Paper:RO-SLAM with SOG

"Efficient Probabilistic Range-Only SLAM", IROS 2008 - PDF - Slides PPT
Abstract: This work addresses Range-Only SLAM (RO-SLAM) as the Bayesian inference problem of sequentially tracking a vehicle while estimating the location of a set of beacons without any prior information. The only assumptions are the availability of odometry and a range sensor able of identifying the different beacons. We propose exploiting the conditional independence between each beacon distribution within a Rao-Blackwellized Particle Filter (RBPF) for maintaining independent Sum of Gaussians (SOGs) for each map element. It is shown then that a proper probabilistic observation model can be derived for online operation with no need for delayed initializations. We provide a rigorous statistical comparison of this proposal with previous work of the authors where a Monte-Carlo approximation was employed instead for the conditional densities. As verified experimentally, this new proposal represents a significant improvement in accuracy, computation time, and robustness against outliers.

Bibtex:

@conference{blanco2008epr,
  title={{Efficient Probabilistic Range-Only SLAM}},
  author={Blanco, J.L. and Fernandez-Madrigal, J.A. and Gonzalez, J.},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems},
  pages={1017--1022},
  year={2008}
}

2. Source code

The programs employed in the experimental results of this paper are:

• simul-beacons: This program generates a random 2D or 3D map of beacons and produces the simulated observations of a robot according to some predefined path.
• rbpf-slam: This application is actually a front-end to the class mrpt::slam::CMetricMapBuilderRBPF. All the parameters to the algorithm are passed through a configuration file in the command line. The filter processes actions and observations from a rawlog file and optionally generates a number of files describing the evolution of the filter and the maps.

More concretely, the SOG approach described in this paper is implemented in the classes: mrpt::slam::CBeacon, and mrpt::slam::CBeaconMap.

 

3. Reproduce an example run of RO RBPF-based SLAM

Open up a terminal and execute:

 $ cd MRPT/share/mrpt/config_files/rbpf-slam/
 $ rbpf-slam RO-SLAM_simulatedData_SOG.ini

You can open this .ini file and tune some parameters to see their effects.

4. Experiments

The next video shows: (i) results for 2D RO-SLAM with a simulated sensor, and (ii) results from real UWB radio data in 3D. Refer to the paper for more details on these results.

The full quality video is also available for download: RESULTS_ROSLAM_SOG.avi (50.6Mb).

This animation illustrates the symmetries found in RO-SLAM for a robot moving in a straight line. When the robot turns a corner, the symmetry is broken, but it still remains with respect to the plane the robot is moving (above and below the movement plane).