| Voxnet: A 3d convolutional neural network for real-time object recognition D Maturana, S Scherer 2015 IEEE/RSJ international conference on intelligent robots and systems …, 2015 | 4097 | 2015 |
| Cubeslam: Monocular 3-d object slam S Yang, S Scherer IEEE Transactions on Robotics 35 (4), 925-938, 2019 | 472 | 2019 |
| Tartanair: A dataset to push the limits of visual slam W Wang, D Zhu, X Wang, Y Hu, Y Qiu, C Wang, Y Hu, A Kapoor, ... 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 297 | 2020 |
| 3d convolutional neural networks for landing zone detection from lidar D Maturana, S Scherer 2015 IEEE international conference on robotics and automation (ICRA), 3471-3478, 2015 | 293 | 2015 |
| Flying fast and low among obstacles: Methodology and experiments S Scherer, S Singh, L Chamberlain, M Elgersma The International Journal of Robotics Research 27 (5), 549-574, 2008 | 225 | 2008 |
| Improving stochastic policy gradients in continuous control with deep reinforcement learning using the beta distribution PW Chou, D Maturana, S Scherer International conference on machine learning, 834-843, 2017 | 210 | 2017 |
| First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles T Mori, S Scherer 2013 IEEE international conference on robotics and automation, 1750-1757, 2013 | 209 | 2013 |
| Real-time semantic mapping for autonomous off-road navigation D Maturana, PW Chou, M Uenoyama, S Scherer Field and Service Robotics: Results of the 11th International Conference …, 2018 | 202 | 2018 |
| Rgb-d slam in dynamic environments using point correlations W Dai, Y Zhang, P Li, Z Fang, S Scherer IEEE transactions on pattern analysis and machine intelligence 44 (1), 373-389, 2020 | 181 | 2020 |
| River mapping from a flying robot: state estimation, river detection, and obstacle mapping S Scherer, J Rehder, S Achar, H Cover, A Chambers, S Nuske, S Singh Autonomous Robots 33, 189-214, 2012 | 176 | 2012 |
| Pop-up slam: Semantic monocular plane slam for low-texture environments S Yang, Y Song, M Kaess, S Scherer 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2016 | 172 | 2016 |
| Super odometry: Imu-centric lidar-visual-inertial estimator for challenging environments S Zhao, H Zhang, P Wang, L Nogueira, S Scherer 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 151 | 2021 |
| An efficient global energy optimization approach for robust 3D plane segmentation of point clouds Z Dong, B Yang, P Hu, S Scherer ISPRS Journal of Photogrammetry and Remote Sensing 137, 112-133, 2018 | 143 | 2018 |
| Tartan racing: A multi-modal approach to the darpa urban challenge C Urmson, JA Bagnell, C Baker, M Hebert, A Kelly, R Rajkumar, ... Carnegie Mellon University, 2007 | 140 | 2007 |
| Flying fast and low among obstacles S Scherer, S Singh, L Chamberlain, S Saripalli Proceedings 2007 IEEE International Conference on Robotics and Automation …, 2007 | 133 | 2007 |
| Autonomous landing at unprepared sites by a full-scale helicopter S Scherer, L Chamberlain, S Singh Robotics and Autonomous Systems 60 (12), 1545-1562, 2012 | 130 | 2012 |
| Hierarchical registration of unordered TLS point clouds based on binary shape context descriptor Z Dong, B Yang, F Liang, R Huang, S Scherer ISPRS Journal of Photogrammetry and Remote Sensing 144, 61-79, 2018 | 127 | 2018 |
| Regionally accelerated batch informed trees (rabit*): A framework to integrate local information into optimal path planning S Choudhury, JD Gammell, TD Barfoot, SS Srinivasa, S Scherer 2016 IEEE International Conference on Robotics and Automation (ICRA), 4207-4214, 2016 | 125 | 2016 |
| Tartanvo: A generalizable learning-based vo W Wang, Y Hu, S Scherer Conference on Robot Learning, 1761-1772, 2021 | 123 | 2021 |
| Autonomous exploration for infrastructure modeling with a micro aerial vehicle L Yoder, S Scherer Field and Service Robotics: Results of the 10th International Conference …, 2016 | 114 | 2016 |
Daniel MaturanaGather AI確認したメール アドレス: gather.ai
