Fetching the paper…
Reading the bibliography…
Mobile ground robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths.
A. T. Le, D. C. Rye, and H. F. Durrant-Whyte, “Estimation of track-soil interactions for autonomous tracked vehicles,” in Proc. of Int. Conf. on Robotics and Autom. (ICRA) . IEEE, 1997, pp. 1388–1393
1997
Earlier work this paper cites.
A. Stentz, J. Bares, S. Singh, and P. Rowe, “A robotic excavator for autonomous truck loading,” in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) . IEEE, 1998, pp. 1885–1893
1998
Earlier work this paper cites.
L. Breiman, “Random Forests,” Machine Learning , vol. 45, no. 1, pp. 5–32, 2001
2001
Earlier work this paper cites.
S. Lacroix, et al. , “Autonomous rover navigation on unknown terrains: Functions and integration,” The Int. Journal of Robotics Research , vol. 21, no. 10-11, pp. 917–942, 2002
2002
Earlier work this paper cites.
K. Perlin, “Improving noise,” in ACM Transactions on Graphics (TOG) , vol. 21, no. 3. ACM, 2002, pp. 681–682
2002
Earlier work this paper cites.
N. Koenig and A. Howard, “Design and use paradigms for gazebo, an open-source multi-robot simulator,” in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , vol. 3, 2004, pp. 2149–2154
2004
Earlier work this paper cites.
C. M. Bishop, Pattern Recognition and Machine Learning . Springer-Verlag New York, Inc., 2006
2006
Earlier work this paper cites.
C. A. Brooks and K. D. Iagnemma, “Self-supervised classification for planetary rover terrain sensing,” in Proc. of the IEEE Aerospace Conf. IEEE, 2007, pp. 1–9
2007
Earlier work this paper cites.
R. Hadsell, M. Scoffier, U. Muller, Y. LeCun, and P. Sermanet, “Mapping and planning under uncertainty in mobile robots with long-range perception,” in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) . IEEE, 2008, pp. 1–6
2008
Earlier work this paper cites.
A. Shirkhodaie and E. Tunstel, “Rover Traversability Assessment Via Visual Sensing Of Spatial And Textural Terrain Image Features,” Intell. Autom. & Soft Computing , vol. 14, no. 3, pp. 295–317, 2008
2008
Earlier work this paper cites.
T. Huntsberger, et al. , “Characterization of the ROAMS simulation environment for testing rover mobility on sloped terrain,” in Proc. of the Int. Symp. on Artificial Intelligence, Robotics and Automation in Space , 2008
2008
Earlier work this paper cites.
R. Hudjakov and M. Tamre, “Aerial imagery terrain classification for long-range autonomous navigation,” in Proc. of the Int. Symp. on Optomechatronic Technologies , 2009, pp. 88–91
2009
Earlier work this paper cites.
R. M. Smelik, K. J. De Kraker, T. Tutenel, R. Bidarra, and S. A. Groenewegen, “A survey of procedural methods for terrain modelling,” in Proc. of the CASA Workshop on 3D Advanced Media In Gaming And Simulation (3AMIGAS) , 2009, pp. 25–34
2009
Cited alongside, same era.
E. Ugur and E. Sahin, “Traversability: A case study for learning and perceiving affordances in robots,” Adaptive Behavior , vol. 18, pp. 258–284, 2010
2010
Cited alongside, same era.
S. Karumanchi, T. Allen, T. Bailey, and S. Scheding, “Non-parametric learning to aid path planning over slopes,” The Int. Journal of Robotics Research , vol. 29, no. 8, pp. 997–1018, 2010
2010
Cited alongside, same era.
D. Silver, J. A. Bagnell, and A. Stentz, “Learning from demonstration for autonomous navigation in complex unstructured terrain,” Int. Journal of Robotic Research , vol. 29, no. 12, pp. 1565–1592, 2010
2010
Cited alongside, same era.
M. Längkvist, A. Kiselev, M. Alirezaie, and A. Loutfi, “Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks,” Remote Sensing , vol. 8, no. 4, 2016
2016
Later among the works it cites.
J. Delmerico, E. Mueggler, J. Nitsch, and D. Scaramuzza, “Active autonomous aerial exploration for ground robot path planning,” IEEE Robotics and Autom. Letters , 2016
2016
Later among the works it cites.
M. Bellone, “Watch your step! terrain traversability for robot control,” Robot Control , 2016
2016
Later among the works it cites.
M. Wermelinger, et al. , “Navigation planning for legged robots in challenging terrain,” in Proc. of the 2016 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) . IEEE, 2016, pp. 2153–0866
2016
Later among the works it cites.
A. Giusti, et al. , “A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots,” IEEE Robotics and Autom. Letters , vol. 1, no. 2, pp. 661–667, 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Lagae, et al. , “A survey of procedural noise functions,” in Comp. Graphics Forum , vol. 29, no. 8. Wiley, 2010, pp. 2579–2600
2010
Cited alongside, same era.
Y. N. Khan, P. Komma, and A. Zell, “High resolution visual terrain classification for outdoor robots,” in Proc. of the IEEE Int. Conf. on Comp. Vision Workshops (ICCV Workshops) , 2011, pp. 1014–1021
2011
Cited alongside, same era.
D. Ciregan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification,” in Proc. of the Conf. on Comp. Vision and Pattern Recognition (CVPR) . IEEE, 2012, pp. 3642–3649
2012
Cited alongside, same era.
P. Papadakis, “Terrain traversability analysis methods for unmanned ground vehicles: A survey,” Engineering Applications of Artificial Intelligence , vol. 26, no. 4, pp. 1373–1385, 2013
2013
Cited alongside, same era.
D. Belter, P. Labecki, and P. Skrzypczynski, “An exploration-based approach to terrain traversability assessment for a walking robot,” in Proc. of the Int. Symp. on Safety, Security, and Rescue Robotics (SSRR) . IEEE, 2013, pp. 2374–3247
2013
Cited alongside, same era.
M. A. Bekhti, Y. Kobayashi, and K. Matsumura, “Terrain traversability analysis using multi-sensor data correlation by a mobile robot,” in Proc. of the IEEE/SICE Int. Symp. on System Integration . IEEE, 2014, pp. 615–620
2014
Cited alongside, same era.
X. Peng, B. Sun, K. Ali, and K. Saenko, “Learning deep object detectors from 3d models,” in Proc. of the Int. Conf. on Comp. Vision (ICCV) , ser. ICCV ’15. IEEE, 2015, pp. 1278–1286
2015
Cited alongside, same era.
M. Abadi, et al. , “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: http://tensorflow.org/
2015
Cited alongside, same era.
2016
Later among the works it cites.
SenseFly, “Elevation datasets,” 2016. [Online]. Available: https://www.sensefly.com
2016
Later among the works it cites.
R. Käslin, et al. , “Collaborative localization of aerial and ground robots through elevation maps,” in Proc. of the IEEE Int. Symp. on Safety, Security, and Rescue Robotics (SSRR) . IEEE, 2016, pp. 284–290
2016
Later among the works it cites.
2016
Later among the works it cites.
“Nips 2017: Learning to run,” https://www.crowdai.org/challenges/nips-2017-learning-to-run , 2017
2017
Closest in time.
A. A. Rusu, et al. , “Sim-to-real robot learning from pixels with progressive nets,” in Proc. of the 1st Annual Conf. on Robot Learning, (CoRL 2017) , 2017, pp. 262–270
2017
Closest in time.
R. O. Chavez-Garcia, J. Guzzi, L. M. Gambardella, and A. Giusti, “Image classification for ground traversability estimation in robotics,” in Proc. of the 18th Int. Conf. on Advanced Concepts for Intelligent Vision Systems (ACIVS) . Springer Int. Publishing, 2017, pp. 325–336
2017
Closest in time.
F. Chollet, Deep Learning with Python . Manning Publications Company, 2017
2017
Closest in time.