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David Juny Yoon

2 accepted papers

2020

Variational Inference With Parameter Learning Applied to Vehicle Trajectory Estimation

RA-L 2020

We present parameter learning in a Gaussian variational inference setting using only noisy measurements (i.e., no groundtruth). This is demonstrated in the context of vehicle trajectory estimation, although the method we propose is general. The letter extends the Exactly Sparse Gaussian Variational

Cited by 24SourceScholar
2019

A White-Noise-on-Jerk Motion Prior for Continuous-Time Trajectory Estimation on SE(3)

RA-L 2019

Simultaneous trajectory estimation and mapping (STEAM) offers an efficient approach to continuous-time trajectory estimation, by representing the trajectory as a Gaussian process (GP). Previous formulations of the STEAM framework use a GP prior that assumes white-noise-on-acceleration, with the prio

Cited by 48SourceScholar