Jonathan D. Gammell

According to our database, Jonathan D. Gammell authored at least 31 papers between 2013 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Event-Based Stereo Visual Odometry With Native Temporal Resolution via Continuous-Time Gaussian Process Regression.

Jianeng Wang, Jonathan D. Gammell

IEEE Robotics Autom. Lett., October, 2023

Osprey: Multi-Session Autonomous Aerial Mapping with LiDAR-based SLAM and Next Best View Planning.

Rowan Border, Nived Chebrolu, Yifu Tao, Jonathan D. Gammell, Maurice F. Fallon

CoRR, 2023

Event-based Visual Odometry with Full Temporal Resolution via Continuous-time Gaussian Process Regression.

Jianeng Wang, Jonathan D. Gammell

CoRR, 2023

2022

Task and Motion Informed Trees (TMIT*): Almost-Surely Asymptotically Optimal Integrated Task and Motion Planning.

Wil Thomason, Marlin P. Strub, Jonathan D. Gammell

IEEE Robotics Autom. Lett., 2022

Adaptively Informed Trees (AIT*) and Effort Informed Trees (EIT*): Asymmetric bidirectional sampling-based path planning.

Marlin P. Strub, Jonathan D. Gammell

Int. J. Robotics Res., 2022

The Surface Edge Explorer (SEE): A measurement-direct approach to next best view planning.

Rowan Border, Jonathan D. Gammell

CoRR, 2022

Effort Informed Roadmaps (EIRM*): Efficient Asymptotically Optimal Multiquery Planning by Actively Reusing Validation Effort.

Valentin N. Hartmann, Marlin P. Strub, Marc Toussaint, Jonathan D. Gammell

Proceedings of the Robotics Research, 2022

2021

AIT* and EIT*: Asymmetric bidirectional sampling-based path planning.

Marlin P. Strub, Jonathan D. Gammell

CoRR, 2021

Multimotion Visual Odometry (MVO).

Kevin M. Judd, Jonathan D. Gammell

CoRR, 2021

Admissible heuristics for obstacle clearance optimization objectives.

Marlin P. Strub, Jonathan D. Gammell

CoRR, 2021

Asymptotically Optimal Sampling-Based Motion Planning Methods.

Jonathan D. Gammell, Marlin P. Strub

Annu. Rev. Control. Robotics Auton. Syst., 2021

2020

Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search.

Jonathan D. Gammell, Timothy D. Barfoot, Siddhartha S. Srinivasa

Int. J. Robotics Res., 2020

A Survey of Asymptotically Optimal Sampling-based Motion Planning Methods.

Jonathan D. Gammell, Marlin P. Strub

CoRR, 2020

Navigation on the Line: Traversability Analysis and Path Planning for Extreme-Terrain Rappelling Rovers.

Michael Paton, Marlin P. Strub, Travis Brown, Rebecca J. Greene, Jacob Lizewski, Vandan Patel, Jonathan D. Gammell, Issa A. D. Nesnas

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Occlusion-Robust MVO: Multimotion Estimation Through Occlusion Via Motion Closure.

Kevin M. Judd, Jonathan D. Gammell

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Proactive Estimation of Occlusions and Scene Coverage for Planning Next Best Views in an Unstructured Representation.

Rowan Border, Jonathan D. Gammell

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics.

Marlin P. Strub, Jonathan D. Gammell

Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques.

Marlin P. Strub, Jonathan D. Gammell

Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

2019

The Oxford Multimotion Dataset: Multiple SE(3) Motions With Ground Truth.

Kevin Michael Judd, Jonathan D. Gammell

IEEE Robotics Autom. Lett., 2019

2018

Informed Sampling for Asymptotically Optimal Path Planning.

Jonathan D. Gammell, Timothy D. Barfoot, Siddhartha S. Srinivasa

IEEE Trans. Robotics, 2018

Multimotion Visual Odometry (MVO): Simultaneous Estimation of Camera and Third-Party Motions.

Kevin M. Judd, Jonathan D. Gammell, Paul Newman

Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018

Surface Edge Explorer (see): Planning Next Best Views Directly from 3D Observations.

Rowan Border, Jonathan D. Gammell, Paul Newman

Proceedings of the 2018 IEEE International Conference on Robotics and Automation, 2018

2017

Informed Asymptotically Optimal Anytime Search.

Jonathan D. Gammell, Timothy D. Barfoot, Siddhartha S. Srinivasa

CoRR, 2017

2016

Regionally accelerated batch informed trees (RABIT*): A framework to integrate local information into optimal path planning.

Sanjiban Choudhury, Jonathan D. Gammell, Timothy D. Barfoot, Siddhartha S. Srinivasa, Sebastian A. Scherer

Proceedings of the 2016 IEEE International Conference on Robotics and Automation, 2016

2015

Batch Informed Trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs.

Jonathan D. Gammell, Siddhartha S. Srinivasa, Timothy D. Barfoot

Proceedings of the IEEE International Conference on Robotics and Automation, 2015

2014

BIT*: Batch Informed Trees for Optimal Sampling-based Planning via Dynamic Programming on Implicit Random Geometric Graphs.

Jonathan D. Gammell, Siddhartha S. Srinivasa, Timothy D. Barfoot

CoRR, 2014

Informed RRT*: Optimal Incremental Path Planning Focused through an Admissible Ellipsoidal Heuristic.

Jonathan D. Gammell, Siddhartha S. Srinivasa, Timothy D. Barfoot

CoRR, 2014

Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic.

Jonathan D. Gammell, Siddhartha S. Srinivasa, Timothy D. Barfoot

Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014

2013

Into Darkness: Visual Navigation Based on a Lidar-Intensity-Image Pipeline.

Timothy D. Barfoot, Colin McManus, Sean Anderson, Hang Dong, Erik Beerepoot, Chi Hay Tong, Paul Furgale, Jonathan D. Gammell, John Enright

Proceedings of the Robotics Research, 2013

Blinded by the Light: Exploiting the Deficiencies of a Laser Rangefinder for Rover Attitude Estimation.

Jonathan D. Gammell, Chi Hay Tong, Timothy D. Barfoot

Proceedings of the Tenth Conference on Computer and Robot Vision, 2013

A Proof-of-Concept, Rover-Based System for Autonomously Locating Methane Gas Sources on Mars.

Laszlo-Peter Berczi, Jonathan D. Gammell, Chi Hay Tong, Michael Daly, Timothy D. Barfoot

Proceedings of the Tenth Conference on Computer and Robot Vision, 2013