1. Hedau, V., Hoiem, D. and Forsyth, D., “Recovering Free Space of Indoor Scenes from a Single Image,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2012) pp. 2807–2814.
2. Labayrade, R., Aubert, D. and Tarel, J. P., “Real Time Obstacle Detection in Stereovision on Non Flat Road Geometry Through “v-Disparity” Representation,” Proceedings of the IEEE Intelligent Vehicle Symposium, vol. 2, (2002) pp. 646–651.
3. Maturana, D., Chou, P. W., Uenoyama, M. and Scherer, S., “Real-time semantic mapping for autonomous off-road navigation,” In: Field and Service Robotics, Springer, Cham. (2018) pp. 335–350.
4. Suger, B., Steder, B., and Burgard, W., “Traversability analysis for mobile robots in outdoor environments: A semi-supervised learning approach based on 3D-lidar data,” In: Robotics and Automation (ICRA), 2015 IEEE International Conference on (IEEE, May 2015) pp. 3941–3946.
5. Dahlkamp, H. et al., “Self-Supervised Monocular Road Detection in Desert Terrain,” Proceedings of Robotics: Science and Systems, Philadelphia.
6. Thrun, S., Montemerlo, M. and Aron, A., “Probabilistic Terrain Analysis for High-Speed Desert Driving,” Proceedings of Robotics: Science and Systems, pp. 16–19.
7. Milella, A. et al., “Visual ground segmentation by radar supervision,” Robot. Auton. Syst. 62 (5), 696–706 (2014).
8. Milella, A., Reina, G. and Foglia, M. M., “A Multi-Baseline Stereo System for Scene Segmentation in Natural Environments,” Proceedings of the IEEE International Conference Technologies for Practical Robot Applications (TePRA), (2013) pp. 1–6.
9. Reina, G. and Milella, A., “Towards autonomous agriculture: Automatic ground detection using trinocular stereovision,” Sensors 12 (9), 12405–12423 (2012).
10. Howard, A. et al., “Towards learned traversability for robot navigation: From underfoot to the far field,” J. Field Robot. 23 (11–12), 1005–1017 (2006).
11. Kim, D., Oh, S. M. and Rehg, J. M., “Traversability Classification for UGV Navigation: A Comparison of Patch and Superpixel Representations,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (2007) pp. 3166–3173.
12. Vernaza, P., Taskar, B. and Lee, D. D., “Online, Self-Supervised Terrain Classification Via Discriminatively Trained Submodular Markov Random Fields,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), (2008) pp. 2750–2757.
13. Hadsell, R. et al., “Learning long-range vision for autonomous off-road driving,” J. Field Robot. 26 (2), 120–144 (2009).
14. Moghadam, P. and Wijesoma, W. S., “Online, Self-Supervised Vision-Based Terrain Classification in Unstructured Environments,” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), (2009) pp. 3100–3105.
15. Kostavelis, I., Nalpantidis, L. and Gasteratos, A., “Collision risk assessment for autonomous robots by offline traversability learning,” Robot. Auton. Syst. 60 (11), 1367–1376 (2012).
16. Reina, G., Milella, A. and Worst, R., “Lidar and stereo combination for traversability assessment of off-road robotic vehicles,” Robotica 34 (12), 2823–2841 (2016).
17. Bajracharya, M. et al., “Autonomous off-road navigation with end-to-end learning for the LAGR program,” J. Field Robot. 26 (1), 3–25 (2009).
18. Brooks, C. A. and Iagnemma, K., “Self-supervised terrain classification for planetary surface exploration rovers,” J. Field Robot. 29 (3), 445–468 (2012).
19. Wurm, K. M. et al., “Identifying vegetation from laser data in structured outdoor environments,” Robot. Auton. Syst. 62 (5), 675–684 (2014).
20. Hu, Z. and Uchimura, K., “Uv-Disparity: An Efficient Algorithm for Stereovision Based Scene Analysis,” Proceedings of the IEEE Intelligent Vehicles Symposium, (2005) pp. 48–54.
21. Harakeh, A., Asmar, D. and Shammas, E., “Ground Segmentation and Occupancy Grid Generation Using Probability Fields,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (2015) pp. 695–702.
22. Harakeh, A., Asmar, D. and Shammas, E., “Identifying good training data for self-supervised free space estimation.” In: Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on (IEEE, June 2016) pp. 3530–3538.
23. Yiruo, D., Wenjia, W. and Yukihiro, K., “Complex Ground Plane Detection Based on v-Disparity Map in Off-Road Environment,” Proceedings of the IEEE Intelligent Vehicles Symposium (IV), (2013) pp. 1137–1142.
24. Zhao, J., Katupitiya, J. and Ward, J., “Global Correlation Based Ground Plane Estimation Using v-Disparity Image,” Proceedings of the IEEE International Conference on Robotics and Automation, (2007) pp. 529–534.
25. Bishop, C. M., Pattern Recognition and Machine Learning (Springer, New York, 2006).
26. Denis, F. et al., “Text Classification and Co-Training from Positive and Unlabeled Examples,” Proceedings of the ICML Workshop: The Continuum from Labeled to Unlabeled Data (2003) pp. 80–87.
27. He, J., Zhang, Y., Li, X. and Wang, Y., “Naive bayes classifier for positive unlabeled learning with uncertainty,” In: Proceedings of the 2010 SIAM International Conference on Data Mining (Society for Industrial and Applied Mathematics, April 2010) pp. 361–372.
28. Scholkopf, B., Williamson, R. C., Smola, A. J. and Shawe-Taylor, J., “SV Estimation of a Distribution's Support,” Proc. 14th Neural Information Processing Systems (NIPS '00), (2000) pp. 582–588.