robot body sense 1 - department of computer sciencekuipers/handouts/s07/lauzon.pdf · agostino...

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Robot Body Sense

Self-Calibration and Health Monitoring

Thomas Lauzon University of Texas at Austin

March 1 20073/1/2007 2

Plan

1. Problem Presentation1. Self-calibration2. Health Monitoring

2. Methods and Tools for Self-calibration3. Existing work on Health Monitoring4. Conclusion

1. Research Objectives2. Evaluation3. Value

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Self-Calibration

The ability to infer system parameters without humanassistance

Ex: On-line use of Laser Range Finder data to determine

the drift error from odometry. Problems:

Sensors and actuators are noisy Natural Wear and Tear over time

Ex: Wheel diameter, structural deformations Weight of passenger varies Damage

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Health Monitoring

The ability to assess the behavioral mode of adevice or the system

Problems: How to distinguish natural wear and tear from

damage? Where is the defect located? How severe is the defect? What is the impact on the operability of the system?

Knowing the health condition can help self-calibration and maintenance

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Further Reflexions

Change in the environment may favor the use of atype of sensor over another. Terrain change: impact on odometry and belief of

position based on given action command Ex: dirt, oil, water, cement, wood, carpet, tile…

In case of fire: Smoke prevents use of laser and cameras, navigation

must be based on odometry and sonars. Lens can become dirty. The operator can clean it if he

is aware of it. More sensors give better calibration when these are non-defective

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Plan




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Methods and tools: Calibration

Two types of parameters: Intrinsic: Focal Length… Extrinsic: 3D Position, roll, pitch, yaw

Two types of methods Manual Calibration Automatic Calibration (Self-Calibration)

Two types of calibration Independent (one device) Based on other sensor observations

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Important Paper:“Online Self-Calibration For Mobile Robots”Nicholas Roy and Sebastian Thrun, ICRA 1999 Presents a way to calibrate odometry based

on incremental maximum likelihoodestimation.

Unknown parameters are the real rotationaland translational drift δrot and δtrans

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Methods and tools: CalibrationTrue values Measured

valuesSystematicerror (drift)

Randomerror withzero mean

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Methods and tools: Calibration Process:

1. Acquire Scan s(i)2. Update occupancy grid with s(i)3. Move to a new location, and record o(i)4. Acquire s(i+1)5. For each position error, compute P(s(i+1)|s(i),o(i), δrot ,

δtrans)6. Compute the most likely δrot , δtrans: δrot(i)* , δtrans(i)*

(argmax of previous calculations)7. Compute δ rot* , δtrans* based on new estimated drift and

global estimated drift8. Update position using computed drift

Note: this doesn’t require recording all past data.

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Results

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Other methods: “Extrinsic Calibration of a Camera and Laser-

Range finder” Robert Pless, Qilong Zhang 2003 Method used: Registration of laser scan line on a

planar pattern

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Learning to improve models: “Simultaneous Calibration of Action and

Sensor Models on a Mobile Robot” Daniel Stronger and Peter Stone (UT), ICRA

2005

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Extended Kalman Filter “Automatic Self-Calibration of a Vision-

System during Robot Motion” ICRA 2006 Agostino Martelli, Davide Scaramuzza and

Roland Siegwart High Accuracy Requires a light source

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Hartley’s self-calibration (based on essentialmatrix properties): “A simple Technique for Self-Calibration” 1999

Paulo R. S. Mendonça and Roberto Cipolla Calculates Camera intrinsic parameters (focal

length) of a stereo pair

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Calibration: General Remarks

All these papers offer good modern techniques forself-calibration.

Some consider the calibration of one device alone. Some consider the calibration of one device with

respect to another A lot of calibration techniques cover camera

calibration They don’t cover Calibration based on more sensors They don’t suggest anything about defective sensors

or actuators

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Plan


2. Methods and Tools for Self-calibration3. Existing work on Health monitoring4. Conclusion


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Health Monitoring

Most health monitoring systems areprobabilistic or statistical:

These include: Run charts Bayesian methods Hidden semi-Markov models

Other systems use heuristics One recent paper illustrates the use of

stochastic optimization to generate modelsand then executing an action that will rule outmany of these models.

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HM: Existing Work

“Diagnosis with Behavioral Modes” Johan de Kleer, Brian C. Williams, IJCAI-89

Introduces Sherlock Sherlock hunts for possible defective candidates It rules out which systems cannot be defective by

logic It determines the best measurement to take based

on calculated probabilities

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HM: Existing Work

“Hidden semi-Markov model-basedmethodology for multi-sensor equipmenthealth diagnosis and prognosis” Ming Dong, David He, European Journal of

Operational Research 2006

Looks very complicated!!!

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HM: Existing Work

“Probabilistic Models for Monitoring and FaultDiagnosis: Application and Evaluation in a MobileRobot” Joaquin Fernandez, Rafael Sanz and Amador

Dieguez Applied AI[2004]

Uses a set of monitors to detect significant differencesbetween perceived and expected states

Presents hybrid decision systems which make use ofheuristics

Some of these are simple to implement (such as awatchdog)

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HM: Existing Work

Work in civil/aerospace engineering onstructural Damage: “Introduction to structural health monitoring”

Charles Farrar and Keith Worden [Dec 2006] Gives a historical overview States Pattern Recognition Paradigm Discusses challenges for SHM

“Bayesian Probabilistic Approach to StructuralHealth Monitoring” M.W Vanik, J. L. Beck and S. K. Au [Journal of

Engineering Mechanics 2000]

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Plan




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Research objectives

Obtaining Vulcan’s parameters automatically Monitoring the different devices to update the

model parameter values and detect defects

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Wheelchair Architecture

Wheel chairbase

Stereo pairLaserRangeFinder

Wheels

x

yz

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Parameters to consider Geometric Parameters:

Length, width, height of wheelchair Wheel positions and orientations.

Extrinsic Parameters of Sensors 6 dof laser range finder pose 6 dof stereo cameras pose

Dynamics: Acceleration/deceleration Moments of inertia

Odometry: δrot and δtrans

Other parameters?

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Evaluation criteria Models of Vulcan’s sensors and actuators shall be

developed They shall incorporate the defined parameters Simulations shall be done under Gazebo (or

USARSIM) Estimation of parameters shall be compared to

ground truth in Gazebo experiments Tests shall include navigating in circle and

comparing estimated 6dof poses of cameras andlaser range finder to ground truth

Breakdown scenarios shall be designed and reactionto these shall be evaluated in terms of accuracy andspeed.

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Value of this project

Self-Calibration Determines system capabilities Removes non random errors Allows coping with changing/non-stable configurations:

Wear and tear Different terrain types…

Health monitoring Allows ignoring data from a defective/inefficient sensor

i.e. when navigating in smoke, dirty lenses… Helps maintenance (i.e. motor still working, but

degraded and needs to be replaced) Helps evaluating if wheelchair is still capable of

control/autonomy.

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Value of this project (2)

Precise control requires accurate calibration(going through a door…)

Operator is not capable of detecting if thewheelchair behaves spuriously unless: wrongroom, saccades, slow motion, halt oraccident!!

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Further Research

If time permits: Relationship between internal representation

of lengths and meters (or inches) Visual Odometry Calibration using optical flow when robot is

moving straight forward (feasibility TBD)

Even further: Prognosis

robot body sense 1 - department of computer sciencekuipers/handouts/s07/lauzon.pdf · agostino...

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