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Improving and Filtering Laser Data for Extrinsic Laser Range Finder/Camera Calibration

Improving and Filtering Laser Data for Extrinsic Laser Range Finder/Camera Calibration. Sukhum Sattaratnamai Advisor: Dr.Nattee Niparnan. Outline. Introduction LRF-Camera System, Applications Related work LRF-Camera Calibration Method Our Problem Challenge, Propose method

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Improving and Filtering Laser Data for Extrinsic Laser Range Finder/Camera Calibration

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  1. Improving and Filtering Laser Data for Extrinsic Laser Range Finder/Camera Calibration SukhumSattaratnamai Advisor: Dr.NatteeNiparnan

  2. Outline • Introduction • LRF-Camera System, Applications • Related work • LRF-Camera Calibration Method • Our Problem • Challenge, Propose method • Scope & Work plan

  3. Nice Point Cloud

  4. Point Cloud Data • Hard to classify the objects without color information

  5. Color Information • Give rich information about the environment

  6. Laser Range Finder • Give depth data of scan plane, and can be used to generate 3D point cloud

  7. Camera • Camera Model

  8. LRF-Camera System

  9. LRF-Camera System

  10. LRF-Camera Calibration • Problem Definition [Ganhua Li, 2007] • Find the transformation [R |t ] of the camera w.r.t. LRF

  11. Applications • Transportation • Surveillance • Tourism • Robotics

  12. Precision? • “Stanley: The Robot that Won the DARPA Grand Challenge”

  13. Precision? • Accident

  14. Objective • Calibration method can give most accurate result • laser data post-processing method

  15. Related work • Projection Error (2D) • Point to Plane Distance (3D)

  16. Related work (2D) • Wasielewski, S.; Strauss, O.;, "Calibration of a multi-sensor system laser rangefinder/camera," Intelligent Vehicles '95 Symposium., 1995

  17. Related work (2D) • Mei, C.; Rives, P.;, "Calibration between a central catadioptric camera and a laser range finder for robotic applications," ICRA 2006

  18. Related work (2D) • Ganhua Li; Yunhui Liu; Li Dong; XuanpingCai; Dongxiang Zhou;, "An algorithm for extrinsic parameters calibration of a camera and a laser range finder using line features," IROS 2007

  19. Related work (3D) • Qilong Zhang; Pless, R.;, "Extrinsic calibration of a camera and laser range finder (improves camera calibration)," IROS 2004

  20. Related work (3D) • Dupont, R.; Keriven, R.; Fuchs, P.;, "An improved calibration technique for coupled single-row telemeter and CCD camera," 3DIM 2005

  21. Comparison • 2004 vs 2007

  22. Our Problem • Propose an autonomous data improving and filtering method which lead to more accurate calibration result

  23. LRF-Camera System • Laser Range Finder • Camera

  24. Challenge • Sensor Model [Kneip, L.; 2009] • Laser range finder sampling an environment discretely • Laser data are noisy : Mixed pixel

  25. Challenge • Laser beams are invisible • Point-Line constrains • No ground truth available • Autonomous process • Autonomously improve and filter the data

  26. Proposed method • Data improvement : Reduce angular error

  27. Proposed method • Data filtering: Remove outlier • In case of mixed pixel: may select neighbor point instead • In case of moving calibration object: remove data pairs

  28. Scope of the research • Propose an autonomous laser data improving and filtering method for extrinsic LRF/camera calibration • Laser range finder and camera can be placed at arbitrarily position as long as they have a common detection area • An environment is suitable for laser range finder and camera so that they can detect the calibration object

  29. Work Plan • Study the works in the related fields • Develop data improvement method • Develop data filtering method • Test the proposed method • Prepare and engage in a thesis defense

  30. Thank you

  31. Bundle adjustment • Conceived in the field of photogrammetry during 1950s and increasingly been used by computer vision researchers during recent years • Mature bundle algorithms are comparatively efficient even on very large problems • Bundle adjustment boils down to minimizing the re-projection error between the image locations of observed and predicted image points • Visual reconstruction attempts to recover a model of a 3D scene from multiple images and also recovers the poses of the cameras that took the images

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