Jonathan Gammell
Jonathan Gammell
Departmental Lecturer in Robotics, University of Oxford
Verified email at - Homepage
Cited by
Cited by
Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic
JD Gammell, SS Srinivasa, TD Barfoot
Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International …, 2014
Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs
JD Gammell, SS Srinivasa, TD Barfoot
Robotics and Automation (ICRA 2015), 2015 IEEE International Conference on, 2015
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
Robotics and Automation (ICRA), 2016 IEEE International Conference on, 4207-4214, 2016
Informed sampling for asymptotically optimal path planning
JD Gammell, TD Barfoot, SS Srinivasa
IEEE Transactions on Robotics 34 (4), 966-984, 2018
Into darkness: Visual navigation based on a lidar-intensity-image pipeline
TD Barfoot, C McManus, S Anderson, H Dong, E Beerepoot, CH Tong, ...
Robotics research, 487-504, 2016
A Mission Control Architecture for robotic lunar sample return as field tested in an analogue deployment to the sudbury impact structure
JE Moores, R Francis, M Mader, GR Osinski, T Barfoot, N Barry, G Basic, ...
Advances in space research 50 (12), 1666-1686, 2012
Rover odometry aided by a star tracker
JD Gammell, CH Tong, P Berczi, S Anderson, TD Barfoot, J Enright
2013 IEEE Aerospace Conference, 1-10, 2013
Multimotion visual odometry (MVO): Simultaneous estimation of camera and third-party motions
KM Judd, JD Gammell, P Newman
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2018
Informed anytime search for continuous planning problems
JD Gammell
University of Toronto, 2017
Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search
JD Gammell, TD Barfoot, SS Srinivasa
The International Journal of Robotics Research 39 (5), 543-567, 2020
Manufacturable MEMS MiniSEMs
R Saini, Z Jandric, J Gammell, SAM Mentink, D Tuggle
Microelectronic engineering 83 (4-9), 1376-1381, 2006
Surface edge explorer (SEE): Planning next best views directly from 3D observations
R Border, JD Gammell, P Newman
2018 IEEE International Conference on Robotics and Automation (ICRA), 1-8, 2018
The probability density function of a transformation-based hyperellipsoid sampling technique
JD Gammell, TD Barfoot
arXiv preprint arXiv:1404.1347, 2014
The Oxford multimotion dataset: Multiple SE(3) motions with ground truth
KM Judd, JD Gammell
IEEE Robotics and Automation Letters 4 (2), 800-807, 2019
Planetary Surface Exploration Using a Network of Reusable Paths
B Stenning, GR Osinski, T Barfoot, G Basic, M Beauchamp, M Daly, ...
Lunar and Planetary Institute Science Conference Abstracts 43, 2360, 2012
A series of robotic and human analogue missions in support of lunar sample return
CL Marion, GR Osinski, S Abou-Aly, I Antonenko, T Barfoot, N Barry, ...
LPI, 2333, 2012
3D surface mapping using a semiautonomous rover: A planetary analog field experiment
RS Merali, C Tong, J Gammell, J Bakambu, E Dupuis, TD Barfoot
Proc. of the 2012 Int. Symposium on Artificial Intelligence, Robotics and …, 2012
Advanced BIT*(ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques
MP Strub, JD Gammell
arXiv preprint arXiv:2002.06589, 2020
BIT*: Sampling-based optimal planning via batch informed trees
JD Gammell, SS Srinivasa, TD Barfoot
The Information-based Grasp and Manipulation Planning Workshop, Robotics …, 2014
A proof-of-concept, rover-based system for autonomously locating methane gas sources on mars
LP Berczi, JD Gammell, CH Tong, M Daly, TD Barfoot
2013 International Conference on Computer and Robot Vision, 29-36, 2013
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