Toward Moving Objects Detection in 3D-Lidar and Camera Data

Clement Deymier, Thierry Chateau

Abstract

In this paper, we propose a major improvement of an algorithm named IPCC (Clement Deymier, 2013a) for Iterative Photo Consistency-Check. His goal is to detect a posteriori moving objects in both camera and rangefinder data. The range data may be provided by different sensors such as: Velodyne, Riegl or Kinect with no distinction. The main idea is to consider that range data acquired on static objects are photo-consistent, they have the same color and texture in all the camera images, but range data acquired on moving object are not photo-consistent. The central matter is to take into account that range sensor and camera are not synchronous, so what is seen in camera is not what range sensors acquire. This work propose to estimate photo-consistency of range data by using his 3D neighborhood as a texture descriptor wich is a major improvement of the original method based on texture patches. A Gaussian mixture method has been developed to deal with occluded background. Moreover, we will see how to remove non photo-consistent range data from the scene by an erosion process and how to repair images by inpainting. Finally, experiments will show the relevance of the proposed method in terms of both accuracy and computation time.

References

  1. Cheng, Z.-Q., Wang, Y.-Z., Li, B., Xu, K., Dang, G., and Jin, S.-Y. (2008). A survey of methods for moving least squares surfaces. In Proceedings of the Fifth Eurographics / IEEE VGTC conference on PointBased Graphics, SPBG'08, pages 9-23, Aire-la-Ville, Switzerland, Switzerland. Eurographics Association.
  2. Clement Deymier, T. C. (2013a). Ipcc algorithm: Moving object detection in 3d-lidar and camera data. In IEEE Intelligent Vehicles Symposium.
  3. Clement Deymier, Damien Vivet, T. C. (2013b). Nonparametric occupancy map using millions of range data. In IEEE Intelligent Vehicles Symposium.
  4. Himmelsbach, M. (2008). Lidar-based 3d object perception. Proceedings of 1st International Workshop on Cognition for Technical Systems.
  5. Jung, B. and Sukhatme, G. S. (2004). Detecting moving objects using a single camera on a mobile robot in an outdoor environment. In in International Conference on Intelligent Autonomous Systems, pages 980-987.
  6. Malartre, F. (2011). Perception intelligente pour la navigation rapide de robots mobiles en environnement naturel. PhD thesis, Ecole Doctorale Science Pour l'Ingénieur de Clermont Ferrand.
  7. Royer, E., Lhuillier, M., Dhome, M., and Lavest, J. (2007). Monocular vision for mobile robot localization and autonomous navigation. International Journal of Computer Vision, 74:237-260. 10.1007/s11263-006- 0023-y.
  8. Rusinkiewicz, S. and Levoy, M. (2001). Efficient variants of the icp algorithm. In International Conference on 3-D Digital Imaging and Modeling.
  9. Slabaugh, G. G., Culbertson, W. B., Malzbender, T., Stevens, M. R., and Schafer, R. W. (2003). Methods for volumetric reconstruction of visual scenes. International Journal of Computer Vision, 57:179-199.
  10. Wurm, K. M., Hornung, A., Bennewitz, M., Stachniss, C., and Burgard, W. (2010). OctoMap: A probabilistic, flexible, and compact 3D map representation for robotic systems. In Proc. of the ICRA 2010 Workshop on Best Practice in 3D Perception and Modeling for Mobile Manipulation, Anchorage, AK, USA.
  11. Yilmaz, A., Li, X., and Shah, M. (2004). Contour based object tracking with occlusion handling in video acquired using mobile cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26:1531- 1536.
  12. Zeng, G., Paris, S., Quan, L., and Sillion, F. (2005). Progressive surface reconstruction from images using a local prior. In In ICCV, pages 1230-1237.
  13. Zografos, V., Nordberg, K., and Ellis, L. (2010). Sparse motion segmentation using multiple six-point consistencies. CoRR, abs/1012.2138.
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Paper Citation


in Harvard Style

Deymier C. and Chateau T. (2014). Toward Moving Objects Detection in 3D-Lidar and Camera Data . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: USA, (ICPRAM 2014) ISBN 978-989-758-018-5, pages 809-816. DOI: 10.5220/0004928608090816


in Bibtex Style

@conference{usa14,
author={Clement Deymier and Thierry Chateau},
title={Toward Moving Objects Detection in 3D-Lidar and Camera Data},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: USA, (ICPRAM 2014)},
year={2014},
pages={809-816},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004928608090816},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: USA, (ICPRAM 2014)
TI - Toward Moving Objects Detection in 3D-Lidar and Camera Data
SN - 978-989-758-018-5
AU - Deymier C.
AU - Chateau T.
PY - 2014
SP - 809
EP - 816
DO - 10.5220/0004928608090816