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Computational cost analysis of extended Kalman filter in simultaneous localization and mapping (EKF-SLAM) problem for autonomous vehicle
| Content Provider | Semantic Scholar |
|---|---|
| Author | Samsuri, Saiful Bahri Zamzuri, Hairi Rahman, Mohd Azizi Abdul Mazlan, Saiful Amri Rahman, Abdul Hadi Abd |
| Copyright Year | 2015 |
| Abstract | Extended Kalman filter (EKF) based solution is one of the most popular techniques for solving simultaneouslocalization and mapping (SLAM) problem. However, previous research showed the implementation of EKF for SLAMsuffered with high computational costs, which affect the performance inreal time application. This paper investigates thecomputational cost performance of an EKF-SLAM algorithm. The analysiswas done by time measurement on sub-stepmotion update and measurement update on EKF by considering the total numbers of landmarks and numerous setting onrange observation distance. The analytical results show that as the number oflandmarks or range observation distancesincreased, the computational cost in measurement update step required more computation time compare to motion updatestep. Furthermore, improvements are needed to optimize the computational cost for the update step. |
| Starting Page | 7764 |
| Ending Page | 7768 |
| Page Count | 5 |
| File Format | PDF HTM / HTML |
| Volume Number | 10 |
| Alternate Webpage(s) | http://www.arpnjournals.com/jeas/research_papers/rp_2015/jeas_0915_2637.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |