5 accepted papers
Radar-Inertial Odometry (RIO) based on the Extended Kalman Filter (EKF) relies on accurate extrinsic calibration between the radar and the Inertial Measurement Unit (IMU) and is sensitive to disturbances, as large linearization errors can degrade performance or even cause divergence. To address thes
Using 3D point clouds in odometry estimation in robotics often requires finding a set of correspondences between points in subsequent scans. While there are established methods for point clouds of sufficient quality, state-of-the-art still struggles when this quality drops. Thus, this paper presents
In this paper, we present a Radar-Inertial Odometry (RIO) method based on the nonlinear optimization of factor graphs in a sliding window fashion. Our method makes use of a light-weight, low-power, inexpensive and commonly available hardware enabling easy deployment on small Unmanned Aerial Vehicles…
In this paper, we present a Radar-Inertial Odometry (RIO) approach that utilizes performance improving modules, enhanced for the sparse and noisy radar signals, from the vision community in order to estimate the full 6DoF pose and 3D velocity of a robot in an unprepared environment. Our method lever…
Multicopter Unmanned Aerial Vehicles (UAV) are small and agile robots with the potential to become prominent in performing autonomous tasks in various Global Navigation Satellite System (GNSS)-denied environments. These environments can potentially be rendered even more challenging due to external f…