Literature DB >> 33562538

Extrinsic Camera Calibration with Line-Laser Projection.

Izaak Van Crombrugge1, Rudi Penne1, Steve Vanlanduit1.   

Abstract

Knowledge of precise camera poses is vital for multi-camera setups. Camera intrinsics can be obtained for each camera separately in lab conditions. For fixed multi-camera setups, the extrinsic calibration can only be done in situ. Usually, some markers are used, like checkerboards, requiring some level of overlap between cameras. In this work, we propose a method for cases with little or no overlap. Laser lines are projected on a plane (e.g., floor or wall) using a laser line projector. The pose of the plane and cameras is then optimized using bundle adjustment to match the lines seen by the cameras. To find the extrinsic calibration, only a partial overlap between the laser lines and the field of view of the cameras is needed. Real-world experiments were conducted both with and without overlapping fields of view, resulting in rotation errors below 0.5°. We show that the accuracy is comparable to other state-of-the-art methods while offering a more practical procedure. The method can also be used in large-scale applications and can be fully automated.

Entities:  

Keywords:  camera calibration; extrinsic calibration; field of view; multi-camera; non-overlap

Year:  2021        PMID: 33562538      PMCID: PMC7914869          DOI: 10.3390/s21041091

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  7 in total

1.  Influence of camera calibration conditions on the accuracy of 3D reconstruction.

Authors:  Anne-Sophie Poulin-Girard; Simon Thibault; Denis Laurendeau
Journal:  Opt Express       Date:  2016-02-08       Impact factor: 3.894

2.  Extrinsic Calibration of Camera Networks Based on Pedestrians.

Authors:  Junzhi Guan; Francis Deboeverie; Maarten Slembrouck; Dirk Van Haerenborgh; Dimitri Van Cauwelaert; Peter Veelaert; Wilfried Philips
Journal:  Sensors (Basel)       Date:  2016-05-09       Impact factor: 3.576

3.  Camera Calibration Using Gray Code.

Authors:  Seppe Sels; Bart Ribbens; Steve Vanlanduit; Rudi Penne
Journal:  Sensors (Basel)       Date:  2019-01-10       Impact factor: 3.576

4.  STAM-CCF: Suspicious Tracking Across Multiple Camera Based on Correlation Filters.

Authors:  Ruey-Kai Sheu; Mayuresh Pardeshi; Lun-Chi Chen; Shyan-Ming Yuan
Journal:  Sensors (Basel)       Date:  2019-07-09       Impact factor: 3.576

5.  A Fast and Robust Extrinsic Calibration for RGB-D Camera Networks.

Authors:  Po-Chang Su; Ju Shen; Wanxin Xu; Sen-Ching S Cheung; Ying Luo
Journal:  Sensors (Basel)       Date:  2018-01-15       Impact factor: 3.576

6.  Multi-Camera Vehicle Tracking Using Edge Computing and Low-Power Communication.

Authors:  Maciej Nikodem; Mariusz Słabicki; Tomasz Surmacz; Paweł Mrówka; Cezary Dołęga
Journal:  Sensors (Basel)       Date:  2020-06-11       Impact factor: 3.576

7.  Flexible and Accurate Calibration Method for Non-Overlapping Vision Sensors Based on Distance and Reprojection Constraints.

Authors:  Tao Jiang; Xu Chen; Qiang Chen; Zhe Jiang
Journal:  Sensors (Basel)       Date:  2019-10-24       Impact factor: 3.576

  7 in total
  1 in total

1.  Accuracy Assessment of Joint Angles Estimated from 2D and 3D Camera Measurements.

Authors:  Izaak Van Crombrugge; Seppe Sels; Bart Ribbens; Gunther Steenackers; Rudi Penne; Steve Vanlanduit
Journal:  Sensors (Basel)       Date:  2022-02-23       Impact factor: 3.576

  1 in total

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