Abstract
This paper focuses on the use of spherical cameras for autonomous robot navigation tasks. Previous works of literature mainly lie in two categories: scene oriented simultaneous localization and mapping and robot oriented heading fields lane detection and trajectory tracking. Those methods face the challenges of either high computation cost or heavy labelling and calibration requirements. In this paper, we propose to formulate the spherical image navigation as an image classification problem, which significantly simplifies the orientation estimation and path prediction procedure and accelerates the navigation process. More specifically, we train an end-to-end convolutional network on our spherical image dataset with novel orientation categories labels. This trained network can give precise predictions on potential path directions with single spherical images. Experimental results on our Spherical-Navi dataset demonstrate that the proposed approach outperforms the comparing methods in realistic applications.
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
Similar content being viewed by others
References
Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, pp. 225–234 (2007)
Caruso, D., Engel, J., Cremers, D.: Large-scale direct slam for omnidirectional cameras. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2015)
Hillel, A.B., Lerner, R., Levi, D., Raz, G.: Recent progress in road and lane detection: a survey. Mach. Vis. Appl. 25, 727–745 (2014)
Liang, X., Wang, H., Chen, W., Guo, D., Liu, T.: Adaptive image-based trajectory tracking control of wheeled mobile robots with an uncalibrated fixed camera. IEEE Trans. Control Syst. Technol. 23, 2266–2282 (2015)
Lu, K., Li, J., An, X., He, H.: Vision sensor-based road detection for field robot navigation. Sensors 15, 29594–29617 (2015)
Chang, C.K., Siagian, C., Itti, L.: Mobile robot vision navigation & localization using gist and saliency. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4147–4154 (2010)
Pomerleau, D.A.: Efficient training of artificial neural networks for autonomous navigation. Neural Comput. 3, 88–97 (1991)
Hadsell, R., Sermanet, P., Ben, J., Erkan, A., Scoffier, M., Kavukcuoglu, K., Muller, U., LeCun, Y.: Learning long-range vision for autonomous off-road driving. J. Field Robot. 26, 120–144 (2009)
Giusti, A., Guzzi, J., Ciresan, D., He, F.L., Rodriguez, J.P., Fontana, F., Faessler, M., Forster, C., Schmidhuber, J., Di Caro, G., Scaramuzza, D., Gambardella, L.: A machine learning approach to visual perception of forest trails for mobile robots. IEEE Robot. Autom. Lett. 1, 661–667 (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp. 1097–1105 (2012)
Ran, L., Zhang, Y., Hua, G.: CANNET: context aware nonlocal convolutional networks for semantic image segmentation. In: IEEE International Conference on Image Processing (ICIP), pp. 4669–4673 (2015)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 38, 142–158 (2016)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 807–814 (2010)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1026–1034 (2015)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 448–456 (2015)
Collobert, R., Kavukcuoglu, K., Farabet, C.: Torch7: a matlab-like environment for machine learning. In: BigLearn, NIPS Workshop. Number EPFL-CONF-192376 (2011)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (No. 61672429, No. 61502364, No. 61272288, No. 61231016), ShenZhen Science and Technology Foundation (JCYJ20160229172932237), Northwestern Polytechnical University (NPU) New AoXiang Star (No. G2015KY0301), Fundamental Research Funds for the Central Universities (No. 3102015AX007), NPU New People and Direction (No. 13GH014604).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ran, L., Zhang, Y., Yang, T., Zhang, P. (2016). Autonomous Wheeled Robot Navigation with Uncalibrated Spherical Images. In: Zhang, Z., Huang, K. (eds) Intelligent Visual Surveillance. IVS 2016. Communications in Computer and Information Science, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-10-3476-3_6
Download citation
DOI: https://doi.org/10.1007/978-981-10-3476-3_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3475-6
Online ISBN: 978-981-10-3476-3
eBook Packages: Computer ScienceComputer Science (R0)