4 papers
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…
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…