improving and filtering laser data for extrinsic laser range finder/camera calibration

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Improving and Filtering Laser Data for Extrinsic Laser Range

Finder/Camera CalibrationSukhum Sattaratnamai

Advisor: Dr.Nattee Niparnan

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OutlineIntroduction

LRF-Camera System, ApplicationsRelated work

LRF-Camera Calibration MethodOur Problem

Challenge, Propose methodScope & Work plan

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Nice Point Cloud

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Point Cloud DataHard to classify the objects without color

information

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Color InformationGive rich information about the environment

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Laser Range FinderGive depth data of scan plane,

and can be used to generate 3D point cloud

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CameraCamera Model

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LRF-Camera System

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LRF-Camera System

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LRF-Camera CalibrationProblem Definition [Ganhua Li, 2007]

Find the transformation [R |t ] of the camera w.r.t. LRF

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TransportationSurveillanceTourismRobotics

Applications

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Precision?“Stanley: The Robot that Won the DARPA

Grand Challenge”

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Precision?Accident

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ObjectiveCalibration method can give most accurate

resultlaser data post-processing method

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Projection Error (2D)

Point to Plane Distance (3D)

Related work

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Related work (2D)Wasielewski, S.; Strauss, O.;, "Calibration of a

multi-sensor system laser rangefinder/camera," Intelligent Vehicles '95 Symposium., 1995

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Related work (2D)Mei, C.; Rives, P.;, "Calibration between a

central catadioptric camera and a laser range finder for robotic applications," ICRA 2006

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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 2007

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Related work (3D)Qilong Zhang; Pless, R.;, "Extrinsic

calibration of a camera and laser range finder (improves camera calibration)," IROS 2004

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Related work (3D)Dupont, R.; Keriven, R.; Fuchs, P.;, "An

improved calibration technique for coupled single-row telemeter and CCD camera," 3DIM 2005

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Comparison2004 vs 2007

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Our ProblemPropose an autonomous data improving and

filtering method which lead to more accurate calibration result

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LRF-Camera SystemLaser Range Finder

Camera

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ChallengeSensor Model [Kneip, L.; 2009]

Laser range finder sampling an environment discretely

Laser data are noisy : Mixed pixel

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ChallengeLaser beams are invisible

Point-Line constrainsNo ground truth available

Autonomous processAutonomously improve and filter the data

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Proposed methodData improvement : Reduce angular error

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Proposed methodData filtering: Remove outlier

In case of mixed pixel: may select neighbor point instead

In case of moving calibration object: remove data pairs

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Scope of the researchPropose 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

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Work PlanStudy the works in the related fieldsDevelop data improvement methodDevelop data filtering methodTest the proposed methodPrepare and engage in a thesis defense

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Thank you

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Bundle adjustmentConceived 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