State estimation is a crucial component for the successful implementation of robotic systems,
relying on sensors such as cameras, LiDAR, and IMUs. However, in real-world scenarios …

There is a current increase in the development of" 4D" Doppler-capable radar and lidar
range sensors that produce 3D point clouds where all points also have information about the …

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 …

Radar offers the advantage of providing additional physical properties related to observed
objects. In this study, we design a physical-enhanced radar-inertial odometry system that …

State estimation is an essential component of autonomous systems, usually relying on
sensor fusion that integrates data from cameras, LiDARs and IMUs. Recently, radars have …

While visual and laser-based simultaneous localization and mapping (SLAM) techniques
have gained significant attention, radar SLAM remains a robust option for challenging …

Respecting the geometry of the underlying system and exploiting its symmetry have been
driving concepts in deriving modern geometric filters for inertial navigation systems (INSs) …

Search and rescue operations require mobile robots to navigate unstructured indoor and
outdoor environments. In particular, actively stabilized multirotor drones need precise …

Enabling robot autonomy in complex environments for mission critical application requires
robust state estimation. Particularly under conditions where the exteroceptive sensors, which …

In order to obtain odometry measurements, we use off the shelf automotive radar sensors.
To this end, we use Doppler measurements of static objects to estimate the yaw rate and the …