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Neural Signed Distance Fields (SDFs) provide a differentiable environment representation to readily obtain collision checks and well-defined gradients for robot navigation tasks.
I. J. Cox, “Blanche-an experiment in guidance and navigation of an autonomous robot vehicle,” IEEE Transactions on robotics and automation , vol. 7, no. 2, pp. 193–204, 1991
1991
Earlier work this paper cites.
R. C. Coulter et al. , Implementation of the pure pursuit path tracking algorithm . Carnegie Mellon University, The Robotics Institute, 1992
1992
Earlier work this paper cites.
D. Fox, W. Burgard, and S. Thrun, “The dynamic window approach to collision avoidance,” IEEE Robotics & Automation Magazine , vol. 4, no. 1, pp. 23–33, 1997
1997
Earlier work this paper cites.
W. Burgard, A. B. Cremers, D. Fox, D. Hähnel, G. Lakemeyer, D. Schulz, W. Steiner, and S. Thrun, “Experiences with an interactive museum tour-guide robot,” Artificial intelligence , vol. 114, no. 1-2, pp. 3–55, 1999
1999
Earlier work this paper cites.
S. Thrun, M. Bennewitz, W. Burgard, A. B. Cremers, F. Dellaert, D. Fox, D. Hahnel, C. Rosenberg, N. Roy, J. Schulte, et al. , “Minerva: A second-generation museum tour-guide robot,” in Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No. 99CH36288C) , vol. 3. IEEE, 1999
1999
Earlier work this paper cites.
G. N. DeSouza and A. C. Kak, “Vision for mobile robot navigation: A survey,” IEEE transactions on pattern analysis and machine intelligence , vol. 24, no. 2, pp. 237–267, 2002
2002
Earlier work this paper cites.
S. Karaman and E. Frazzoli, “Sampling-based algorithms for optimal motion planning,” The international journal of robotics research , vol. 30, no. 7, pp. 846–894, 2011
2011
Earlier work this paper cites.
E. S. Jones and S. Soatto, “Visual-inertial navigation, mapping and localization: A scalable real-time causal approach,” The International Journal of Robotics Research , vol. 30, no. 4, pp. 407–430, 2011
2011
Earlier work this paper cites.
M. Zucker, N. Ratliff, A. D. Dragan, M. Pivtoraiko, M. Klingensmith, C. M. Dellin, J. A. Bagnell, and S. S. Srinivasa, “Chomp: Covariant hamiltonian optimization for motion planning,” The International journal of robotics research , vol. 32, no. 9-10, pp. 1164–1193, 2013
2013
Earlier work this paper cites.
J. D. Gammell, S. S. Srinivasa, and T. D. Barfoot, “Informed rrt: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic,” in 2014 IEEE/RSJ international conference on intelligent robots and systems . IEEE, 2014, pp. 2997–3004
2014
Earlier work this paper cites.
P. Mirowski, R. Pascanu, F. Viola, H. Soyer, A. Ballard, A. Banino, M. Denil, R. Goroshin, L. Sifre, K. Kavukcuoglu, et al. , “Learning to navigate in complex environments,” in International Conference on Learning Representations , 2016
2016
Earlier work this paper cites.
H. Oleynikova, A. Millane, Z. Taylor, E. Galceran, J. Nieto, and R. Siegwart, “Signed distance fields: A natural representation for both mapping and planning,” in RSS 2016 workshop: geometry and beyond-representations, physics, and scene understanding for robotics . University of Michigan, 2016
2016
Earlier work this paper cites.
H. Oleynikova, Z. Taylor, M. Fehr, R. Siegwart, and J. Nieto, “Voxblox: Incremental 3d euclidean signed distance fields for on-board mav planning,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 1366–1373
2017
Earlier work this paper cites.
S. Gupta, J. Davidson, S. Levine, R. Sukthankar, and J. Malik, “Cognitive mapping and planning for visual navigation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2616–2625
2017
Earlier work this paper cites.
C. Finn, T. Yu, T. Zhang, P. Abbeel, and S. Levine, “One-shot visual imitation learning via meta-learning,” in Conference on robot learning . PMLR, 2017, pp. 357–368
2017
Cited alongside, same era.
Y. Zhu, R. Mottaghi, E. Kolve, J. J. Lim, A. Gupta, L. Fei-Fei, and A. Farhadi, “Target-driven visual navigation in indoor scenes using deep reinforcement learning,” in 2017 IEEE international conference on robotics and automation (ICRA) . IEEE, 2017, pp. 3357–3364
2017
Cited alongside, same era.
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, “Deepsdf: Learning continuous signed distance functions for shape representation,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019
2019
Cited alongside, same era.
L. Han, F. Gao, B. Zhou, and S. Shen, “Fiesta: Fast incremental euclidean distance fields for online motion planning of aerial robots,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 4423–4430
J. Ortiz, A. Clegg, J. Dong, E. Sucar, D. Novotny, M. Zollhoefer, and M. Mukadam, “isdf: Real-time neural signed distance fields for robot perception,” in Robotics: Science and Systems , 2022
2022
Later among the works it cites.
M. Kurenkov, A. Potapov, A. Savinykh, E. Yudin, E. Kruzhkov, P. Karpyshev, and D. Tsetserukou, “Nfomp: Neural field for optimal motion planner of differential drive robots with nonholonomic constraints,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 10 991–10 998, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
R. Ni and A. H. Qureshi, “Ntfields: Neural time fields for physics-informed robot motion planning,” in The Eleventh International Conference on Learning Representations , 2022
2022
Later among the works it cites.
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2019
Cited alongside, same era.
A. Gropp, L. Yariv, N. Haim, M. Atzmon, and Y. Lipman, “Implicit geometric regularization for learning shapes,” in International Conference on Machine Learning . PMLR, 2020, pp. 3789–3799
2020
Cited alongside, same era.
K. Saulnier, N. Atanasov, G. J. Pappas, and V. Kumar, “Information theoretic active exploration in signed distance fields,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 4080–4085
2020
Cited alongside, same era.
D. S. Chaplot, D. Gandhi, S. Gupta, A. Gupta, and R. Salakhutdinov, “Learning to explore using active neural slam,” in International Conference on Learning Representations , 2020
2020
Cited alongside, same era.
S. Stepputtis, J. Campbell, M. Phielipp, S. Lee, C. Baral, and H. Ben Amor, “Language-conditioned imitation learning for robot manipulation tasks,” Advances in Neural Information Processing Systems , vol. 33, pp. 13 139–13 150, 2020
2020
Cited alongside, same era.
X. Zhou, Z. Wang, H. Ye, C. Xu, and F. Gao, “Ego-planner: An esdf-free gradient-based local planner for quadrotors,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 478–485, 2020
2020
Cited alongside, same era.
G. Jocher, “Yolov5 by ultralytics,” 2020. [Online]. Available: https://github.com/ultralytics/yolov5
2020
Cited alongside, same era.
M. N. Finean, W. Merkt, and I. Havoutis, “Predicted composite signed-distance fields for real-time motion planning in dynamic environments,” in Proceedings of the International Conference on Automated Planning and Scheduling , vol. 31, 2021, pp. 616–624
2021
Cited alongside, same era.
J. Ye, D. Batra, A. Das, and E. Wijmans, “Auxiliary tasks and exploration enable objectgoal navigation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 16 117–16 126
2021
Cited alongside, same era.
Y. Pan, Y. Kompis, L. Bartolomei, R. Mascaro, C. Stachniss, and M. Chli, “Voxfield: Non-projective signed distance fields for online planning and 3d reconstruction,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 5331–5338
2022
Later among the works it cites.
M. Adamkiewicz, T. Chen, A. Caccavale, R. Gardner, P. Culbertson, J. Bohg, and M. Schwager, “Vision-only robot navigation in a neural radiance world,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4606–4613, 2022
2022
Later among the works it cites.
P. Liu, K. Zhang, D. Tateo, S. Jauhri, J. Peters, and G. Chalvatzaki, “Regularized deep signed distance fields for reactive motion generation,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 6673–6680
2022
Later among the works it cites.
C. Li, F. Xia, R. Martín-Martín, M. Lingelbach, S. Srivastava, B. Shen, K. E. Vainio, C. Gokmen, G. Dharan, T. Jain, et al. , “igibson 2.0: Object-centric simulation for robot learning of everyday household tasks,” in Conference on Robot Learning . PMLR, 2022, pp. 455–465
2022
Later among the works it cites.
S. Macenski, T. Foote, B. Gerkey, C. Lalancette, and W. Woodall, “Robot operating system 2: Design, architecture, and uses in the wild,” Science Robotics , vol. 7, no. 66, p. eabm6074, 2022. [Online]. Available: https://www.science.org/doi/abs/10.1126/scirobotics.abm6074
2022
Later among the works it cites.
F. Yang, C. Wang, C. Cadena, and M. Hutter, “iplanner: Imperative path planning,” in Robotics: Science and Systems , 2023
2023
Later among the works it cites.
T. Zhang, J. Wang, C. Xu, A. Gao, and F. Gao, “Continuous implicit sdf based any-shape robot trajectory optimization,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 282–289
2023
Later among the works it cites.
V. Vasilopoulos, S. Garg, J. Huh, B. Lee, and V. Isler, “Hio-sdf: Hierarchical incremental online signed distance fields,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 17 537–17 543
2024
Later among the works it cites.
Y. Cao and N. M. Nor, “An improved dynamic window approach algorithm for dynamic obstacle avoidance in mobile robot formation,” Decision Analytics Journal , vol. 11, p. 100471, 2024
2024
Later among the works it cites.