Improved Pedestrian Navigation Based on Drift-Reduced NavChip™ MEMS IMU

Eric FoxlinAug. 3, 2009

WPI Workshop onPrecision Indoor Personnel Location and

Tracking for Emergency Responders

Outline

• Summary of the problem and NavShoe™ solution concept

• Original results with COTS MEMS inertial sensors and magnetometers (from WPI 2006)

• Development of the NavChip™ proprietary MEMS IMU and performance improvements realized over COTS MEMS sensors

• The fundamental problem of heading observability and some new solution ideas

The Problem

• GPS doesn’t work indoors

• Cannot assume a prepared environment or use of any tracking infrastructure

• To obtain meter-level accuracy and thorough coverage with RF/UWB requires setting up multiple antennas around the building

• Pedometer/compass dead-reckoning modules not sufficiently robust and accurate

– Must calibrate for individual user’s step size– Changes in size of steps cause errors– Variations in direction of steps cause errors– Current products specify drift accumulation of 2-5% of

distance travelled, and that assumes normal walking with consistent gait.

Strapdown Inertial Navigation

-g position

integrationrate

gyros orientation

accelscoord.

transform

ω

doubleintegration

f

B

f N

position

orientation

B aN

Inertial Navigation Error Growth

100 101 102 1030

50

100

150

200

250

300

350

400

450

500

seconds

mm

commercial-grade tactical-grade

navigation-grade

strategic-grade

geophysical limit

NavShoe™ Concept• Foot-mounted inertial measurement unit (IMU)

• Short-term inertial navigation measures the 6-DOF trajectory of each step – works with any kind of motion

• Break cubic error growth by resetting velocity to zero after each step:

• Take advantage of correlated position/velocity errors in Kalman filter to also remove most position error with each ZVU:

• Correct heading drift of small MEMS gyros, based on compass measurements averaged over a long distance

Results (Outdoors)Trajectory of NavShoe during 741m road loop.

-50 0 50 100 150

-160

-140

-120

-100

-80

-60

-40

-20

0

20

easting (meters)

north

ing

(met

ers)

Results (Indoors)

easting (meters)-4 -3 -2 -1 0 1 2 3 4 5 6

-2

-1

0

1

2

3

4

5

6

north

ing

(met

ers)

sofa

dining table

kitchen

1st-floor bedroom

upstairs bedroom

bed

Trajectory of NavShoe during 118.5 m (322 s) exploratory path through house. Final position error was (-0.32 0.10 -0.06), about 0.3%

Plan view Elevation

-2 -1 0 1 2 3 4

-1

0

1

2

3

4

easting (meters)

up (m

eter

s)

Need for Improved Inertial Sensor

• Outdoor and wood-frame house navigation results were very good at 0.3% of distance travelled.

• However, this solution was heavily dependant on compass to control yaw drift, and did not work so well in typical office building containing lots of steel.

• Ability to reject magnetic disturbances is proportional to quality of gyros, so in 2007 InterSense embarked on development of next generation IMU, “NavChip™”, based on custom MEMS technology with 10X performance gains

NavChip™ Performance Goals

• 2X wider dynamic ranges to handle max shoe-mounted rates and accelerations

• Single-chip epoxy-encapsulated IMU for ruggedness, long-term alignment stability & environmental protection

• Miniaturized for embedding in shoe

• 10X improved gyro drift to better reject magnetic disturbances

NavChip Progress Summary• Rev.A received Dec 08 – preliminary performance

characterization & design validation led to several design and manufacturing improvements

• Rev.B received April 09 - demonstrated at Sensors Expo in June

• Rev.C redesigned for improved manufacturing yield, expect first articles in August.

Prototype Test Results –Gyro Allan Variance

Prototype Test Results –Gyro Integration Drift

Prototype Test Results –Accelerometer Allan Variance

IC3:VRW: 0.19 m/s/rt-hraccel stability: 80 ug

NavChip:VRW: 0.036 m/s/rt-hraccel stability: 30 ug

NavChip v. InertiaCube3 Spec.s

Spec IC3 NavChip

Gyro range 1200 2000

Accelerometer range 5g 11g

Angle Random Walk 5.3°/√hr 0.3°/√hr

Gyro Bias Stability 62°/hr typ. 12°/hr typ.

Accel Bias Stability 0.1 mg typ. 0.04 mg typ.

Operating Temp. 0 to 70°C -20 to 80°C

Size 26x38x15 mm (15 cm3)

12.5x23x7.5 mm (3 cm3)

Power 225 mW 120 mW

Real-Time Demo w/ NavChip

• Just tiny NavChip, no mags, easy to embed in sole of shoe:

• Flexible lightweight Kalman Filter SW– Configurable with many different measurement models:

ZUPTing, GPS, magnetometers, RF ranging, etc– Can process 1000 Hz NavChip data with almost no load on

handheld PC (200 Hz used in following results)

Unaided NavChip Indoors

Outdoors

Outside Coming In

Heading Observability Issue

• Even with the improved gyros, it is too hard to extract reliable heading information from magnetometers in a metal-rich environment:

• Need a different aiding source

Aiding possibilities

• Solutions that won’t work for firefighters:–Map matching –Straight line assumptions–RF triangulation

• Solutions that might work for firefighters:–Sparse RF range aiding–Trajectory matching software

Sparse RF Ranging

Trajectory Matching Idea

• Even if it is impossible to state a firefighter’s absolute coordinates in the building accurately due to gradual heading drift, it is still possible to find him by retracing the path he took

• Trajectory matching software overlays the rescuer’s trajectory (green) on the victim’s trajectory (yellow), and automatically warps one or both trajectories to bring them together when they start to diverge.

• This allows the incident commander to see at a glance where the rescuer is along the victim’s path and give him appropriate directions over the radio

Trajectory Matching Implementation

• Each trajectory consists of a string of dots (one per step) connected by springs with rest length equal to the original (NavShoe-measured) distance between the dots.

• Each dot also contains an angular (torque) spring with rest angle equal to the original angle between the incoming and outgoing step vectors.

• The origin of both trajectories is fixed at the same point, e.g. entrance door.

• Moving or applying force to a point in the middle of a trajectory will bend the portion from the origin to the control point (past history). The remaining (future) section will rotate and translate as a rigid object.

Trajectory Matching Rescue Procedure

• Rescuer starts out following the victim’s yellow path, and green path is initially well aligned

• Eventually the two paths start to diverge slightly, but not yet enough to confuse the incident commander.

• As the green path grows, software automatically marks interesting features along the path, such as corner points, and draws dotted lines to probable corresponding points on the yellow path. Once there are enough corresponding points to give the software high confidence, it snaps all the corresponding points together, warping the early part of the two trajectories into close alignment.

• The remaining (downstream) sections of the trajectories also move closer, making it easier for the incident commander to give directions, and easier for the software to establish more correspondences.

Trajectory Matching Example

• Red cross-links represent possible corresponding points which could be pulled together.• Note that rescuer need not follow every detail of the victim’s trajectory

Extension to real-time situational awareness

• If a number of firefighters are moving through a building, each will eventually pass through certain corridors or around certain corners that others have passed or he has passed before.

• If this happens before a player’s trajectory has diverged too much, perhaps the matching software can match features in different players’ paths, even though they are not following each other.

• This will produce more accurate trajectories that are all aligned in the same global coordinate frame, and allow the commander to see where all firefighters are at any time.

Questions?

• InterSense is seeking partners to help develop localization systems incorporating the NavChip or our real-time filtering software.

• www.intersense.com