Simultaneous Localization and Mapping (SLAM) allows pedestrians to derive maps from inertial sensors mounted on their feet, without requiring pre-existing maps. The algorithm, called FootSLAM, uses particles to represent possible odometry errors and map hypotheses. It weights particles based on compatibility with sensor readings and an individual map. FootSLAM was shown to effectively bound error growth from inertial sensors indoors and in outdoor-indoor-outdoor scenarios, deriving globally aligned maps without external inputs. Future work includes collaborative mapping across multiple users.

1. Simultaneous Localization and Mapping
for Pedestrians
using only Foot-Mounted Inertial Sensors
Patrick Robertson, Michael Angermann, Bernhard Krach
German Aerospace Center (DLR)
B. Krach is now with EADS Germany

2. Raw NavShoe Odometry Results
Algorithm: Extended Kalman Filter with Zero Velcocity Updates (Foxlin)
NavShoe INS produced reasonable results NavShoe INS had larger heading slips;
stand alone, but still unbounded error growth unbounded error begins to rise earlier

3. State of the Art: Use Maps
Inertial sensors used indoors achieve accurate positioning
when used in conjunction with maps
Krach, Robertson: WPNC 08, PLANS 08+
Widyawan, Klepal, Beauregard: WPNC 08
Woodman, Harle: UbiComp 2008
But what if the map
is unknown?

4. So, could we derive a map from this?
Naïve approach:
“Transfer the raw odometry trace to
a piece of wire and bend it bit by bit
so that similar areas overlap”

5. SLAM in Robotics
Simultaneous Localization and Mapping - identified by
robotics community in mid ‘80s!
Premise:
Localization using odometry and sensing of known
landmarks is easy!
Mapping of landmarks given known location and
orientation (pose) is easy!
Simultaneous Localization and Mapping is hard!

6. What about SLAM for Humans?
Human pedestrians are not robots but share
some similarities with them
Visual sensors (eyes)
'Odometry' (in humans: sensed by
proprioception)
Path and planning and execution
In humans, we usually have little or no
direct 'access' to most of these senses and
functions
Our central assumption:
The pedestrian is able to actively control
motion without violating physical
constraints (i.e. walls, etc)

7. A Person Processes Numerous Visual Inputs

8. Bayesian Formulation: DBN
Time k-1 Time k Time k+1
Pose
P P P
U: Actual step taken
(pose change vector)
Measured U U U
Step Error states of
Zu the odometry
Zu E Zu E Zu E
Int Int Int
Intention
„what the person
wants to do“
Vis Vis Vis
Visual information
„what the person sees“
“Environment” = Map … constant over time
Map

9. Intuitive Explanation of the Sequential
Monte Carlo Algorithm
FootSLAM lets particles, or hypotheses, explore the state
space of odometry errors, like evolution of drift
In this way, every particle is trying a slightly “differently bent
piece of wire”
Particles are weighted by their “compatibility” with
their individual map
optional sensor readings, such as GPS,
magnetometer
We can show that this is optimal in the Bayesian sense!

10. Experiments and Results
Measurement data taken from a pedestrian wearing
a foot mounted IMU
Two scenarios:
Indoor only
Outdoor – indoor - outdoor sequence

11. Video
See
http://www.kn-s.dlr.de/indoornav

12. Resulting Maps

13. Resulting Maps

14. Relative Position Accuracy - Indoors
No Scale Adaptation was Performed

15. Outdoor-Indoor-Outdoor

16. Concluding Notes
FootSLAM effectively bounds the otherwise unbounded error growth
without the need for pre-existing maps!
FootSLAM (like all forms of SLAM) is inherently invariant to rotation,
translation and scale
In mixed scenarios, the resulting maps are globally and precisely anchored
using GPS
Our future work:
Map building with multiple users;
“crowdsourcing” collaborative mapping
Movies: http://www.kn-s.dlr.de/indoornav/

17. Thank you!
Movies: http://www.kn-s.dlr.de/indoornav/