Abstract
This paper introduces a flying robot mapping and localization proposal from an onboard depth camera. The miniature flying robot is part of an ongoing project related to ambient assisted living and home health. The flying depth camera is used with a double function; firstly, as a range sensor for mapping from scratch during navigation, and secondly, as a gray-scale camera for localization. The Harris corner detection algorithm is implemented as key point detector for the creation and/or identification of indoor spatial relations. During the localization phase, the spatial relations created from detected corners in the mapping phase are compared to the corners identified in the map. The flying robot position is estimated by matching these spatial relations.
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
References
D. Iwakura, K. Nonami, Indoor localization of flying robot by means of infrared sensors. J. Robot. Mechatron. 25, 201–210 (2013)
A. Fernández-Caballero, J.M. Latorre, J.M. Pastor, A. Fernández-Sotos, Improvement of the elderly quality of life and care through smart emotion regulation, Ambient Assisted Living and Daily Activities, pp. 348–355, 2014
J.C. Castillo, D. Carneiro, J. Serrano-Cuerda, P. Novais, A. Fernández-Caballero, J. Neves, A multi-modal approach for activity classification and fall detection. Int. J. Syst. Sci. 45, 810–824 (2014)
D. Carneiro, J.C. Castillo, P. Novais, A. Fernández-Caballero, J. Neves, Multimodal behavioral analysis for non-invasive stress detection. Expert Syst. Appl. 39, 13376–13389 (2012)
M. Oliver, F. Montero, A. Fernández-Caballero, P. González, J.P. Molina, RGB-D assistive technologies for acquired brain injury: description and assessment of user experience. Expert Syst. (2014). doi:10.1111/exsy.12096
A. Briod, P. Kornatowski, J.-C. Zufferey, D. Floreano, A collision-resilient flying robot. J. Field Robot. 31, 496–509 (2014)
K.M. Wurm, C. Stachniss, G. Grisetti, Bridging the gap between feature- and grid-based SLAM. Robot. Auton. Syst. 58, 140–148 (2010)
J. Martínez-Gómez, A. Fernández-Caballero, I. García-Varea, L. Rodríuez, C. Romero-González, A taxonomy of vision systems for ground mobile robots. Int. J. Adv. Robot. Syst. 11, 111 (2014)
T. Collins, Occupancy grid learning using contextual forward modelling. J. Intell. Robot. Syst. 64, 505–542 (2011)
S. Almansa-Valverde, J.C. Castillo, A. Fernández-Caballero, Mobile robot map building from time-of-flight camera. Expert Syst. Appl. 39, 8835–8843 (2012)
A. Ramisa, A. Goldhoorn, D. Aldavert, R. Toledo, R. Lopez de Mantaras, Combining invariant features and the ALV homing method for autonomous robot navigation based on panoramas. J. Intell. Robot. Syst. 64, 625–649 (2011)
M. Bosse, R. Zlot, Keypoint design and evaluation for place recognition in 2D lidar maps. Robot. Auton. Syst. 57, 1211–1224 (2009)
M. Cummins, P. Newman, FAB-MAP: probabilistic localization and mapping in the space of appearance. Int. J. Robot. Res. 27, 647–665 (2008)
D.G. Lowe, Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)
G. Arbeiter, J. Fischer, A. Verl, 3D perception and modeling for manipulation on Care-O-Bot 3, in 2010 IEEE International Conference on Robotics and Automation, p. 5, 2010
H. Bay, A. Ess, T. Tuytelaars, L. Van Gool, SURF: speeded up robust features. Comput. Vis. Image Underst. 110, 346–359 (2008)
J. Li, N.M. Allinson, A comprehensive review of current local features for computer vision. Neurocomputing 71, 1771–1787 (2008)
S. Smith, J. Brady, Susan: a new approach to low-level image-processing. Int. J. Comput. Vis. 23, 45–78 (1997)
C. Harris, M. Stephens, A combined corner and edge detector, in The Fourth Alvey Vision Conference, pp. 147–151, 1988
J. Lee, H. Ko, Gradient-based local affine invariant feature extraction for mobile robot localization in indoor environments. Pattern Recogn. Lett. 29, 1934–1940 (2008)
A. Fernández-Caballero, M.T. López, S. Saiz-Valverde, Dynamic stereoscopic selective visual attention (DSSVA): integrating motion and shape with depth in video segmentation. Expert Syst. Appl. 34, 1394–1402 (2008)
G. Bennett, Probability inequalities for the sum of independent random variables. J. Am. Stat. Assoc. 57, 33–45 (1962)
Acknowledgments
This work was partially supported by Spanish Ministerio de Economía y Competitividad / FEDER under TIN2013-47074-C2-1-R grant.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Belmonte, L.M., Castillo, J.C., Fernández-Caballero, A., Almansa-Valverde, S., Morales, R. (2015). Flying Depth Camera for Indoor Mapping and Localization. In: Mohamed, A., Novais, P., Pereira, A., Villarrubia González, G., Fernández-Caballero, A. (eds) Ambient Intelligence - Software and Applications. Advances in Intelligent Systems and Computing, vol 376. Springer, Cham. https://doi.org/10.1007/978-3-319-19695-4_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-19695-4_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19694-7
Online ISBN: 978-3-319-19695-4
eBook Packages: EngineeringEngineering (R0)