Particle Filter SLAM on FPGA: A Case Study on Robot@Factory Competition

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Sileshi, BG (Author) Other; Oliver, J (Author) Other; Toledo, R (Author) Other; Goncalves, J (Author) Other; Paulo Gomes da Costa (Author) FEUP

Conference proceedings International

Title: ROBOT 2015: SECOND IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, VOL 1

Pages: 411-423

2nd Iberian Robotics Conference (ROBOT)

Lisbon, PORTUGAL, NOV 19-21, 2015

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 10 Citations

Other information

Authenticus ID: P-00K-2GJ

DOI: 10.1007/978-3-319-27146-0_32

Abstract (EN): 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 uncertainties 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13