Abstract
In this paper we present several interest points detectors and we analyze their suitability when used as landmark extractors for vision-based simultaneous localization and mapping (vSLAM). For this purpose, we evaluate the detectors according to their repeatability under changes in viewpoint and scale. These are the desired requirements for visual landmarks. Several experiments were carried out using sequence of images captured with high precision. The sequences represent planar objects as well as 3D scenes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Transactions on Robotics 23(1) (2007)
Hähnel, D., Burgard, W., Fox, D., Thrun, S.: An efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements. In: IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, Las Vegas, NV, USA (2003)
Leonard, J., Durrant-Whyte, H.: Mobile robot localization by tracking geometric beacons. IEEE Transactions on Robotics and Automation 7(4) (1991)
Biber, P., Andreasson, H., Duckett, T., Schilling, A.: 3d modelling of indoor environments by a mobile robot with a laser scanner and panoramic camera. In: IEEE/RSJ Int. Conf. on Intelligent Robots & Systems (2004)
Eustice, R., Singh, H., Leonard, J.: Exactly sparse delayed-state filters. In: IEEE Int. Conf. on Robotics & Automation (2005)
Triebel, R., Burgard, W.: Improving simultaneous mapping and localization in 3d using global constraints. In: National Conference on Artificial Intelligence (2005)
Lemaire, T., Lacroix, S.: Monocular-vision based SLAM using line segments. In: IEEE Int. Conf. on Robotics & Automation (2007)
Frintrop, S., Jensfelt, P., Christensen, H.I.: Attentional landmark selection for visual slam. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (2006)
Little, J., Se, S., Lowe, D.: Vision-based mobile robot localization and mapping using scale-invariant features. In: IEEE Int. Conf. on Robotics & Automation, IEEE Computer Society Press, Los Alamitos (2001)
Gil, A., Reinoso, O., Burgard, W., Stachniss, C., Martínez Mozos, O.: Improving data association in rao-blackwellized visual SLAM. In: IEEE/RSJ Int. Conf. on Intelligent Robots & Systems (2006)
Valls Miro, J., Zhou, W., Dissanayake, G.: Towards vision based navigation in large indoor environments. In: IEEE/RSJ Int. Conf. on Intelligent Robots & Systems (2006)
Davison, A.J., Murray, D.W.: Simultaneous localisation and map-building using active vision. IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)
Hygounenc, E., Jung, I.K., Souères, P., Lacroix, S.: The autonomous blimp project of laas-cnrs: Achievements in flight control and terrain mapping. International Journal of Robotics Research 23(4-5) (2004)
Murillo, A.C., Guerrero, J.J., Sagüés, C.: Surf features for efficient robot localization with omnidirectional images. In: IEEE Int. Conf. on Robotics & Automation (2007)
Jensfelt, P., Kragic, D., Folkesson, J., Björkman, M.: A framework for vision based bearing only 3D SLAM. In: IEEE Int. Conf. on Robotics & Automation (2006)
Se, S., Lowe, D.G., Little, J.: Vision-based mobile robot localization and mapping using scale-invariant features. In: IEEE Int. Conf. on Robotics & Automation, IEEE Computer Society Press, Los Alamitos (2001)
Little, J., Se, S., Lowe, D.: Global localization using distinctive visual features. In: IEEE/RSJ Int. Conf. on Intelligent Robots & Systems (2002)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10) (2005)
Schmid, C., Mohr, R., Bauckhage, C.: Evaluaton of interest point detectors. International Journal of computer Vision 37(2) (2000)
Harris, C.G., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference (1998)
Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: Int. Conf. on Computer Vision (2001)
Lowe, D.: Object recognition from local scale-invariant features. In: Int. Conf. on Computer Vision (1999)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. Journal of computer Vision 2(60) (2004)
Bay, H., Tuytelaars, T., Van Gool, L.: Object recognition from local scale-invariant features. In: European Conference on Computer Vision (2006)
Smith, S.: A new class of corner finder. In: British Machine Vision Conference (1992)
Dorkó, G., Schmid, C.: Selection of scale invariant neighborhoods for object class recognition. In: Int. Conf. on Computer Vision (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mozos, Ó.M., Gil, A., Ballesta, M., Reinoso, O. (2007). Interest Point Detectors for Visual SLAM. In: Borrajo, D., Castillo, L., Corchado, J.M. (eds) Current Topics in Artificial Intelligence. CAEPIA 2007. Lecture Notes in Computer Science(), vol 4788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75271-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-75271-4_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75270-7
Online ISBN: 978-3-540-75271-4
eBook Packages: Computer ScienceComputer Science (R0)