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Sukhum Sattaratnamai Advisor: Dr.Nattee Niparnan 1.

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Presentation on theme: "Sukhum Sattaratnamai Advisor: Dr.Nattee Niparnan 1."— Presentation transcript:

1 Sukhum Sattaratnamai Advisor: Dr.Nattee Niparnan 1

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

3 Nice Point Cloud 3

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

5 Color Information Give rich information about the environment 5

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

7 Camera Camera Model 7

8 LRF-Camera System 8

9 9

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

11 Transportation Surveillance Tourism Robotics Applications 11

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

13 Precision? Accident 13

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

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

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

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

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

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

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

21 Comparison 2004 vs

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

23 LRF-Camera System Laser Range Finder Camera 23

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

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

26 Proposed method Data improvement : Reduce angular error 26

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 27

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 28

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 29

30 30

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 31


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