Abstract
Simultaneous localization and mapping (SLAM) estimate the position of a mobile robot and the locations of its landmarks from noisy measurements. EKF-SLAM is a very important class of algorithms that uses the extended Kalman filter (EKF) for SLAM. It is among the most widely used algorithms in the field of mobile robotics for localization and mapping missions for its capability to treat noise and its very high level of accuracy. In light of the computational complexity of SLAM algorithms, the major focus of research to meet real-time requirements is on reducing the computational complexity to develop embedded systems based on lower resource and computationally complex platforms. This paper presents the design of the hardware architecture of EKF-SLAM and its implementation on FPGA. The whole design is implemented through Cyclone 2 FPGA; it can achieve 114 MHz and uses 21512 LUTs, which leads to lightweight hardware architecture.
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Hammia, S., Hatim, A., Bouaaddi, A., Haijoub, A. (2022). Lightweight Hardware Architecture of EKF-SLAM and Its FPGA Implementation. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2022. Lecture Notes in Networks and Systems, vol 454. Springer, Cham. https://doi.org/10.1007/978-3-031-01942-5_74
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