20 citations · 22 across the 14 of their papers we have counts for
8 papers · 1 filter
Embedded Bare-Metal Radar-Inertial Odometry
Nicolai Adil Øyen Aatif, Morten Nissov, Kostas Alexis
Compact extraplanetary rovers and micro aerial vehicles require robust state estimation frameworks designed to operate under strict computational constraints in unforgiving environ…
BEVIO: Efficient Bird's-Eye-View based Sparse-Update Visual-Inertial Odometry for Lunar Day-Night Navigation
Mohit Singh, Shehryar Khattak, Ashish Goel +3
Visual-Inertial Odometry (VIO) provides smooth, high-rate state estimates and has been widely used for robotic navigation in both terrestrial and planetary applications. However, i…
The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
Mihir Dharmadhikari, Nikhil Khedekar, Mihir Kulkarni +7
We introduce and open-source the Unified Autonomy Stack, a system-level solution that enables resilient autonomy across diverse aerial and ground robot morphologies. The architectu…
On the Characterization and Limits of 4D Radar for Aided Inertial Navigation
Morten Nissov, Kostas Alexis
Frequency Modulated Continuous Wave (FMCW) radar is a promising sensor for aided inertial navigation, due to its robustness in environments that challenge traditional alternatives,…
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…
DigiForest: Digital Analytics and Robotics for Sustainable Forestry
Marco Camurri, Enrico Tomelleri, Matías Mattamala +18
Covering one third of Earth's land surface, forests are vital to global biodiversity, climate regulation, and human well-being. In Europe, forests and woodlands reach approximately…
Tightly-Coupled Radar-Visual-Inertial Odometry
Morten Nissov, Mohit Singh, Kostas Alexis
Visual-Inertial Odometry (VIO) is a staple for reliable state estimation on constrained and lightweight platforms due to its versatility and demonstrated performance. However, pert…
Cross-Modal Reinforcement Learning for Navigation with Degraded Depth Measurements
Omkar Sawant, Luca Zanatta, Grzegorz Malczyk +1
This paper presents a cross-modal learning framework that exploits complementary information from depth and grayscale images for robust navigation. We introduce a Cross-Modal Wasse…