Abstract
A visual memory (VM) is a topological map that represents an environment as a direct graph of key images. Thus, visual information acquired from cameras onboard the robot are the only data to construct the map. This work presents the construction of a VM suited for the humanoid robot navigation framework. Additionally, a genetic algorithm that estimates the epipolar geometry is proposed to tackle the problem of image matching used within the VM construction process. Experimental results using a humanoid robot dataset are presented to validate the efficacy of our approach. Further, the solution for image matching based on the proposed genetic algorithm was compared with RANSAC.
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References
Z. Chen, S.T. Birchfield, Qualitative vision-based mobile robot navigation, in Proceeding of ICRA (2006), pp. 2686–2692
O. Booij, Z. Terwijn, Z. Zivkovic et al., Navigation using an appearance-based topological map, in Proceeding of ICRA (2007), pp. 3927–3932
J. Ido, Y. Shimizu, Y. Matsumoto et al., Indoor navigation for a humanoid robot using a view sequence. Int. J. Robot. Res. 28(2), 315–325 (2009)
A. Diosi, S. Segvic, A. Remazeilles et al., Experimental evaluation of autonomous driving based on visual memory and image-based visual servoing. IEEE Trans. Intell. Transp. Syst. 12(3), 833–870 (2011)
H.M. Becerra, C. Sagues, Y. Mezouar et al., Visual navigation of wheeled mobile robots using direct feedback of a geometric constraint. Auton. Robots 37(2), 137–156 (2014)
J. Courbon, Y. Mezouar, P. Martinet, Indoor navigation of a non-holonomic mobile robot using a visual memory. Auton. Robots 25, 253–266 (2008)
J. Courbon, Y. Mezouar, P. Martinet, Autonomous navigation of vehicles from a visual memory using a generic camera model. IEEE Trans. Intell. Transp. Syst. 10(3), 392–402 (2009)
N.G. Aldana-Murillo, J.B. Hayet, H.M. Becerra, Comparison of local descriptors for humanoid robots localization using a visual bag of words approach. Intell. Autom. Soft Co., 1–11 (2017)
J. Delfin, H.M. Becerra, G. Arechavaleta, Humanoid localization and navigation using a visual memory, in International Conference on Humanoid Robots (Humanoids) (2016), pp. 725–731
J. Delfin, H.M. Becerra, G. Arechavaleta, Humanoid navigation using a visual memory with obstacle avoidance. Rob. Auton. Sys. 109, 109–124 (2018)
E. Ovalle, Generación de una memoria visual para la navegación autónoma de un robot humanoide (2016)
R.I. Hartley, A. Zisserman, Multiple View Geometry in Computer Visionm, 2nd edn. (Cambridge University Press, 2004)
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López-Martínez, A., Cuevas, F.J., Sosa-Balderas, J.V. (2019). Visual Memory Construction for Autonomous Humanoid Robot Navigation. In: Martínez-García, A., Bhattacharya, I., Otani, Y., Tutsch, R. (eds) Progress in Optomechatronic Technologies . Springer Proceedings in Physics, vol 233. Springer, Singapore. https://doi.org/10.1007/978-981-32-9632-9_12
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DOI: https://doi.org/10.1007/978-981-32-9632-9_12
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