Source localization using particle filtering on FPGA for robotic navigation with imprecise binary measurement

TL;DR

This paper introduces a high-speed FPGA-based particle filter architecture for real-time source localization in robotic navigation, significantly reducing computation time compared to traditional CPU implementations.

Contribution

It presents a novel FPGA implementation of particle filtering with pipelining and parallelization, enabling real-time localization for robotic systems.

Findings

Achieved a processing time of 5.62 microseconds for 1024 particles.

Validated the system on a UGV with light source localization.

Demonstrated scalability and modularity of the FPGA-based design.

Abstract

Particle filtering is a recursive Bayesian estimation technique that has gained popularity recently for tracking and localization applications. It uses Monte Carlo simulation and has proven to be a very reliable technique to model non-Gaussian and non-linear elements of physical systems. Particle filters outperform various other traditional filters like Kalman filters in non-Gaussian and non-linear settings due to their non-analytical and non-parametric nature. However, a significant drawback of particle filters is their computational complexity, which inhibits their use in real-time applications with conventional CPU or DSP based implementation schemes. This paper proposes a modification to the existing particle filter algorithm and presents a highspeed and dedicated hardware architecture. The architecture incorporates pipelining and parallelization in the design to reduce execution time considerably. The design is validated for a source localization problem wherein we estimate the position of a source in real-time using the particle filter algorithm implemented on hardware. The validation setup relies on an Unmanned Ground Vehicle (UGV) with a photodiode housing on top to sense and localize a light source. We have prototyped the design using Artix-7 field-programmable gate array (FPGA), and resource utilization for the proposed system is presented. Further, we show the execution time and estimation accuracy of the high-speed architecture and observe a significant reduction in computational time. Our implementation of particle filters on FPGA is scalable and modular, with a low execution time of about 5.62 us for processing 1024 particles and can be deployed for real-time applications.