Search Results for author: Timothy D. Barfoot

Found 12 papers, 2 papers with code

Self-Supervised Feature Learning for Long-Term Metric Visual Localization

no code implementations30 Nov 2022 Yuxuan Chen, Timothy D. Barfoot

Visual localization is the task of estimating camera pose in a known scene, which is an essential problem in robotics and computer vision.

Visual Localization

Unsupervised Learning of Lidar Features for Use in a Probabilistic Trajectory Estimator

no code implementations22 Feb 2021 David J. Yoon, Haowei Zhang, Mona Gridseth, Hugues Thomas, Timothy D. Barfoot

Though the framework is general to any form of parameter learning and sensor modality, we demonstrate application to feature and uncertainty learning with a deep network for 3D lidar odometry.

Variational Inference Robotics

UAV Localization Using Autoencoded Satellite Images

no code implementations10 Feb 2021 Mollie Bianchi, Timothy D. Barfoot

This trained autoencoder is used to compress a real UAV image, which is then compared to the precollected, nearby, autoencoded GE images using an inner-product kernel.

Relatively Lazy: Indoor-Outdoor Navigation Using Vision and GNSS

no code implementations13 Jan 2021 Benjamin Congram, Timothy D. Barfoot

Adding additional absolute sensors such as Global Navigation Satellite Systems (GNSS) has the potential to expand the domain of Visual Teach and Repeat to environments where the ability to visually localize is not guaranteed.

Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation

2 code implementations10 Dec 2020 Hugues Thomas, Ben Agro, Mona Gridseth, Jian Zhang, Timothy D. Barfoot

We provide insights into our network predictions and show that our approach can also improve the performances of common localization techniques.

Navigate Point Cloud Segmentation +3

Variational Inference as Iterative Projection in a Bayesian Hilbert Space with Application to Robotic State Estimation

no code implementations14 May 2020 Timothy D. Barfoot, Gabriele M. T. D'Eleuterio

The goal is to find an approximate probability density function (PDF) from a chosen family that is in some sense 'closest' to the full Bayesian posterior.

Bayesian Inference Variational Inference

Variational Inference with Parameter Learning Applied to Vehicle Trajectory Estimation

no code implementations21 Mar 2020 Jeremy N. Wong, David J. Yoon, Angela P. Schoellig, Timothy D. Barfoot

Our contribution is to additionally learn parameters of our system models (which may be difficult to choose in practice) within the ESGVI framework.

Variational Inference

Multivariate Gaussian Variational Inference by Natural Gradient Descent

no code implementations27 Jan 2020 Timothy D. Barfoot

This short note reviews so-called Natural Gradient Descent (NGD) for multivariate Gaussians.

Variational Inference

Mapless Online Detection of Dynamic Objects in 3D Lidar

no code implementations19 Sep 2018 David J. Yoon, Tim Y. Tang, Timothy D. Barfoot

This paper presents a model-free, setting-independent method for online detection of dynamic objects in 3D lidar data.


Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs

no code implementations22 May 2014 Jonathan D. Gammell, Siddhartha S. Srinivasa, Timothy D. Barfoot

In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on unifying graph- and sampling-based planning techniques.


Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic

1 code implementation8 Apr 2014 Jonathan D. Gammell, Siddhartha S. Srinivasa, Timothy D. Barfoot

We present the algorithm as a simple modification to RRT* that could be further extended by more advanced path-planning algorithms.


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