1. ECEn 670 Mini-Conference29-Nov.-2011Everett Bryan, Bryce Pincock Velocity Estimation using the Kinect Sensor Everett Bryan Bryce Pincock 29-Nov.-2011

2. ECEn 670 Mini-Conference29-Nov.-2011Everett Bryan, Bryce Pincock Outline  Problem Statement  Noise Characterization  Velocity Estimation  Results  Conclusion

3. ECEn 670 Mini-Conference29-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

4. ECEn 670 Mini-Conference29-Nov.-2011Everett Bryan, Bryce Pincock Noise Characterization  Data Collection  Error analysis  Noise analysis  Verification of Noise Models

5. ECEn 670 Mini-Conference29-Nov.-2011Everett Bryan, Bryce Pincock Noise Characterization  Data Collection  Kinect parallel to flat wall  Capture depth map at 1cm increments

6. ECEn 670 Mini-Conference29-Nov.-2011Everett Bryan, Bryce Pincock Noise Characterization  Error Analysis  True depth map known  Subtract captured depth map from truth

7. ECEn 670 Mini-Conference29-Nov.-2011Everett Bryan, Bryce Pincock Noise Characterization  Noise Analysis  Deterministic Noise  Random Noise

8. ECEn 670 Mini-Conference29-Nov.-2011Everett Bryan, Bryce Pincock Noise Characterization  Noise Analysis  Deterministic Noise  Error vs distance

9. ECEn 670 Mini-Conference29-Nov.-2011Everett Bryan, Bryce Pincock Noise Characterization  Noise Analysis  Random Noise

10. ECEn 670 Mini-Conference29-Nov.-2011Everett Bryan, Bryce Pincock Noise Characterization  Noise Analysis  Random Noise

11. ECEn 670 Mini-Conference29-Nov.-2011Everett Bryan, Bryce Pincock Noise Characterization  Kinect Measurement  Simulated Measurement  Verification of Noise Models

12. ECEn 670 Mini-Conference29-Nov.-2011Everett Bryan, Bryce Pincock Velocity Estimation  Improve velocity estimates using Minimum Mean Squared Error (MMSE) linear estimator

13. ECEn 670 Mini-Conference29-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 r t

14. ECEn 670 Mini-Conference29-Nov.-2011Everett Bryan, Bryce Pincock Results

15. ECEn 670 Mini-Conference29-Nov.-2011Everett Bryan, Bryce Pincock Results

16. ECEn 670 Mini-Conference29-Nov.-2011Everett Bryan, Bryce Pincock Conclusion  Successfully characterized noise within the Kinect  Successfully applied a MMSE linear estimator to estimate velocity

17. ECEn 670 Mini-Conference29-Nov.-2011Everett Bryan, Bryce Pincock Thank you