of the learning property of neural networks. We modified an already developed Matlab simulation source for the hybrid filter-SLAM for simulation and comparison. The simulation results showed the effectiveness of the proposed algorithms as compared with an EKF-based SLAM.
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Choi, KS., Song, BK., Lee, SG. (2009). Hybrid Filter Based Simultaneous Localization and Mapping for a Mobile Robot. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_28
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DOI: https://doi.org/10.1007/978-3-642-01513-7_28
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