Particle filter SLAM on FPGA: a case study on robot@factory competition

Particle filters are sequential Monte Carlo estimation methods with applications in the field of mobile robotics for performing tasks such as tracking, simultaneous localization and mapping (SLAM) and navigation, by dealing with the uncertainties and/or noise generated by the sensors as well as with the intrinsic uncertaintie of the environment. This work presents a field programmable gate arrays (FPGA) implementation of a particle filter applied to SLAM problem based on a low cost Neato XV-11 laser scanner sensor. Post processing is performed on data provided by a realistic simulation of a differential robot, equipped with a hacked Neato XV-11 laser scanner, that navigates in the Robot@Factory competition maze. The robot was simulated using SimTwo, which is a realistic simulation software that can support several types of robots. The simulator provides the robot ground truth, odometry and the laser scanner data. The results achieved from this study confirmed the possible use such low cost laser scanner for different robotics applications.The authors would like to thank the Universitat Autònoma de Barcelona for the financial support to conduct this research work in collaboration with the Polytechnic Institute of Bragança (IPB). The authors also express their gratitude to the IPB for inviting Mr. Biruk Getachew Sileshi to perform this work as as an invited researcher for the duration of the research work. The authors would also acknowledge the project TIN2014-56919-C3-2-R DETECCION Y MONITOREO DE INCENDIOS USANDO IMAGENES MULTIESPECTRALES for the support provided to this work.