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Exploration of unknown environments is a fundamental problem in robotics and an essential component in numerous applications of autonomous systems.
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1997
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2011
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2012
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2013
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2014
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2015
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2015
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2016
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2016
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2016
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2016
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2017
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2017
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2017
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2017
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S. Song, F. Yu, A. Zeng, A. X. Chang, M. Savva, and T. A. Funkhouser, “Semantic scene completion from a single depth image,”
2017
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H. Oleynikova, Z. Taylor, M. Fehr, R. Y. Siegwart, and J. I. Nieto, “Voxblox: Incremental 3d euclidean signed distance fields for on-board mav planning,”
2017
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S. Shah, D. Dey, C. Lovett, and A. Kapoor, “Airsim: High-fidelity visual and physical simulation for autonomous vehicles,” in
2017
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J. McCormac, A. Handa, A. Davison, and S. Leutenegger, “SemanticFusion: Dense 3D semantic mapping with convolutional neural networks,” in
2017
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N. Sünderhauf, O. Brock, W. Scheirer, R. Hadsell, D. Fox, J. Leitner, B. Upcroft, P. Abbeel, W. Burgard, M. Milford,
2018
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M. Strecke and J. Stueckler, “EM-Fusion: Dynamic Object-Level SLAM With Probabilistic Data Association,” in
2019
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E. Vespa, N. Funk, P. H. Kelly, and S. Leutenegger, “Adaptive-resolution octree-based volumetric slam,” in
2019
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M. Popović, T. Vidal-Calleja, G. Hitz, J. J. Chung, I. Sa, R. Siegwart, and J. Nieto, “An informative path planning framework for uav-based terrain monitoring,”
2020
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L. Schmid, M. Pantic, R. Khanna, L. Ott, R. Siegwart, and J. Nieto, “An efficient sampling-based method for online informative path planning in unknown environments,”
2020
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R. Reinhart, T. Dang, E. Hand, C. Papachristos, and K. Alexis, “Learning-based path planning for autonomous exploration of subterranean environments,” in
2020
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P. Henderson, R. Islam, P. Bachman, J. Pineau, D. Precup, and D. Meger, “Deep reinforcement learning that matters,” in
2018
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D. Zhu, T. Li, D. Ho, C. Wang, and M. Q.-H. Meng, “Deep reinforcement learning supervised autonomous exploration in office environments,” in
2018
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B. Hepp, D. Dey, S. N. Sinha, A. Kapoor, N. Joshi, and O. Hilliges, “Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility,” in
2018
Cited alongside, same era.
T. Dang, C. Papachristos, and K. Alexis, “Autonomous exploration and simultaneous object search using aerial robots,” in
2018
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C. Witting, M. Fehr, R. Bähnemann, H. Oleynikova, and R. Siegwart, “History-aware autonomous exploration in confined environments using mavs,” in
2018
Cited alongside, same era.
J. Mccormac, R. Clark, M. Bloesch, A. Davison, and S. Leutenegger, “Fusion++: Volumetric Object-Level SLAM,” in
2018
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T. Dang, F. Mascarich, S. Khattak, C. Papachristos, and K. Alexis, “Graph-based path planning for autonomous robotic exploration in subterranean environments,” in
2019
Cited alongside, same era.
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T. Dang, M. Tranzatto, S. Khattak, F. Mascarich, K. Alexis, and M. Hutter, “Graph-based subterranean exploration path planning using aerial and legged robots,”
2020
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A. Dai, S. Papatheodorou, N. Funk, D. Tzoumanikas, and S. Leutenegger, “Fast frontier-based information-driven autonomous exploration with an mav,”
2020
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2020
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S.-C. Wu, K. Tateno, N. Navab, and F. Tombari, “SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion,”
2020
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L. Schmid, V. Reijgwart, L. Ott, J. Nieto, R. Siegwart, and C. Cadena, “A unified approach for autonomous volumetric exploration of large scale environments under severe odometry drift,”
2021
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V. M. Respall, D. Devitt, R. Fedorenko, and A. Klimchik, “Fast sampling-based next-best-view exploration algorithm for a mav,” in
2021
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Y. Kompis, L. Bartolomei, R. Mascaro, L. Teixeira, and M. Chli, “Informed sampling exploration path planner for 3d reconstruction of large scenes,”
2021
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B. Zhou, Y. Zhang, X. Chen, and S. Shen, “Fuel: Fast uav exploration using incremental frontier structure and hierarchical planning,”
2021
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M. Popović, F. Thomas, S. Papatheodorou, N. Funk, T. Vidal-Calleja, and S. Leutenegger, “Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation,”
2021
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S. Lionar, L. Schmid, C. Cadena, R. Siegwart, and A. Cramariuc, “NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric Mapping,” in
2021
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J. Huang, S.-S. Huang, H. Song, and S.-M. Hu, “Di-fusion: Online implicit 3d reconstruction with deep priors,” in
2021
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L. Schmid, C. Ni, Y. Zhong, R. Siegwart, and O. Andersson, “Fast and compute-efficient sampling-based local exploration planning via distribution learning,”
2022
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L. Roldao, R. De Charette, and A. Verroust-Blondet, “3d semantic scene completion: a survey,”
2022
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L. Schmid, J. Delmerico, J. L. Schönberger, J. Nieto, M. Pollefeys, R. Siegwart, and C. Cadena, “Panoptic Multi-TSDFs: a Flexible Representation for Online Multi-resolution Volumetric Mapping and Long-term Dynamic Scene Consistency,” in
2022
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R. Zurbrügg, H. Blum, C. Cadena, R. Siegwart, and L. Schmid, “Embodied Active Domain Adaptation for Semantic Segmentation via Informative Path Planning,”
2022
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