ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Velocity Estimation using the Kinect Sensor

Everett Bryan

Bryce Pincock

29-Nov.-2011

ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Outline

Problem Statement Noise Characterization Velocity Estimation Results Conclusion

ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Problem Statement

The Microsoft Kinect is a new RGB-D sensor Great alternative to stereo cameras No available models of noise

Robots operating in GPS-denied environments require external proximity sensors to estimate its states Safe and desired operation

Characterize noise in Kinect and apply to a linear velocity estimator

ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Noise Characterization

Data Collection Error analysis Noise analysis Verification of Noise Models

ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Noise Characterization

Data Collection Kinect parallel to flat wall Capture depth map at 1cm increments

ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Noise Characterization

Error Analysis True depth map known Subtract captured depth map from truth

ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Noise Characterization

Noise Analysis Deterministic Noise Random Noise

)( )( mmzfMM truthmeasured

ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Noise Characterization

Noise Analysis Deterministic Noise

Error vs distance

6610)( rcrcczf

ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Noise Characterization

Noise Analysis Random Noise

)(zfMM truthmeasured

ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Noise Characterization

Noise Analysis Random Noise

ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Noise Characterization

Kinect Measurement Simulated Measurement

Verification of Noise Models

ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Velocity Estimation

AXY

1][ Xt RYXEA

)/( 1 smt

rrV tt

Improve velocity estimates using Minimum Mean Squared Error (MMSE) linear estimator

ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Velocity Estimation

Track features in successive frames Simplify to tracking the nxn center pixels in the depth

map Requires no solution to complex feature extraction and data

correspondence Take average value from nxn pixels as rt

)/( 1 smt

rrV tt

ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Results

ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Results

ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Conclusion

Successfully characterized noise within the Kinect Successfully applied a MMSE linear estimator to

estimate velocity

ECEn 670 Mini-Conference 29-Nov.-2011Everett Bryan, Bryce Pincock

Thank you