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.

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!

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)

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)

A Person Processes Numerous Visual Inputs

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.

FootSLAMHuman Odometry Data Processed with a Particle Filter

5 meters

Human-Recognizable Places

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

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)

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!

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

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

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:

Weight update

If particle i revisited a place:

If particle marked a new place:

r cancels out and pL accounts for places being sparse

Intuitive Illustration

Place

Intuitive Illustration: Perfect Assoc.

Place

Intuitive Illustration: Unknown Assoc.

Place

dmin

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

Resulting Maps

Large conference Table Canteen

Improvement of Positioning Accuracy

Video

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

Thank you!

Movies and papers: http://www.kn-s.dlr.de/indoornav/

Intuitive Illustration

Place