139 citations · 421 across the 12 of their papers we have counts for
12 papers
Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data
Andrej Janda, Brandon Wagstaff, Edwin G. Ng +1
Reducing the quantity of annotations required for supervised training is vital when labels are scarce and costly. This reduction is particularly important for semantic segmentation…
Self-Supervised Pre-training of 3D Point Cloud Networks with Image Data
Andrej Janda, Brandon Wagstaff, Edwin G. Ng +1
Reducing the quantity of annotations required for supervised training is vital when labels are scarce and costly. This reduction is especially important for semantic segmentation t…
A Self-Supervised, Differentiable Kalman Filter for Uncertainty-Aware Visual-Inertial Odometry
Brandon Wagstaff, Emmett Wise, Jonathan Kelly
Visual-inertial odometry (VIO) systems traditionally rely on filtering or optimization-based techniques for egomotion estimation. While these methods are accurate under nominal con…
On the Coupling of Depth and Egomotion Networks for Self-Supervised Structure from Motion
Brandon Wagstaff, Valentin Peretroukhin, Jonathan Kelly
Structure from motion (SfM) has recently been formulated as a self-supervised learning problem, where neural network models of depth and egomotion are learned jointly through view…
Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching
Justin Tomasi, Brandon Wagstaff, Steven L. Waslander +1
Successful visual navigation depends upon capturing images that contain sufficient useful information. In this letter, we explore a data-driven approach to account for environmenta…
Self-Supervised Scale Recovery for Monocular Depth and Egomotion Estimation
Brandon Wagstaff, Jonathan Kelly
The self-supervised loss formulation for jointly training depth and egomotion neural networks with monocular images is well studied and has demonstrated state-of-the-art accuracy.…
Heteroscedastic Uncertainty for Robust Generative Latent Dynamics
Oliver Limoyo, Bryan Chan, Filip Marić +3
Learning or identifying dynamics from a sequence of high-dimensional observations is a difficult challenge in many domains, including reinforcement learning and control. The proble…
Self-Supervised Deep Pose Corrections for Robust Visual Odometry
Brandon Wagstaff, Valentin Peretroukhin, Jonathan Kelly
We present a self-supervised deep pose correction (DPC) network that applies pose corrections to a visual odometry estimator to improve its accuracy. Instead of regressing inter-fr…
Robust Data-Driven Zero-Velocity Detection for Foot-Mounted Inertial Navigation
Brandon Wagstaff, Valentin Peretroukhin, Jonathan Kelly
We present two novel techniques for detecting zero-velocity events to improve foot-mounted inertial navigation. Our first technique augments a classical zero-velocity detector by i…
Probabilistic Regression of Rotations using Quaternion Averaging and a Deep Multi-Headed Network
Valentin Peretroukhin, Brandon Wagstaff, Matthew Giamou +1
Accurate estimates of rotation are crucial to vision-based motion estimation in augmented reality and robotics. In this work, we present a method to extract probabilistic estimates…
LSTM-Based Zero-Velocity Detection for Robust Inertial Navigation
Brandon Wagstaff, Jonathan Kelly
We present a method to improve the accuracy of a zero-velocity-aided inertial navigation system (INS) by replacing the standard zero-velocity detector with a long short-term memory…
Improving Foot-Mounted Inertial Navigation Through Real-Time Motion Classification
Brandon Wagstaff, Valentin Peretroukhin, Jonathan Kelly
We present a method to improve the accuracy of a foot-mounted, zero-velocity-aided inertial navigation system (INS) by varying estimator parameters based on a real-time classificat…