improving simultaneous localization and mapping for pedestrian navigation and automatic mapping of...
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Improving Simultaneous Localization and Mapping for Pedestrian Navigation and Automatic Mapping of Buildings by using Online Human-Based Feature Labeling. Pesented at IEEE/ION PLANS 2010, May 2010, Palm Springs, CA, USA. Authors: Patrick Robertson, Michael Angermann, Mohammed Khider, German Aerospace Center (DLR)TRANSCRIPT
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Improving Simultaneous Localization and Mapping for Pedestrian Navigation and Automatic Mapping of Buildings
by using Online Human-Based Feature LabelingPatrick Robertson, Michael Angermann,
Mohammed Khider, German Aerospace Center (DLR)
Slides from: “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.
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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!
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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), can be measured using inertial sensors
Path and planning and execution
For humans: little or no direct 'access' to senses and functions
Our central assumption:
The pedestrian is able to actively control motion without violating physical constraints (i.e. walls, etc)
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Raw NavShoe Odometry Results
NavShoe INS produced reasonable resultsstand alone, but still unbounded error growth
NavShoe INS had larger heading slips;unbounded error begins to rise earlier
Algorithm: Extended Kalman Filter with Zero Velcocity Updates (Foxlin)
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A Person Processes Numerous Visual Inputs
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Six ways out of the hexagon
First order Markov process
Location dependent
Time Invariant
Probabilistic map
FootSLAM: Hexagonal Grid over Space
Human motion is modelled by a person choosing which edge of the hexagon to cross.
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FootSLAMHuman Odometry Data Processed with a Particle Filter
5 meters
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Human-Recognizable Places
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A
C
G
E
Physical space
F
B
D
1
2
3 4
5
67
8 9
10
11
A B C A F E B D G E D B
Timestamped placestamps
Perfect association
Partial association
Unknown association
- Arrows denote pedestian‘s trajectory; - letter-coded circles with denote unique places; - colors denote some recognizable aspect of the place
An Example of Placestamps
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The PlaceSLAM Dynamic Bayesian Network (DBN)
P
U
Zu E
Int
Vis
L, M
“Visual impression -what the person sees“
Intention“where the person wants to go”
Time k-1
Measured Step
“Environment” = Human recognizable Places L and FootSLAM Map M; both are constant over time
Time k
P
U
Zu E
Int
Vis
ZL
A
Placestamp
Place identifier
seen
ZL
A
Odometry Error states
Actual step taken (pose change vector)
Pose (= location, orientation)
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Intuitive Explanation of the Sequential Monte Carlo Estimator
FootSLAM lets particles, or hypotheses, explore the state space of odometry errors, like evolution of drift as well as the association of places
In this way, every particle is trying a slightly “differently bent piece of wire”
Particles are weighted by their “compatibility” with
their individual PlaceSLAM map
their individual FootSLAM map
optional sensor readings, such as GPS, magnetometer
We can show that this is optimal in the Bayesian sense!
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Illustration of Proposal Function 1
dmin
Particle position
dmin
If particle is closer than dmin to some existing place(s) then choose the closest place
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Illustration of Proposal Function 2
dmin
If particle is further than dmin from all existing places then choose a new place at the particle‘s current position
Particle position
New place proposed to be here
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Algorithm SummaryPerform
FootSLAMWeighting
and FootSLAMmap update
Locate closestexisting placeto particle’s
current Pose P
Placestampwas reported
Select this Identifier
(closest place)
Choosenew identifier
None withindmin
Closest is within dmin
Multiply weightby PL
Multiply weightby GaussianLikelihood
(PLANS paper (12))
Initialise new place’s location to current
particle pose P
Update place’s location with current
particle pose P
No
plac
esta
mp
repo
rted
Performfor all Np
Particles:
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Weight update
If particle i revisited a place:
If particle marked a new place:
r cancels out and pL accounts for places being sparse
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Intuitive Illustration
Place
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Intuitive Illustration: Perfect Assoc.
Place
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Intuitive Illustration: Unknown Assoc.
Place
dmin
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Experiments and Results
Measurement data taken from a pedestrian wearing a foot mounted IMU
Placestamps collected during the walk
Two scenarios:
Indoor only
Outdoor – indoor - outdoor sequence
Indoor only: only foot mounted IMU
Mixed scenario: foot mounted IMU as well as GPS and compass sensors
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Resulting Maps
Large conference Table Canteen
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Improvement of Positioning Accuracy
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Video
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Concluding Notes
PlaceSLAM is a useful adjunct to FootSLAM and improves accuracy and stability
Two main forms of PlaceSLAM: Perfect Association (“press a certain button”) and unknown association (“press any button”)
Error assumptions: Humans are lazy in reporting but do not erroneously report places
Bayesian derivation
Suggested future work:
More experimental data in different sites and for different building sizes and geometries
Map building with multiple users; “crowdsourcing” collaborative mapping
Extend error models, overlapping and multiple places, RFID tags
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Thank you!
Movies and papers: http://www.kn-s.dlr.de/indoornav/
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Intuitive Illustration
Place