20 citations · 22 across the 14 of their papers we have counts for
15 papers
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…
Neural NMPC through Signed Distance Field Encoding for Collision Avoidance
Martin Jacquet, Marvin Harms, Kostas Alexis
This paper introduces a neural Nonlinear Model Predictive Control (NMPC) framework for mapless, collision-free navigation in unknown environments with Aerial Robots, using onboard…
IMU-Preintegrated Radar Factors for Asynchronous Radar-LiDAR-Inertial SLAM
Johan Hatleskog, Morten Nissov, Kostas Alexis
Fixed-lag Radar-LiDAR-Inertial smoothers conventionally create one factor graph node per measurement to compensate for the lack of time synchronization between radar and LiDAR. For…
Embedded Safe Reactive Navigation for Multirotors Systems using Control Barrier Functions
Nazar Misyats, Marvin Harms, Morten Nissov +2
Aiming to promote the wide adoption of safety filters for autonomous aerial robots, this paper presents a safe control architecture designed for seamless integration into widely us…
Safe Quadrotor Navigation using Composite Control Barrier Functions
Marvin Harms, Martin Jacquet, Kostas Alexis
This paper introduces a safety filter to ensure collision avoidance for multirotor aerial robots. The proposed formalism leverages a single Composite Control Barrier Function from…
Probabilistic Degeneracy Detection for Point-to-Plane Error Minimization
Johan Hatleskog, Kostas Alexis
Degeneracies arising from uninformative geometry are known to deteriorate LiDAR-based localization and mapping. This work introduces a new probabilistic method to detect and mitiga…
Neural Control Barrier Functions for Safe Navigation
Marvin Harms, Mihir Kulkarni, Nikhil Khedekar +2
Autonomous robot navigation can be particularly demanding, especially when the surrounding environment is not known and safety of the robot is crucial. This work relates to the syn…
N-MPC for Deep Neural Network-Based Collision Avoidance exploiting Depth Images
Martin Jacquet, Kostas Alexis
This paper introduces a Nonlinear Model Predictive Control (N-MPC) framework exploiting a Deep Neural Network for processing onboard-captured depth images for collision avoidance i…