Related Topics
Indoor Navigation
Reference Data Sets
Mobility Models
Multipath Mitigation
Human Activity Recognition

Contact
Dr. Patrick Robertson
Dr. Michael Angermann
Maria Garcia Puyol

Last Update:
28. October 2011
Authors:
Patrick Robertson
Michael Angermann
Maria Garcia Puyol
German Aerospace Center (DLR)
Institute of Communications and Navigation
Department Communications Systems
Cooperative Systems Group

FootSLAM and PlaceSLAM - (Movies here)


General information

FootSLAM uses inertial-based measurements such as pedestrian dead reckoning or NavShoe step measurements as the basis for computing the underlying building structure. We have performed experiments where a person wearing a foot mounted IMU walked in our office environment for roughly 10-15 minutes. The data was pre-processed with a Kalman filter to obtain step estimates (see section III C in this paper) and then processed in a sequential Rao-Blackwellized Particle Filter (RBPF) in a typical Fast-SLAM factorization. It's important to point out that no visual or ranging sensors were used; FootSLAM's only features or landmarks are the probability distributions of human motion as a function of location. See the papers below for experimental results and derivation of the Bayesian filter and RBPF.

PlaceSLAM is an extension to odometry based SLAM for pedestrians that incorporates human-reported measurements of recognizable features, or "places" in an environment. PlaceSLAM uses a spatial representation of such places can be built up during the localization process. We see an important application to be in mapping of new areas by volunteering pedestrians themselves, in particular to improve the accuracy of "FootSLAM" which is based on human step estimation (odometry). We distinguish between two important cases which depend on whether the pedestrian is required to report a place's identifier or not. Results based on experimental data show that the approach can significantly improve the accuracy and stability of FootSLAM and this with very little additional complexity. After mapping has been performed, users of such improved FootSLAM maps need not report places themselves - see the new videos below and the upcoming PLANS paper with the derivations and results.

FeetSLAM is simply cooperative FootSLAM. The objective is that data from many walks can be combined to generate a more accurate and more encompassing total FootSLAM map. We have implemented an iterative processing algorithm motivated by Turbo Decoding from channel coding theory that takes maps from one data set as prior maps for other data sets. In two experiments performed so far we show that the algorithm improves the mapping accuracy with increasing iterations. The results were recently published at ION GNSS 2011. See thesis, videos and slides below.

A working set of thoughts and comments on FootSLAM:

Click here for Further explanations and thoughts on FootSLAM.

Please get in touch!

If you are a researcher or group working on FootSLAM we would very much like to hear about your experiences and publications, so please get in touch with us! We are hoping that this page can serve as a pointer to other groups working on this new area.

Some illustrations of FootSLAM:

The result of processing raw pedestrian step measurements from a NavShoe (top left) to a FootSLAM hexagon map (bottom right). Watch the corresponding video
The Dynamic Bayesian Network for FootSLAM used in the formal derivation.
Relative positioning accuracy indoors at two reference points in the corridor. Watch the corresponding video.
Performance for the outdoor-indoor-outdoor scenario where GPS was used outdoors.


PlaceSLAM and an example of a sequence of placestamps with different levels of association..
The Dynamic Bayesian Network for FootSLAM combined with PlaceSLAM.
Absolute positioning accuracy indoors for outdoor/indoor scenario. Watch a video
.Example of a combined FootSLAM and PlaceSLAM map. Watch a video

Movies

NEW: FeetSLAM Movies (shown at ION GNSS 2011, September 2011).

These videos include a comparison with the true layout. The first videos also shows a quantitative evaluation of the ratio of walls or funiture violated by our estimated map. They were produced as part of Maria Garcia Puyol's Master thesis on FeetSLAM performed at DLR and supervised by the University of Malaga, Spain.

FootSLAM Movies with an improved ZUPT

These videos now include a comparison with the true layout and some background data on the experiment and processing.

FootSLAM with PlaceSLAM Movies

These videos now include a comparison with the true layout and some background data on the experiment and processing.

Original FootSLAM Movies (2009; old ZUPT)


Publications

On most papers you can follow the ELIB link to our internal electronic library or just click on the PDF links to download directly. Else just email us.