Abstract
This paper explores the idea of predicting the likely performance of a robot’s perception system based on past experience in the same workspace. In particular, we propose to build a place-specific model of perception performance from observations gathered over time. We evaluate our method in a classical decision making scenario in which the robot must choose when and where to drive autonomously in 60 km of driving data from an urban environment. We demonstrate that leveraging visual appearance within a state-of-the-art navigation framework increases the accuracy of our performance predictions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Neural Information Processing Systems (NIPS) (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015)
Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint 2015. arXiv:1511.00561
Hawke, J., Gurau, C., Tong, C.H., Posner, I.: Wrong today, right tomorrow: experience-based classification for robot perception. In: Field and Service Robotics (FSR), June 2015
Gurau, C., Hawke, J., Tong, C.H., Posner, I.: Learning on the job: improving robot perception through experience. In: Neural Information Processing Systems (NIPS) Workshop on Autonomously Learning Robots, Montreal, Quebec, Canada, 12 December 2014
Peynot, T., Underwood, J., Scheding, S.: Towards reliable perception for unmanned ground vehicles in challenging conditions. In: IROS, October 2009
Peynot, T., Scheding, S., Terho, S.: The marulan data sets: multi-sensor perception in a natural environment with challenging conditions. Int. J. Robot. Res. 29(13), 1602–1607 (2010)
Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: CVPR 2011, June 2011
Khosla, A., Zhou, T., Malisiewicz, T., Efros, A.A., Torralba, A.: Undoing the damage of dataset bias. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 158–171. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33718-5_12
Churchill, W., Tong, C.H., Gurau, C., Posner, I., Newman, P.: Know your limits: embedding localiser performance models in teach and repeat maps. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2015)
Dequaire, J., Tong, C.H., Churchill, W., Posner, I.: Off the beaten track: predicting localisation performance in visual teach and repeat. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, May 2016
Grimmett, H., Triebel, R., Paul, R., Posner, I.: Introspective classification for robot perception. Int. J. Robot. Res. (IJRR) (2015)
Zhang, P., Wang, J., Farhadi, A., Hebert, M., Parikh, D.: Predicting failures of vision systems. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2014)
Furgale, P., Barfoot, T.D.: Visual teach and repeat for long-range rover autonomy. J. Field Robot. 27(5), 534–560 (2010)
Churchill, W., Newman, P.: Experience-based navigation for long-term localisation. Int. J. Robot. Res. (IJRR) 32(14), 1645–1661 (2013)
Linegar, C., Churchill, W., Newman, P.: Work smart, not hard: recalling relevant experiences for vast-scale but time-constrained localisation. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, May 2015
Dollar, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532–1545 (2014)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA (2005)
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the kitti dataset. Int. J. Robot. Res. (IJRR) 32(11), 1231–1237 (2013)
Wang, D.Z., Posner, I.: Voting for voting in online point cloud object detection. In: Proceedings of Robotics: Science and Systems, Rome, Italy, July 2015
Acknowledgements
The authors gratefully acknowledge the support of this work by the European Community’s Seventh Framework Programme under grant agreement no FP7-610603 (EUROPA2) and by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant number EP/J012017/1. The authors would also like to thank Dushyant Rao for his helpful suggestions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Gurău, C., Tong, C.H., Posner, I. (2017). Fit for Purpose? Predicting Perception Performance Based on Past Experience. In: Kulić, D., Nakamura, Y., Khatib, O., Venture, G. (eds) 2016 International Symposium on Experimental Robotics. ISER 2016. Springer Proceedings in Advanced Robotics, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-50115-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-50115-4_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50114-7
Online ISBN: 978-3-319-50115-4
eBook Packages: EngineeringEngineering (R0)