Zee: Zero-Effort Crowdsourcing
for Indoor Localization
Anshul Rai, Krishna Kant Chintalapudi, Venkata N. Padmanabhan,
Rijurekha Sen
Speaker: Huan Yang
Basic Idea
• Zee is a system that makes the calibration zero-effort, by enabling
training data to be crowdsourced without any explicit effort on the
part of users. The only site-specific input that Zee depends on is a
map showing the pathways and barriers. Zee tracks user walk
distance and orientation. Using both of the track data and floor map,
Zee can propose a user walk path. Then using the positions along the
walk path and RSS correspondingly as training data to build a WiFi
database and it will be updated during the time user using it. For any
incoming query, Zee applies HORUS or EZ model on the database to
estimate the user location.
Example Scenario
• Inferring a user’s location
• Backward belief propagation
• Recording WiFi measurements
• Using past WiFi measurements to locate subsequent users
Architecture
• Placement Independent Motion Estimator
• Counting Steps
• Estimating Heading Offset Range
• Augmented Particle Filter
• WiFi Database
Counting Steps
• Idle vs Motion: The STD is small
when the user is idle. For the motion
scenario the STD is very large.
• The STD is under 0.01g with 99%
probability when the user is idle, it is
over 0.01g with almost 100%
probability when the user is walking.
Counting Steps
• Repetitive nature of walks: the
acceleration pattern for a given
user with a particular device
placement repeats.
Counting Steps
• Generates a step occurred event every
the WALKING state.
samples while the user in
Estimating Heading Offset Range
• Magnetic offset: usually a characteristic of a
given location, depending on the construction
and other materials in the vicinity, and
typically remains stable with time.
• Placement offset: usually remains unchanged
even when the user takes a turn and changes
the direction of walking.
• Heading offset:
Estimating Heading Offset Range
• The spectrum of a typical walk: the
second harmonic is either
completely absent or is extremely
weak in the accelerations
experienced by the phone in the
direction perpendicular to the
user’s walk. It is however always
present and dominant in the
direction parallel to the user’s walk.
Estimating Heading Offset Range
• Suppose the magnitude of the
second harmonic in the Fourier
transform along north is
and that
along west is
.
• Heading offset:
or
• Error estimation:
sectors
Augmented Particle Filter
• As a user continues to walk in an indoor environment, navigating
through hallways and turning around corners, the possibilities for the
user’s path and location shrink progressively.
• 4-D particle
• Particle update
Augmented Particle Filter
• Forward pass
Augmented Particle Filter
• Backward pass
WiFi Database
• HORUS
• Construct RSS probability distribution
• Probability of observing RSS at any location
• Using Bayesian inference to compute
likelihood location
and find the maximum
WiFi Database
• EZ
• Log Distance Path Loss model
• Distance estimation
• Standard trilateration
Results
• Without WiFi database
Results
• With WiFi database
Results
• Overall
Conclusion
• Strength
•
•
•
•
No need of user participation
No need of user initial location
Independent of device placement
Active learning strategy of
database refinement
• Weakness
• Floor map needed
• Particle may not converge
• For the early queries, the result
may not precise
Questions?