Application of LSD-SLAM for Visualization Temperature in Wide-area Environment

Masahiro Yamaguchi, Hideo Saito, Shoji Yachida

Abstract

In this paper, we propose a method to generate a three-dimensional (3D) thermal map by overlaying thermal images onto a 3D surface reconstructed by a monocular RGB camera. In this method, we capture the target scene moving both an RGB camera and a thermal camera, which are mounted on the same zig. From the RGB image sequence, we reconstruct 3D structures of the scene by using Large-Scale Direct Monocular Simultaneous Localization and Mapping (LSD-SLAM), on which temperature distribution captured by the thermal camera is overlaid, thus generate a 3D thermal map. The geometrical relationship between those cameras is calibrated beforehand by using a calibration board that can be detected by both cameras. Since we do not use depth cameras such as Kinect, the depth of the target scene is not limited by the measurement range of the depth camera; any depth range can be captured. To demonstrating this technique, we show synthesized 3D thermal maps for both indoor and outdoor scenes.

References

  1. Bay, H., Tuytelaars, T., and Van Gool, L. (2006). SURF: Speeded up robust features. In European Conference on Computer Vision, pages 404-417. Springer.
  2. Borrmann, D., Nüchter, A., Dakulovic, M., Maurovic, I., Petrovic, I., Osmankovic, D., and Velagic, J. (2012). The project thermalmapper-thermal 3d mapping of indoor environments for saving energy. IFAC Proceedings Volumes, 45(22):31-38.
  3. Engel, J., Schöps, T., and Cremers, D. (2014). Lsd-slam: Large-scale direct monocular slam. In European Conference on Computer Vision, pages 834-849. Springer.
  4. Engel, J., Sturm, J., and Cremers, D. (2013). Semi-dense visual odometry for a monocular camera. In Proceedings of the IEEE international conference on computer vision, pages 1449-1456.
  5. Glover, A., Maddern, W., Warren, M., Reid, S., Milford, M., and Wyeth, G. (2012). Openfabmap: An open source toolbox for appearance-based loop closure detection. In Robotics and Automation (ICRA), 2012 IEEE International Conference on, pages 4730-4735. IEEE.
  6. Ham, Y. and Golparvar-Fard, M. (2013). An automated vision-based method for rapid 3d energy performance modeling of existing buildings using thermal and digital imagery. Advanced Engineering Informatics, 27(3):395-409.
  7. Klein, G. and Murray, D. (2007). Parallel tracking and mapping for small ar workspaces. In Mixed and Augmented Reality, 2007. ISMAR 2007. 6th IEEE and ACM International Symposium on, pages 225-234. IEEE.
  8. Lowe, D. G. (1999). Object recognition from local scaleinvariant features. In Computer vision, 1999. The proceedings of the seventh IEEE international conference on, volume 2, pages 1150-1157. Ieee.
  9. Matsumoto, K., Nakagawa, W., Saito, H., Sugimoto, M., Shibata, T., and Yachida, S. (2015). Ar visualization of thermal 3d model by hand-held cameras. In SciTePress.
  10. Mur-Artal, R., Montiel, J., and Tardós, J. D. (2015). Orbslam: a versatile and accurate monocular slam system. IEEE Transactions on Robotics, 31(5):1147-1163.
  11. Nakagawa, W., Matsumoto, K., de Sorbier, F., Sugimoto, M., Saito, H., Senda, S., Shibata, T., and Iketani, A. (2014). Visualization of temperature change using RGB-D camera and thermal camera. In Workshop at the European Conference on Computer Vision, pages 386-400. Springer.
  12. Prakash, S., Lee, P. Y., and Caelli, T. (2006). 3d mapping of surface temperature using thermal stereo. In 2006 9th International Conference on Control, Automation, Robotics and Vision.
  13. Vidas, S., Moghadam, P., and Bosse, M. (2013). 3d thermal mapping of building interiors using an rgb-d and thermal camera. In Robotics and Automation (ICRA), 2013 IEEE International Conference on, pages 2311- 2318. IEEE.
  14. Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1330-1334.
Download


Paper Citation


in Harvard Style

Yamaguchi M., Saito H. and Yachida S. (2017). Application of LSD-SLAM for Visualization Temperature in Wide-area Environment . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 216-223. DOI: 10.5220/0006153402160223


in Bibtex Style

@conference{visapp17,
author={Masahiro Yamaguchi and Hideo Saito and Shoji Yachida},
title={Application of LSD-SLAM for Visualization Temperature in Wide-area Environment},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={216-223},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006153402160223},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Application of LSD-SLAM for Visualization Temperature in Wide-area Environment
SN - 978-989-758-225-7
AU - Yamaguchi M.
AU - Saito H.
AU - Yachida S.
PY - 2017
SP - 216
EP - 223
DO - 10.5220/0006153402160223