activity
20142023
most citedInformed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic

1.2k citations · 1.2k across the 7 of their papers we have counts for

collaborators

7 papers

cs.RO2023

Continuous-Time Range-Only Pose Estimation

Abhishek Goudar, Timothy D. Barfoot, Angela P. Schoellig

Range-only (RO) localization involves determining the position of a mobile robot by measuring the distance to specific anchors. RO localization is challenging since the measurement…

cs.RO2023

Towards Consistent Batch State Estimation Using a Time-Correlated Measurement Noise Model

David J. Yoon, Timothy D. Barfoot

In this paper, we present an algorithm for learning time-correlated measurement covariances for application in batch state estimation. We parameterize the inverse measurement covar…

cs.RO2022

Picking Up Speed: Continuous-Time Lidar-Only Odometry using Doppler Velocity Measurements

Yuchen Wu, David J. Yoon, Keenan Burnett +4

Frequency-Modulated Continuous-Wave (FMCW) lidar is a recently emerging technology that additionally enables per-return instantaneous relative radial velocity measurements via the…

cs.RO2022

The Foreseeable Future: Self-Supervised Learning to Predict Dynamic Scenes for Indoor Navigation

Hugues Thomas, Jian Zhang, Timothy D. Barfoot

We present a method for generating, predicting, and using Spatiotemporal Occupancy Grid Maps (SOGM), which embed future semantic information of real dynamic scenes. We present an a…

cs.RO2014

Batch Nonlinear Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression

Sean Anderson, Timothy D. Barfoot, Chi Hay Tong +1

In this paper, we revisit batch state estimation through the lens of Gaussian process (GP) regression. We consider continuous-discrete estimation problems wherein a trajectory is v…

cs.RO20141.2k cited

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

Rapidly-exploring random trees (RRTs) are popular in motion planning because they find solutions efficiently to single-query problems. Optimal RRTs (RRT*s) extend RRTs to the probl…