Ion feet slam 2011 v4

Collaborative Pedestrian Mapping of Buildings Using Inertial Sensors and FootSLAM
Patrick Robertson
German Aerospace Center (DLR)
María García Puyol
DLR and University of Malaga
Michael Angermann
DLR

Challenges for Indoor Navigation
Outdoor
Outdoor positioning for pedestrians and automobiles uses Global Navigation Satellite Systems (GNSS)
Maps readily available
Indoor
GNSS signals strongly disturbed
Combination of pedestrian dead reckoning with maps is advantageous
Existing maps are often imprecise, unavailable, obsolete, proprietary, and limited
Approaches:
Sensor Fusion,
Map Aiding,
FootSLAM

FootSLAM - Simultaneous Localization and Mapping (SLAM) for pedestrians
FootSLAM converts raw human odometry (left) to maps of walkable areas (right)

FootSLAM Map Representation
Regular 2D hexagon grid
Each edge of the hexagons is associated with a transition count that represents an estimate of the probability with which it was crossed
Resulting maps:
Map Posterior Distribution
“Maximum a posteriori” Map

Collaborative Mapping Scenario
Use map for inertial based map assisted pedestrian navigation
Collaborative
FootSLAMprocessing
Map
Anonymized odometry data collected byvolunteers and / or users
Data may be used to refine the maps

Motivation for Iterative “Turbo” FeetSLAM
Optimal multiple data set estimator is a trivial extension of FootSLAM, but would suffer from severe depletion (too many particles required)
Heuristic approach borrowed from Turbo Coding from comunications theory:
Decompose the problem into smaller ones
Iterative processing
Each processing stage feeds the other with “prior” information
For FootSLAM, the “prior” can be shown to be the maps from all other data sets correctly added together
Iterative processing: we can pre-process the prior maps during iterations (cooling, filtering …)
Similarities to simulated annealing

How do we Combine Different Maps?
Different walks start in different locations and with different starting headings
Even when we run FootSLAM many times for the same data set and same starting conditions, the resulting map is never the same, and may be shifted, rotated or slightly scaled
An example with two data sets showing the need for transformation:
As humans, we would be very good at combining these two maps: we would rotate one until they both fit, then we would add them!

Combining Two Maps
Transformation of one map so that they both “fit”
Transforming the map counts
Projection of the counts to match the hexagon grids
Correlate the transformed and fixed maps
Find the transformation that gives the best “fit” (correlation) between the two maps
Combination of the two maps by simply adding the counts

Transformation and Projection: Example
Transformation and projection is performed on an edge by edge basis
Transformation

Projection of one edge: Finding the Target Hexagons
TARGET GRID

Distance factor
Angular factor
How to Share the Counts among the Edges of the Target Hexagons
Distance weight
Angular weight

Two Examples of Transformation and Projection
Rotation=0.5804 rad
X shift= -0.2m
Y shift= 1.2m
Scale factor=1.0
Rotation=0.0rad
X shift= 0.0m
Y shift= 0.0m
Scale factor=1.15

Finding the best Transformation
To combine two individual maps, we need to find the best transformation for one of them to match the other
Try all different combinations of x and y shifts, rotation and scale factor values, and compute the resulting log-likelihood value of the transformation (see paper for details)
The maximum log-likelihood value  best transformation

Combining Two Maps
The combination of two maps after transformation and projection is achieved by simply adding the counts of their edges:

Processing more that Two Maps
Also correlate the new combined map with the existing ones and proceed the same way
Obtain the combined map, add it to the pool and remove the two individual maps
Take all the maps pairwise and correlate them
Choose the pair that has the highest correlation

Prior Map Generation: Weakening and Filtering
For each data set, a prior map is generated by adding the other maps (after being combined as shown before)
We process maps iteratively
A prior map can be weakened and filtered to control its influence on the FootSLAM map estimation process:
We want FeetSLAM to converge gradually to the “correct” total map
Weakening: dividing the counts by a prior map weakening factor >1
Spatial Filtering: spreading the counts over more hexagons
Start with a weak and strongly filtered map for early iterations
Make the map stronger and less filtered as iterations proceed

Used at thenext iteration
Algorithm at each Iteration
Cooling &
Filtering
Data sets (walks)
Transformations T1…Tn from previous iteration
Starting Conditions SC1…SCn from previous iteration
Data Sets D1…Dn
Starting Conditions
SC1…SCn
Prior maps
P1…Pn
FootSLAM (Di)
Transform(SCi)
Individual Maps M1…Mn
Manually written SC (or GPS anchors)
Combination (M1…Mn)
Starting Conditions are applied to the Data before FootSLAM. Over iterations, Data is correctly aligned before FootSLAM. See video!
Total Map
Transform(Mi)
M1T…MnT
Add counts
Starting Conditions of FS maps SC1…SCn
Transform-ations T1…Tn
Prior maps P1…Pn

Two scenarios
DLR
90000 particles
5 walks
37 hours for 10 iterations
Video
MIT
90000 particles
4 walks
42 hours for 10 iterations
Video

Finish
Start
MIT Track 3 Raw Odometry

DLR Data: Comparison with the true ground floor
The floor plan we originally usedas a ground truth was wrong!
… here
In the original plan this wall was…

Result of the MIT Experiment

Comparison with the true ground floor
Total map
One of the maps

Achievements and further work
Achievements
Presented a fully automated FeetSLAM implementation
Evaluated with two data sets
Total map is more complete than the individual ones
The maps become more precise and accurate over iterations
Individual maps that do not converge without a prior converge when using the information provided by other maps
Further work
Online and real-time map merging
GPS anchors
Improve mathematical basis for correlation function
Performance enhancements, computational requirements
Address 3D

Collaborative Indoor Mapping

Thanks for your Attention!

Characteristics of FootSLAM
Foot-mounted IMU sensor  measures pedestrian odometry
Optionally GPS (for absolute reference)
Human motion modeled as a first order Markov process
Each particle estimates the pose + odometry errors + individual map
Hence: each particle tries a certain pose history – and estimates a “walkability” map based on this
Dynamic Bayesian Network for FootSLAM

Collaborative Mapping
Collaborative FootSLAM  FeetSLAM
Data sets that arise from different walks and that may or may not start and finish at the same point and that can overlap more or less
Current status: Non-real time approach (offline processing)
Collaborative mapping of airports,
museums and other public buildings
Goals
Complete map of the “walkable areas”
Accurate individual maps
Adapt to changes in the environment (walls, furniture, etc)
The map is then used to let(other) pedestrians navigate using these maps

Projection of the Counts
The transformation is performed to make one map match another
When comparing or adding two maps, we need them to be within the same coordinate system, that is, grid of hexagons
When we transform a map, its hexagons are usually not aligned with the hexagons of the target hexagon grid
We need to project the transformed map onto the target grid
x
y

3. Comparison of two mapsAugmented Log-Likelihood value
Accounted for map: the map that
is transformed
Underneath map: the map that is
fixed
Heuristic hexagoncorrelation term
Log FootSLAM weight

Example of the log-likelihood function
α=0.8
β=0.04

Ion feet slam 2011 v4

by patrickrobertson on Oct 14, 2011

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