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The process of designing costmaps for off-road driving tasks is often a challenging and engineering-intensive task.
G. E. Uhlenbeck and L. S. Ornstein, “On the theory of the brownian motion,” Physical review , vol. 36, no. 5, p. 823, 1930
1930
Earlier work this paper cites.
E. T. Jaynes, “On the rationale of maximum-entropy methods,” Proceedings of the IEEE , vol. 70, no. 9, pp. 939–952, 1982
1982
Earlier work this paper cites.
M.-P. Dubuisson and A. K. Jain, “A modified hausdorff distance for object matching,” in Proceedings of 12th international conference on pattern recognition , vol. 1. IEEE, 1994, pp. 566–568
1994
Earlier work this paper cites.
R. T. Rockafellar, S. Uryasev et al. , “Optimization of conditional value-at-risk,” Journal of risk , vol. 2, pp. 21–42, 2000
2000
Earlier work this paper cites.
P. Abbeel and A. Y. Ng, “Apprenticeship learning via inverse reinforcement learning,” in Proceedings of the twenty-first international conference on Machine learning , 2004, p. 1
2004
Earlier work this paper cites.
S. R. Team, “Stanford racing team’s entry in the 2005 darpa grand challenge,” Published on DARPA Grand Challenge , 2005
2005
Earlier work this paper cites.
L. D. Jackel, E. Krotkov, M. Perschbacher et al. , “The darpa lagr program: Goals, challenges, methodology, and phase i results,” Journal of Field robotics , vol. 23, no. 11-12, pp. 945–973, 2006
2006
Earlier work this paper cites.
N. D. Ratliff, J. A. Bagnell, and M. A. Zinkevich, “Maximum margin planning,” in Proceedings of the 23rd international conference on Machine learning , 2006, pp. 729–736
2006
Earlier work this paper cites.
B. D. Ziebart, A. L. Maas, J. A. Bagnell et al. , “Maximum entropy inverse reinforcement learning.” in Aaai , vol. 8. Chicago, IL, USA, 2008, pp. 1433–1438
2008
Earlier work this paper cites.
N. D. Ratliff, D. Silver, and J. A. Bagnell, “Learning to search: Functional gradient techniques for imitation learning,” Autonomous Robots , vol. 27, no. 1, pp. 25–53, 2009
2009
Earlier work this paper cites.
J. A. Bagnell, D. Bradley, D. Silver et al. , “Learning for autonomous navigation,” IEEE Robotics & Automation Magazine , vol. 17, no. 2, pp. 74–84, 2010
2010
Earlier work this paper cites.
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
P. Fankhauser and M. Hutter, “A universal grid map library: Implementation and use case for rough terrain navigation,” in Robot Operating System (ROS) . Springer, 2016, pp. 99–120
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European conference on computer vision . Springer, 2016, pp. 630–645
2016
Earlier work this paper cites.
M. Wulfmeier, D. Rao, D. Z. Wang et al. , “Large-scale cost function learning for path planning using deep inverse reinforcement learning,” The International Journal of Robotics Research , vol. 36, no. 10, pp. 1073–1087, 2017
2017
Cited alongside, same era.
G. Williams, A. Aldrich, and E. A. Theodorou, “Model predictive path integral control: From theory to parallel computation,” Journal of Guidance, Control, and Dynamics , vol. 40, no. 2, pp. 344–357, 2017
2017
Cited alongside, same era.
P. Krüsi, P. Furgale, M. Bosse, and R. Siegwart, “Driving on point clouds: Motion planning, trajectory optimization, and terrain assessment in generic nonplanar environments,” Journal of Field Robotics , vol. 34, no. 5, pp. 940–984, 2017
2017
Cited alongside, same era.
G. Williams, N. Wagener, B. Goldfain et al. , “Information theoretic mpc for model-based reinforcement learning,” in 2017 IEEE International Conference on Robotics and Automation . IEEE, 2017, pp. 1714–1721
2017
J. Mai, “System design, modelling, and control for an off-road autonomous ground vehicle,” Master’s thesis, Carnegie Mellon University, Pittsburgh, PA, July 2020
2020
Later among the works it cites.
S. H. Young, “Robot autonomy in complex environments,” in Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III , vol. 11746. SPIE, 2021, p. 1174602
2021
Later among the works it cites.
2021
Later among the works it cites.
A. Choudhry, B. Moon, J. Patrikar et al. , “Cvar-based flight energy risk assessment for multirotor uavs using a deep energy model,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 262–268
2021
Later among the works it cites.
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Cited alongside, same era.
M. Wigness, J. G. Rogers, and L. E. Navarro-Serment, “Robot navigation from human demonstration: Learning control behaviors,” in 2018 IEEE international conference on robotics and automation (ICRA) . IEEE, 2018, pp. 1150–1157
2018
Cited alongside, same era.
2018
Cited alongside, same era.
P. Fankhauser, M. Bjelonic, C. D. Bellicoso et al. , “Robust rough-terrain locomotion with a quadrupedal robot,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 5761–5768
2018
Cited alongside, same era.
D. Maturana, P.-W. Chou, M. Uenoyama, and S. Scherer, “Real-time semantic mapping for autonomous off-road navigation,” in Field and Service Robotics . Springer, 2018, pp. 335–350
2018
Cited alongside, same era.
M. Wigness, S. Eum, J. G. Rogers et al. , “A rugd dataset for autonomous navigation and visual perception in unstructured outdoor environments,” in International Conference on Intelligent Robots and Systems , 2019
2019
Cited alongside, same era.
A. G. Kendall, “Geometry and uncertainty in deep learning for computer vision,” Ph.D. dissertation, University of Cambridge, UK, 2019
2019
Cited alongside, same era.
D. Pathak, D. Gandhi, and A. Gupta, “Self-supervised exploration via disagreement,” in International conference on machine learning . PMLR, 2019, pp. 5062–5071
2019
Cited alongside, same era.
Z. Zhu, N. Li, R. Sun et al. , “Off-road autonomous vehicles traversability analysis and trajectory planning based on deep inverse reinforcement learning,” in 2020 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2020, pp. 971–977
2020
Cited alongside, same era.
D. D. Fan, A.-A. Agha-Mohammadi, and E. A. Theodorou, “Learning risk-aware costmaps for traversability in challenging environments,” IEEE Robotics and Automation Letters , vol. 7, no. 1, pp. 279–286, 2021
2021
Later among the works it cites.
S. J. Wang, S. Triest, W. Wang et al. , “Rough terrain navigation using divergence constrained model-based reinforcement learning,” in 5th Annual Conference on Robot Learning , 2021
2021
Later among the works it cites.
S. Zhao, H. Zhang, P. Wang et al. , “Super odometry: Imu-centric lidar-visual-inertial estimator for challenging environments,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 8729–8736
2021
Later among the works it cites.
S. Scherer, V. Agrawal, G. Best et al. , “Resilient and modular subterranean exploration with a team of roving and flying robots,” Field Robotics Journal , pp. 678–734, May 2022
2022
Later among the works it cites.
“Arl autonomy stack,” 2022, accessed 13-September-2022. [Online]. Available: https://www.arl.army.mil/business/collaborative-alliances/current-cras/sara-cra/sara-overview/
2022
Later among the works it cites.
P. Borges, T. Peynot, S. Liang et al. , “A survey on terrain traversability analysis for autonomous ground vehicles: Methods, sensors, and challenges,” Field Robotics , vol. 2, no. 1, pp. 1567–1627, 2022
2022
Later among the works it cites.
K. Lee, D. Isele, E. A. Theodorou, and S. Bae, “Spatiotemporal costmap inference for mpc via deep inverse reinforcement learning,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 3194–3201, 2022
2022
Later among the works it cites.
A. Shaban, X. Meng, J. Lee et al. , “Semantic terrain classification for off-road autonomous driving,” in Conference on Robot Learning . PMLR, 2022, pp. 619–629
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.