Enhancing Real-time Simultaneous Localization and Mapping with FPGA-based EKF-SLAM's Hardware Architecture

Abstract

Simultaneous Localization and Mapping enable a mobile robot that is exploring an uncharted environment to localize itself and calculate its path within the map. In the context of green technologies and applications, there is a growing need for efficient SLAM solutions that not only provide accurate localization and mapping but also minimize power consumption. EKF-SLAM is a SLAM solution based on the Extended Kalman Filter, it is well known In the domain of robotics for its ability to handle non-linear models, its ability to handle noise related to the sensors, and its extremely high degree of precision. To guarantee real-time performance, the EKF-SLAM implementation requires a high-performance hardware architecture. In light of this challenge, researchers are thinking about using parallel processing platforms like FPGAs, which can provide the required level of performance and meet strict constraints on physics, computing capacity, and electrical power. This study describes a hardware architecture's implementation design for EKF-SLAM on an FPGA platform. The entire design is built on the Cyclone 2 FPGA, which has a maximum speed of 114 MHz and 18577 LUTs, creating a highly efficient hardware architecture.