Sukhum Sattaratnamai Advisor: Dr.Nattee Niparnan Improving and Filtering Laser Data for Extrinsic Laser Range Finder/Camera Calibration Sukhum Sattaratnamai Advisor: Dr.Nattee Niparnan

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

Nice Point Cloud

Point Cloud Data Hard to classify the objects without color information

Color Information Give rich information about the environment

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

Camera Camera Model

LRF-Camera System

LRF-Camera System

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

Applications Transportation Surveillance Tourism Robotics

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

Precision? Accident

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

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

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

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

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

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

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

Comparison 2004 vs 2007

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

LRF-Camera System Laser Range Finder Camera

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

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

Proposed method Data improvement : Reduce angular error

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

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

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

Thank you

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