Ingmar Posner
Title
Cited by
Cited by
Year
Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks
M Engelcke, D Rao, DZ Wang, CH Tong, I Posner
2017 IEEE International Conference on Robotics and Automation (ICRA), 1355-1361, 2017
3662017
Voting for Voting in Online Point Cloud Object Detection
DZ Wang, I Posner
Robotics: Science and Systems, 2015
2652015
Maximum Entropy Deep Inverse Reinforcement Learning
M Wulfmeier, P Ondruska, I Posner
CoRR 2015, 2015
2032015
Navigating, recognizing and describing urban spaces with vision and lasers
P Newman, G Sibley, M Smith, M Cummins, A Harrison, C Mei, I Posner, ...
The International Journal of Robotics Research 28 (11-12), 1406-1433, 2009
1732009
Deep tracking: Seeing beyond seeing using recurrent neural networks
P Ondruska, I Posner
Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016
1502016
Toward automated driving in cities using close-to-market sensors: An overview of the v-charge project
P Furgale, U Schwesinger, M Rufli, W Derendarz, H Grimmett, ...
2013 IEEE Intelligent Vehicles Symposium (IV), 809-816, 2013
1332013
What could move? finding cars, pedestrians and bicyclists in 3d laser data
DZ Wang, I Posner, P Newman
2012 IEEE International Conference on Robotics and Automation, 4038-4044, 2012
1142012
Sequential attend, infer, repeat: Generative modelling of moving objects
AR Kosiorek, H Kim, I Posner, YW Teh
arXiv preprint arXiv:1806.01794, 2018
1082018
Find your own way: Weakly-supervised segmentation of path proposals for urban autonomy
D Barnes, W Maddern, I Posner
2017 IEEE International Conference on Robotics and Automation (ICRA), 203-210, 2017
842017
A generative framework for fast urban labeling using spatial and temporal context
I Posner, M Cummins, P Newman
Autonomous Robots 26 (2), 153-170, 2009
822009
Model-free detection and tracking of dynamic objects with 2D lidar
DZ Wang, I Posner, P Newman
The International Journal of Robotics Research 34 (7), 1039-1063, 2015
802015
Large-scale cost function learning for path planning using deep inverse reinforcement learning
M Wulfmeier, D Rao, DZ Wang, P Ondruska, I Posner
The International Journal of Robotics Research 36 (10), 1073-1087, 2017
782017
Deep tracking in the wild: End-to-end tracking using recurrent neural networks
J Dequaire, P Ondr˙ška, D Rao, D Wang, I Posner
The International Journal of Robotics Research 37 (4-5), 492-512, 2018
742018
Deep tracking: Seeing beyond seeing using recurrent neural networks
P Ondr˙ška, I Posner
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligenceá…, 2016
732016
The oxford radar robotcar dataset: A radar extension to the oxford robotcar dataset
D Barnes, M Gadd, P Murcutt, P Newman, I Posner
2020 IEEE International Conference on Robotics and Automation (ICRA), 6433-6438, 2020
722020
Watch this: Scalable cost-function learning for path planning in urban environments
M Wulfmeier, DZ Wang, I Posner
2016 IEEE/RSJ International Conference on Intelligent Robots and Systemsá…, 2016
692016
On the limitations of representing functions on sets
E Wagstaff, F Fuchs, M Engelcke, I Posner, MA Osborne
International Conference on Machine Learning, 6487-6494, 2019
612019
Genesis: Generative scene inference and sampling with object-centric latent representations
M Engelcke, AR Kosiorek, OP Jones, I Posner
arXiv preprint arXiv:1907.13052, 2019
602019
End-to-end tracking and semantic segmentation using recurrent neural networks
P Ondruska, J Dequaire, DZ Wang, I Posner
arXiv preprint arXiv:1604.05091, 2016
582016
Deep inverse reinforcement learning
M Wulfmeier, P Ondruska, I Posner
CoRR, abs/1507.04888, 2015
542015
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