yingkunwu / fastslam

This project implements grid-based FastSLAM1.0 and FastSLAM2.0 algorithms to solve SLAM problem in a simulated environment.

FastSLAM

Final project for UMN CSCI 5512 Artificial Intelligence II course

Introduction

The purpose of this project is to simulate a Simultaneously Localization and Mapping problem by allowing a robot with beam sensors to navigate in a 2D grid map. In this project, I implemented both the famous particle filter based SLAM algorithm, FastSLAM1.0 and FastSLAM2.0, in python. The program is able to work in any given grid map as long as the environment is static and each grid is either free (0) or occupied (1).

Usage

Run the following command to run FastSLAM1.0:

python fastslam1.py -m [given map] -p [number of particles]

Similarly, run the following command to run FastSLAM2.0:

python fastslam2.py -m [given map] -p [number of particles]

For now the given map should be either 'scene-1' or 'scene-2', and the number of particles should be a integer.

Result

FastSLAM 1.0

FastSLAM 2.0

References

• Michael Montemerlo, Sebastian Thrun, Daphne Koller, and Ben Wegbreit. Fastslam: A factored solution to the simultaneous localization and mapping problem.
• Giorgio Grisetti, Cyrill Stachniss, and Wolfram Burgard. Improved techniques for grid mapping with rao- blackwellized particle filters.
• https://github.com/udacity/RoboND-MCL-Lab
• https://github.com/ivanwong9290/CMU_16833_SLAM/tree/ae5a127fadb64b9cf3e2de51b26d2ad339e50b6a
• https://bostoncleek.github.io/project/fast-slam
• https://github.com/p16i/particle-filter/tree/5d8a82ea95b93b2d868435a797e5c8c6bdc5de56