Comparison of real-time performance of Kalman filter-based slam methods for unmanned ground vehicle (UGV) navigation

Hakan Temeltaş; Deniz Kavak

doi:10.1117/12.819432

30 April 2009 Comparison of real-time performance of Kalman filter-based slam methods for unmanned ground vehicle (UGV) navigation

Hakan Temeltaş, Deniz Kavak

Author Affiliations +

Proceedings Volume 7332, Unmanned Systems Technology XI; 733222 (2009) https://doi.org/10.1117/12.819432
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

Simultaneous Localization and Mapping (SLAM) using for the mobile robot navigation has two main problems. First problem is the computational complexity due to the growing state vector with the added landmark in the environment. Second problem is data association which matches the observations and landmarks in the state vector. In this study, we compare Extended Kalman Filter (EKF) based SLAM which is well-developed and well-known algorithm, and Compressed Extended Kalman Filter (CEKF) based SLAM developed for decreasing of the computational complexity of the EKF based SLAM. We write two simulation program to investigate these techniques. Firts program is written for the comparison of EKF and CEKF based SLAM according to the computational complexity and covariance matrix error with the different numbers of landmarks. In the second program, EKF and CEKF based SLAM simulations are presented. For this simulation differential drive vehicle that moves in a 10m square trajectory and LMS 200 2-D laser range finder are modelled and landmarks are randomly scattered in that 10m square environment.

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Hakan Temeltaş and Deniz Kavak "Comparison of real-time performance of Kalman filter-based slam methods for unmanned ground vehicle (UGV) navigation", Proc. SPIE 7332, Unmanned Systems Technology XI, 733222 (30 April 2009); https://doi.org/10.1117/12.819432

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KEYWORDS

Filtering (signal processing)

Algorithm development

Computer simulations

Matrices

Mobile robots

Unmanned ground vehicles

Electronic filtering

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