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This work describes a hardware architecture for implementing a sequential approach of the Extended Kalman Filter (EKF) that is suitable for mobile robotics tasks, such as self-localization, mapping and navigation problems. As such algorithm is computationally intensive, commonly it is implemented in PC-based platforms to be employed on larger robots. In order to allow the development of small robotic platforms, as those required in many current state of the art research (for instance microrobotics area), small size, low-power and high precision computation capabilities are required. This work proposes a hardware architecture for self-localization task, allowing the fusion of data coming from different sensors such as ultrasonic and laser rangefinder. The architecture is based on a single precision floating-point arithmetic, allowing the operations to be computed with large precision and dynamic range. The system has been adapted for achieving a reconfigurable platform, suitable for research tasks, and applied to a Pioneer 3AT mobile robot platform from Mobile Robots Inc. In order to compare the performance of the system, the same localization technique has been implemented in a high-end PC, as well as using an embedded microprocessor Nios II from Altera Inc. In this paper several metrics have been used to evaluate system performance and suitability, measuring FPGA resources consumption and performance. Finally, the suitability of reconfigurable devices for such kind of applications has been verified and also discussed.

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