Abstract
Mobile robotics has become an essential content in many subjects within most Bachelor’s and Master’s degrees in engineering. Visual sensors have emerged as a powerful tool to perform reliable localization and mapping tasks for a mobile robot. Moreover, the use of images permits achieving other high level tasks such as object and people detection, recognition, or tracking. Nonetheless, our teaching experience confirms that students encounter many difficulties before dealing with visual localization and mapping algorithms. Initial stages such as data acquisition (images and trajectory), preprocessing or visual feature extraction, usually imply a considerable effort for many students. Consequently, the teaching process is prolonged, whereas the active learning and the students’ achievement are certainly affected. Considering these facts, we have implemented a Matlab software tool to generate an open variety of virtual environments. This allows students to easily obtain synthetic raw data, according to a predefined robot trajectory inside the designed environment. The virtualization software also produces a set of images along the trajectory for performing visual localization and mapping experiments. As a result, the overall testing procedure is alleviated and students report to take better advantage of the lectures and the practical sessions, thus demonstrating higher achievement in terms of comprehension of fundamental mobile robotics concepts. Comparison results regarding the achievement of students, engagement, satisfaction and attitude to the use of the tool, are presented.
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
Payá, L., Gil, A., Reinoso, O.: A state-of-the-art review on mapping and localization of mobile robots using omnidirectional vision sensors. J. Sens. 2017, 1–20 (2017)
Valiente, D., Payá, L., Jiménez, L.M., Sebastián, J.M., Reinoso, O.: Visual information fusion through bayesian inference for adaptive probability-oriented feature matching. Sensors 18(7), 2041 (2018)
Cole, D., Newman, P.: Using laser range data for 3D SLAM in outdoor environments. In: IEEE ICRA, U.S.A., pp. 1556–1563 (2006)
Lee, S.J., Song, J.B.: A new sonar salient feature structure for EKF-based SLAM. In: IEEE IROS, pp. 5966–5971 (2010)
Ferreira, N.M.F., Freitas, E.D.C.: Robotics as multi-disciplinary learning: a summer course perspective. In: 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), pp. 536–543, July 2018
Oliver, J., Toledo, R., Valderrama, E.: A learning approach based on robotics in computer science and computer engineering. In: IEEE EDUCON 2010 Conference, pp. 1343–1347, April 2010
López-Gay, R., Martínez Sáez, J., Martínez Torregrosa, J.: Obstacles to mathematization in physics: the case of the differential. Sci. Educ. 24, 591–613 (2015)
Ferrerira, N.M.F., Freitas, E.D.C.: Computer applications for education on industrial robotic systems. Comput. Appl. Eng. Educ. 26(5), 1186–1194 (2018)
Xuemei, L., Jiashu, C., Jinhu, L., Gang, X.: Innovative education activities with vision based robot navigation system. In: 2010 International Conference on Optics, Photonics and Energy Engineering (OPEE), vol. 2, pp. 505–507, May 2010
Gil, A., Juliá, M., Reinoso, O.: MRXT: the multi-robot exploration tool. Int. J. Adv. Robot. Syst. 12(29), 1–10 (2015)
Tosello, E., Michieletto, S., Pagello, E.: Training master students to program both virtual and real autonomous robots in a teaching laboratory. In: 2016 IEEE Global Engineering Education Conference (EDUCON), pp. 621–630, April 2016
Saraiva, A.A., Barros, M.P., Nogueira, A.T., Fonseca Ferreira, N.M., Valente, A.: Virtual interactive environment for low-cost treatment of mechanical strabismus and amblyopia. Information 9(7), 175 (2018)
Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Trans. Rob. 23(1), 34–46 (2007)
Mur, R., Tards, J.D.: ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-d cameras. IEEE Trans. Rob. 33(5), 1255–1262 (2017)
Liu, M., Siegwart, R.: Topological mapping and scene recognition with lightweight color descriptors for an omnidirectional camera. IEEE Trans. Rob. 30(2), 310–324 (2014)
Menegatti, E., Maeda, T., Ishiguro, H.: Image-based memory for robot navigation using properties of omnidirectional images. Robot. Auton. Syst. 47(4), 251–267 (2004)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893, June 2005
Friedman, A.: Framing pictures: the role of knowledge in automatized encoding and memory for gist. J. Exp. Psychol. Gen. 108, 316–55 (1979)
Harris, C.G., Stephens, M.: A combined corner and edge detector. In: Proceedings of Alvey Vision Conference, Manchester, UK (1988)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Proceedings of the European Conference on Computer Vision, Graz, Austria (2006)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: Proceedings of the 2011 International Conference on Computer Vision, Washington, DC, USA, pp. 2564–2571 (2011)
Valiente, D., Gil, A., Reinoso, O., Juliá, M., Holloway, M.: Improved omnidirectional odometry for a view-based mapping approach. Sensors 17(2), 325 (2017)
Acknowledgements
This work has been partially supported by: the Spanish Government (DPI2016-78361-R, AEI/FEDER, UE); the Valencian Research Council and the European Social Fund (post-doctoral grant APOSTD/2017/028).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Valiente, D., Berenguer, Y., Payá, L., Fonseca Ferreira, N.M., Reinoso, O. (2020). Environment Virtualization for Visual Localization and Mapping. In: Merdan, M., Lepuschitz, W., Koppensteiner, G., Balogh, R., Obdržálek, D. (eds) Robotics in Education. RiE 2019. Advances in Intelligent Systems and Computing, vol 1023. Springer, Cham. https://doi.org/10.1007/978-3-030-26945-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-26945-6_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26944-9
Online ISBN: 978-3-030-26945-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)