4 papers
Equivariant Filter for Radar-Inertial Odometry
Giulio Delama, Jan Michalczyk, Morten Nissov +4
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 se…
Reformulating AI-based Multi-Object Relative State Estimation for Aleatoric Uncertainty-based Outlier Rejection of Partial Measurements
Thomas Jantos, Giulio Delama, Stephan Weiss +1
Precise localization with respect to a set of objects of interest enables mobile robots to perform various tasks. With the rise of edge devices capable of deploying deep neural net…
Aleatoric Uncertainty from AI-based 6D Object Pose Predictors for Object-relative State Estimation
Thomas Jantos, Stephan Weiss, Jan Steinbrener
Deep Learning (DL) has become essential in various robotics applications due to excelling at processing raw sensory data to extract task specific information from semantic objects.…
Learning Point Correspondences In Radar 3D Point Clouds For Radar-Inertial Odometry
Jan Michalczyk, Stephan Weiss, Jan Steinbrener
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…