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.

WiSLAM uses RSS (received signal strength) measurements from Wifi access points (APs) as additional measurements. In the case of using RSS, WiSLAM needs to estimate not only the location, but also the effective transmit power of each AP. We have adopted a RBPF approach in which every particle carries its own WiSLAM map. WiSLAM can be added (well, multiplied in the likelihood function ...) to FootSLAM/PlaceSLAM. We use a Gaussian Mixture Model to approximate the location of the APs and assume a descrete PDF of the AP's effective transmit power. Our work was presented at IPIN 2011 [IEEE Xplore].

FootSLAM may be combined with a Prior Map to improve the position accuracy and early convergence. We propose to use Angular PDFs generated from floor plans to represent prior map for FootSLAM. The FootSLAM approach supported by additional prior-maps is very flexible: The prior map may represent only the outer walls of a building, and perhaps some of the inner walls. Because FootSLAM can over time learn the correct map, it is inconsequential if a number of walls are missing or erroneous because the map will be corrected over time. If a map is available it is profitable to use it as prior map. More informations about angular PDFs used as maps in pedestrian navigation can be found on our mobility models webpage Mobility Models. This work was presented at PLANS 2012 [ELIB] [IEEE Xplore].

Recent developments

Further explanations and thoughts on FootSLAM: link.

A FootSLAM Gallery: More examples of FootSLAM maps. For example our new building

Please get in touch!

If you are a researcher or group working on FootSLAM/WiSLAM 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.

Selected Slides on FootSLAM / PlaceSLAM / WiSLAM (click on image):


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


Principle of WiSLAM: With sufficient motion of the receiver, the location (and TX power) of an Access Points (AP) become(s) observable..	Dynamic Bayesian Network (DBN) for FootSLAM combined with WiSLAM.	Result of mapping with known pedestrian location with real data.	Result of WiSLAM (and FootSLAM) with real data.

Complete slideshows below.

Movies

New: FootSLAM video showing raw odometry, particle distribution and heading rate bias posterior

FootSLAM on one of the MIT walks

This video shows the raw human odometry, the map, the particle distribution and the heading rate bias posterior (histogram on top left). The blue dots on the x,y particle display are the mean position computed at that time, and it is frozen on the display. We can observe to which extent the mean estimated position - conditioned only on measurements available at that time - deviates from what the eye can recognize as the "true" track (e.g. when compared to the map on the bottom left). The histogram shows the estimate of the heading rate bias (HRB) which begins as a wide (prior) distribution and then converges during loop closures. We can see the effect of this HRB on the raw odometry which shows the typical spiralling behaviour (further explanation and more videos from the MIT experiment on this page).

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 either follow the ELIB link to our internal electronic library, or to IEEE Xplore, or just click on the PDF links to download directly. Else just email us and we will be happy to send a copy.

Robertson, Patrick and Angermann, Michael and Krach, Bernhard
Simultaneous Localization and Mapping for Pedestrians using only Foot-Mounted Inertial Sensors [PDF]
Proc. Ubicomp 2009, 30. Sep.-3. Oct. 2009, Orlando, Florida, USA. See also presentations below.
Robertson, Patrick and Angermann, Michael and Krach, Bernhard and Khider, Mohammed
Inertial Systems Based Joint Mapping and Positioning for Pedestrian Navigation [ELIB][PDF]
Proc. ION GNSS 2009, 22. Sep. - 25. Sep. 2009, Savannah, Georgia, USA. See also presentations below.
Robertson, Patrick and Angermann, Michael and Khider, Mohammed
Improving Simultaneous Localization and Mapping for Pedestrian Navigation and Automatic Mapping of Buildings
by using Online Human-Based Feature Labeling, in Proc. IEEE/ION PLANS 2010, May 2010, Palm Springs, CA, USA. See also presentations below.
Robertson, Patrick and Angermann, Michael and Krach, Bernhard and Khider, Mohammed
SLAM Dance: Inertial-Based Joint Mapping and Positioning for Pedestrian Navigation [PDF], InsideGNSS, May 2010. See also presentations below.
Robertson, Patrick and Garcia Puyol, Maria and Angermann, Michael
Collaborative Pedestrian Mapping of Buildings Using Inertial Sensors and FootSLAM [ELIB] [PDF]
Collaborative Pedestrian Mapping of Buildings Using Inertial Sensors and FootSLAM. ION GNSS 2011, 19.-23. Sep. 2011, Portland, Oregon, USA. See also presentations below.
Garcia Puyol, Maria
Merging of maps obtained with human odometry based on FootSLAM for pedestrian navigation [PDF]
Diploma Thesis presented at University of Malaga (Spain) on September 23rd 2011.
Bruno, Luigi and Robertson, Patrick
WiSLAM: Improving FootSLAM with WiFi. [IEEE Xplore]
Indoor Positioning and Indoor Navigation (IPIN), 2011 International Conference, 21-23 Sept. 2011, Guimaraes, Portugal. ISBN 978-1-4577-1803-8.
Angermann, Michael and Robertson, Patrick
FootSLAM: Pedestrian Simultaneous Localization and Mapping Without Exteroceptive Sensors�Hitchhiking on Human Perception and Cognition. [IEEE Xplore]. Proceedings of the IEEE , vol.100, no.Special Centennial Issue, pp.1840-1848, May 13 2012, doi: 10.1109/JPROC.2012.2189785

Slides

Simultaneous Localization and Mapping for Pedestrians using only Foot-Mounted Inertial Sensors

View more documents from guest5fe3bb.

Improving Simultaneous Localization and Mapping for Pedestrian Navigation and Automatic Mapping of Buildings by using Online Human-Based Feature Labeling

View more presentations from patrickrobertson.

Ion feet slam 2011 v4

View more presentations from patrickrobertson

WiSlam presentation

View more PowerPoint from lbruno236

Animated powerpoint slides showing odometry error and coordinate systems - modified and improved w.r.t. ION GNSS 2009 and SLAM Dance.

Incase of any questions please contact:
Patrick Robertson, phone: +49 8153 28 2808, email: patrick.robertson@dlr.de
Michael Angermann, phone: +49 8153 28 2893, email: michael.angermann@dlr.de
Maria Garcia Puyol, phone: +49 8153 28 2892, email: maria.garciapuyol@dlr.de
Mohammed Khider, phone: +49 8153 28 2830, email: mohammed.khider@dlr.de
Susanna Kaiser, phone: +49 8153 28 2862, email: susanna.kaiser@dlr.de