2 citations · 4 across the 9 of their papers we have counts for
10 papers · 1 filter
Are Doppler Velocity Measurements Useful for Spinning Radar Odometry?
Daniil Lisus, Keenan Burnett, David J. Yoon +3
Spinning, frequency-modulated continuous-wave (FMCW) radars with 360 degree coverage have been gaining popularity for autonomous-vehicle navigation. However, unlike `fixed' automot…
IMU as an Input vs. a Measurement of the State in Inertial-Aided State Estimation
Keenan Burnett, Angela P. Schoellig, Timothy D. Barfoot
Treating IMU measurements as inputs to a motion model and then preintegrating these measurements has almost become a de-facto standard in many robotics applications. However, this…
Continuous-Time Radar-Inertial and Lidar-Inertial Odometry using a Gaussian Process Motion Prior
Keenan Burnett, Angela P. Schoellig, Timothy D. Barfoot
In this work, we demonstrate continuous-time radar-inertial and lidar-inertial odometry using a Gaussian process motion prior. Using a sparse prior, we demonstrate improved computa…
Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights
Daniil Lisus, Johann Laconte, Keenan Burnett +2
This paper presents a novel deep-learning-based approach to improve localizing radar measurements against lidar maps. This radar-lidar localization leverages the benefits of both s…
Need for Speed: Fast Correspondence-Free Lidar-Inertial Odometry Using Doppler Velocity
David J. Yoon, Keenan Burnett, Johann Laconte +5
In this paper, we present a fast, lightweight odometry method that uses the Doppler velocity measurements from a Frequency-Modulated Continuous-Wave (FMCW) lidar without data assoc…
Radar Odometry Combining Probabilistic Estimation and Unsupervised Feature Learning
Keenan Burnett, David J. Yoon, Angela P. Schoellig +1
This paper presents a radar odometry method that combines probabilistic trajectory estimation and deep learned features without needing groundtruth pose information. The feature ne…