Jing Dong

Affiliations:
  • Georgia Institute of Technology, Institute for Robotics and Intelligent Machines, USA


According to our database1, Jing Dong authored at least 13 papers between 2015 and 2023.

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Timeline

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Bibliography

2023
Decentralization and Acceleration Enables Large-Scale Bundle Adjustment.
Proceedings of the Robotics: Science and Systems XIX, Daegu, 2023

2022
iSDF: Real-Time Neural Signed Distance Fields for Robot Perception.
Proceedings of the Robotics: Science and Systems XVIII, New York City, NY, USA, June 27, 2022

Theseus: A Library for Differentiable Nonlinear Optimization.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2019
Extending 3D Reconstruction to Temporal and Multi-model Sensor Data for Precision Agriculture.
PhD thesis, 2019

STEAP: simultaneous trajectory estimation and planning.
Auton. Robots, 2019

2018
Continuous-time Gaussian process motion planning via probabilistic inference.
Int. J. Robotics Res., 2018

Sparse Gaussian Processes on Matrix Lie Groups: A Unified Framework for Optimizing Continuous-Time Trajectories.
Proceedings of the 2018 IEEE International Conference on Robotics and Automation, 2018

Learning to Align Images Using Weak Geometric Supervision.
Proceedings of the 2018 International Conference on 3D Vision, 2018

2017
Sparse Gaussian Processes for Continuous-Time Trajectory Estimation on Matrix Lie Groups.
CoRR, 2017

Simultaneous Trajectory Estimation and Planning via Probabilistic Inference.
Proceedings of the Robotics: Science and Systems XIII, 2017

4D crop monitoring: Spatio-temporal reconstruction for agriculture.
Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

2016
Motion Planning as Probabilistic Inference using Gaussian Processes and Factor Graphs.
Proceedings of the Robotics: Science and Systems XII, University of Michigan, Ann Arbor, Michigan, USA, June 18, 2016

2015
Distributed real-time cooperative localization and mapping using an uncertainty-aware expectation maximization approach.
Proceedings of the IEEE International Conference on Robotics and Automation, 2015


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