arXiv subjects

Martin Saska

Publications and source records attributed to Martin Saska.

At least 37 records · Page 2Linked to original sources

arXiv2026

Relative State Estimation using Event-Based Propeller Sensing

Autonomous swarms of multi-Unmanned Aerial Vehicle (UAV) system requires an accurate and fast relative state estimation. Although monocular frame-based camera methods perform well in ideal conditions, they are slow, suffer scale ambiguity, and often struggle in visually challenging conditions. The advent of event cameras addresses these challenging tasks by providing low latency, high dynamic range, and microsecond-level temporal resolution. This paper proposes a framework for relative state estimation for quadrotors using event-based propeller sensing. The propellers in the event stream are tracked by detection to extract the region-of-interests. The event streams in these regions are processed in temporal chunks to estimate per-propeller frequencies. These frequency measurements drive a kinematic state estimation module as a thrust input, while camera-derived position measurements provide the update step. Additionally, we use geometric primitives derived from event streams to estimate the orientation of the quadrotor by fitting an ellipse over a propeller and backprojecting it to recover body-frame tilt-axis. The existing event-based approaches for quadrotor state estimation use the propeller frequency in simulated flight sequences. Our approach estimates the propeller frequency under 3% error on a test dataset of five real-world outdoor flight sequences, providing a method for decentralized relative localization for multi-robot systems using event camera.

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arXiv2026

Perception-Aware Communication-Free Multi-UAV Coordination in the Wild

We present a communication-free method for safe multi-robot coordination in complex environments such as forests with dense canopy cover, where GNSS is unavailable. Our approach relies on an onboard anisotropic 3D LiDAR sensor used for SLAM as well as for detecting obstacles and neighboring robots. We develop a novel perception-aware 3D navigation framework that enables robots to safely and effectively progress toward a goal region despite limited sensor field-of-view. The approach is evaluated through extensive simulations across diverse scenarios and validated in real-world field experiments, demonstrating its scalability, robustness, and reliability.

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arXiv2026

Distributed UAV Formation Control Robust to Relative Pose Measurement Noise

A technique that allows a Formation-Enforcing Control (FEC) derived from graph rigidity theory to interface with a realistic relative localization system onboard lightweight Unmanned Aerial Vehicles (UAVs) is proposed in this paper. The proposed methodology enables reliable real-world deployment of UAVs in tight formations using relative localization systems burdened by non-negligible sensory noise. Such noise otherwise causes undesirable oscillations and drifts in sensor-based formations, and this effect is not sufficiently addressed in existing FEC algorithms. The proposed solution is based on decomposition of the gradient descent-based FEC command into interpretable elements, and then modifying these individually based on the estimated distribution of sensory noise, such that the resulting action limits the probability of overshooting the desired formation. The behavior of the system was analyzed and the practicality of the proposed solution was compared to pure gradient-descent in real-world experiments where it presented significantly better performance in terms of oscillations, deviation from the desired state

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arXiv2026

Fusion of Visual-Inertial Odometry with LiDAR Relative Localization for Cooperative Guidance of a Micro-Scale Aerial Vehicle

A novel relative localization approach for guidance of a micro-scale Unmanned Aerial Vehicle (UAV) by a well-equipped aerial robot fusing Visual-Inertial Odometry (VIO) with Light Detection and Ranging (LiDAR) is proposed in this paper. LiDAR-based localization is accurate and robust to challenging environmental conditions, but 3D LiDARs are relatively heavy and require large UAV platforms, in contrast to lightweight cameras. However, visual-based self-localization methods exhibit lower accuracy and can suffer from significant drift with respect to the global reference frame. To benefit from both sensory modalities, we focus on cooperative navigation in a heterogeneous team of a primary LiDAR-equipped UAV and a secondary micro-scale camera-equipped UAV. We propose a novel cooperative approach combining LiDAR relative localization data with VIO output on board the primary UAV to obtain an accurate pose of the secondary UAV. The pose estimate is used to precisely and reliably guide the secondary UAV along trajectories defined in the primary UAV reference frame. The experimental evaluation has shown the superior accuracy of our method to the raw VIO output, reaching the average 3D Absolute Trajectory Error (ATE) of 0.28 m, and demonstrated its capability to guide the secondary UAV along desired trajectories while mitigating VIO drift. Thus, such a heterogeneous system can explore large areas with LiDAR precision, as well as visit locations inaccessible to the large LiDAR-carrying UAV platforms, as was showcased in a real-world cooperative mapping scenario.

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arXiv2026

Distributed Lloyd-Based algorithm for uncertainty-aware multi-robot under-canopy flocking

In this letter, we present a distributed algorithm for flocking in complex environments that operates at constant altitude, without explicit communication, no a priori information about the environment, and by using only on-board sensing and computation capabilities. We provide sufficient conditions to guarantee collision avoidance with obstacles and other robots without exceeding a desired maximum distance from a predefined set of neighbors (flocking or proximity maintenance constraint) during the mission. The proposed approach allows to operate in crowded scenarios and to explicitly deal with tracking errors and on-board sensing errors. The algorithm was verified through simulations with varying number of UAVs and also through numerous real-world experiments in a dense forest involving up to four UAVs.

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arXiv2026

Autonomous Inspection of Power Line Insulators with UAV on an Unmapped Transmission Tower

This paper introduces an online inspection algorithm that enables an autonomous UAV to fly around a transmission tower and obtain detailed inspection images without a prior map of the tower. Our algorithm relies on camera-LiDAR sensor fusion for online detection and localization of insulators. In particular, the algorithm is based on insulator detection using a convolutional neural network, projection of LiDAR points onto the image, and filtering them using the bounding boxes. The detection pipeline is coupled with several proposed insulator localization methods based on DBSCAN, RANSAC, and PCA algorithms. The performance of the proposed online inspection algorithm and camera-LiDAR sensor fusion pipeline is demonstrated through simulation and real-world flights. In simulation, we showed that our single-flight inspection strategy can save up to 24 % of total inspection time, compared to the two-flight strategy of scanning the tower and afterwards visiting the inspection waypoints in the optimal way. In a real-world experiment, the best performing proposed method achieves a mean horizontal and vertical localization error for the insulator of 0.16 +- 0.08 m and 0.16 +- 0.11 m, respectively. Compared to the most relevant approach, the proposed method achieves more than an order of magnitude lower variance in horizontal insulator localization error.

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arXiv2026

Position-Based Flocking for Persistent Alignment without Velocity Sensing

Coordinated collective motion in bird flocks and fish schools inspires algorithms for cohesive swarm robotics. This paper presents a position-based flocking model that achieves persistent velocity alignment without velocity sensing. By approximating relative velocity differences from changes between current and initial relative positions and incorporating a time- and density-dependent alignment gain with a non-zero minimum threshold to maintain persistent alignment, the model sustains coherent collective motion over extended periods. Simulations with a collective of 50 agents demonstrate that the position-based flocking model attains faster and more sustained directional alignment and results in more compact formations than a velocity-alignment-based baseline. This position-based flocking model is particularly well-suited for real-world robotic swarms, where velocity measurements are unreliable, noisy, or unavailable. Experimental results using a team of nine real wheeled mobile robots are also presented.

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arXiv2026

Geometric Model Predictive Path Integral for Agile UAV Control with Online Collision Avoidance

In this letter, we introduce Geometric Model Predictive Path Integral (GMPPI), a sampling-based controller capable of tracking agile trajectories while avoiding obstacles. In each iteration, GMPPI generates a large number of candidate rollout trajectories and then averages them to create a nominal control to be followed by the controlled Unmanned Aerial Vehicle (UAV). Classical Model Predictive Path Integral (MPPI) faces a trade-off between tracking precision and obstacle avoidance; high-noise random rollouts are inefficient for tracking but necessary for collision avoidance. To this end, we propose leveraging geometric SE(3) control to generate a portion of GMPPI rollouts. To maximize their benefit, we introduce a UAV-tailored cost function balancing tracking performance with obstacle avoidance. All generated rollouts are projected onto depth images for collision avoidance, representing, to our knowledge, the first method utilizing depth data directly in a UAV MPPI loop. Simulations show GMPPI matches the tracking error of an obstacle-blind geometric controller while exceeding the avoidance capabilities of state-of-the-art planners and learning-based controllers. Real-world experiments demonstrate flight at speeds up to 17 m/s and obstacle avoidance up to 10 m/s.

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arXiv2026

FDA Flocking: Future Direction-Aware Flocking via Velocity Prediction

Understanding self-organization in natural collectives such as bird flocks inspires swarm robotics, yet most flocking models remain reactive, overlooking anticipatory cues that enhance coordination. Motivated by avian postural and wingbeat signals, as well as multirotor attitude tilts that precede directional changes, this work introduces a principled, bio-inspired anticipatory augmentation of reactive flocking termed Future Direction-Aware (FDA) flocking. In the proposed framework, agents blend reactive alignment with a predictive term based on short-term estimates of neighbors' future velocities, regulated by a tunable blending parameter that interpolates between reactive and anticipatory behaviors. This predictive structure enhances velocity consensus and cohesion-separation balance while mitigating the adverse effects of sensing and communication delays and measurement noise that destabilize reactive baselines. Simulation results demonstrate that FDA achieves faster and higher alignment, enhanced translational displacement of the flock, and improved robustness to delays and noise compared to a purely reactive model. Future work will investigate adaptive blending strategies, weighted prediction schemes, and experimental validation on multirotor drone swarms.

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arXiv2026

Vision-only UAV State Estimation for Fast Flights Without External Localization Systems: A2RL Drone Racing Finalist Approach

Fast flights with aggressive maneuvers in cluttered GNSS-denied environments require fast, reliable, and accurate UAV state estimation. In this paper, we present an approach for onboard state estimation of a high-speed UAV using a monocular RGB camera and an IMU. Our approach fuses data from Visual-Inertial Odometry (VIO), an onboard landmark-based camera measurement system, and an IMU to produce an accurate state estimate. Using onboard measurement data, we estimate and compensate for VIO drift through a novel mathematical drift model. State-of-the-art approaches often rely on more complex hardware (e.g., stereo cameras or rangefinders) and use uncorrected drifting VIO velocities, orientation, and angular rates, leading to errors during fast maneuvers. In contrast, our method corrects all VIO states (position, orientation, linear and angular velocity), resulting in accurate state estimation even during rapid and dynamic motion. Our approach was thoroughly validated through 1600 simulations and numerous real-world experiments. Furthermore, we applied the proposed method in the A2RL Drone Racing Challenge 2025, where our team advanced to the final four out of 210 teams and earned a medal.

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arXiv2026

Aerial Robots Persistent Monitoring and Target Detection: Deployment and Assessment in the Field

In this article, we present a distributed algorithm for multi-robot persistent monitoring and target detection. In particular, we propose a novel solution that effectively integrates the Time-inverted Kuramoto model, three-dimensional Lissajous curves, and Model Predictive Control. We focus on the implementation of this algorithm on aerial robots, addressing the practical challenges involved in deploying our approach under real-world conditions. Our method ensures an effective and robust solution that maintains operational efficiency even in the presence of what we define as type I and type II failures. Type I failures refer to short-time disruptions, such as tracking errors and communication delays, while type II failures account for long-time disruptions, including malicious attacks, severe communication failures, and battery depletion. Our approach guarantees persistent monitoring and target detection despite these challenges. Furthermore, we validate our method with extensive field experiments involving up to eleven aerial robots, demonstrating the effectiveness, resilience, and scalability of our solution.

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arXiv2025

Towards Fully Onboard State Estimation and Trajectory Tracking for UAVs with Suspended Payloads

This paper addresses the problem of tracking the position of a cable-suspended payload carried by an unmanned aerial vehicle, with a focus on real-world deployment and minimal hardware requirements. In contrast to many existing approaches that rely on motion-capture systems, additional onboard cameras, or instrumented payloads, we propose a framework that uses only standard onboard sensors--specifically, real-time kinematic global navigation satellite system measurements and data from the onboard inertial measurement unit--to estimate and control the payload's position. The system models the full coupled dynamics of the aerial vehicle and payload, and integrates a linear Kalman filter for state estimation, a model predictive contouring control planner, and an incremental model predictive controller. The control architecture is designed to remain effective despite sensing limitations and estimation uncertainty. Extensive simulations demonstrate that the proposed system achieves performance comparable to control based on ground-truth measurements, with only minor degradation (< 6%). The system also shows strong robustness to variations in payload parameters. Field experiments further validate the framework, confirming its practical applicability and reliable performance in outdoor environments using only off-the-shelf aerial vehicle hardware.

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arXiv2025

RADRON: Cooperative Localization of Ionizing Radiation Sources by MAVs with Compton Cameras

We present a novel approach to localizing radioactive material by cooperating Micro Aerial Vehicles (MAVs). Our approach utilizes a state-of-the-art single-detector Compton camera as a highly sensitive, yet miniature detector of ionizing radiation. The detector's exceptionally low weight (40 g) opens up new possibilities of radiation detection by a team of cooperating agile MAVs. We propose a new fundamental concept of fusing the Compton camera measurements to estimate the position of the radiation source in real time even from extremely sparse measurements. The data readout and processing are performed directly onboard and the results are used in a dynamic feedback to drive the motion of the vehicles. The MAVs are stabilized in a tightly cooperating swarm to maximize the information gained by the Compton cameras, rapidly locate the radiation source, and even track a moving radiation source.

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arXiv2025

FIMD: Fast Isolated Marker Detection for UV-Based Visual Relative Localisation in Agile UAV Swarms

A novel approach for the fast onboard detection of isolated markers for visual relative localisation of multiple teammates in agile UAV swarms is introduced in this paper. As the detection forms a key component of real-time localisation systems, a three-fold innovation is presented, consisting of an optimised procedure for CPUs, a GPU shader program, and a functionally equivalent FPGA streaming architecture. For the proposed CPU and GPU solutions, the mean processing time per pixel of input camera frames was accelerated by two to three orders of magnitude compared to the \rev{unoptimised state-of-the-art approach}. For the localisation task, the proposed FPGA architecture offered the most significant overall acceleration by minimising the total delay from camera exposure to detection results. Additionally, the proposed solutions were evaluated on various 32-bit and 64-bit embedded platforms to demonstrate their efficiency, as well as their feasibility for applications using low-end UAVs and MAVs. Thus, it has become a crucial enabling technology for agile UAV swarming.

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arXiv2025

Simultaneous learning of state-to-state minimum-time planning and control

This paper tackles the challenge of learning a generalizable minimum-time flight policy for UAVs, capable of navigating between arbitrary start and goal states while balancing agile flight and stable hovering. Traditional approaches, particularly in autonomous drone racing, achieve impressive speeds and agility but are constrained to predefined track layouts, limiting real-world applicability. To address this, we propose a reinforcement learning-based framework that simultaneously learns state-to-state minimum-time planning and control and generalizes to arbitrary state-to-state flights. Our approach leverages Point Mass Model (PMM) trajectories as proxy rewards to approximate the true optimal flight objective and employs curriculum learning to scale the training process efficiently and to achieve generalization. We validate our method through simulation experiments, comparing it against Nonlinear Model Predictive Control (NMPC) tracking PMM-generated trajectories and conducting ablation studies to assess the impact of curriculum learning. Finally, real-world experiments confirm the robustness of our learned policy in outdoor environments, demonstrating its ability to generalize and operate on a small ARM-based single-board computer.

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arXiv2025

Optical communication-based identification for multi-UAV systems: theory and practice

Mutual relative localization and identification is an important feature for the stabilization and navigation of multi-Unmanned Aerial Vehicle (UAV) systems. Camera-based communications technology, also referred to as Optical Camera Communications (OCC) in the literature, is a novel approach that could bring a valuable solution to such a complex task. In such system, the UAVs are equipped with LEDs that act as beacons and with cameras allowing them to locate the LEDs of other UAVs. Specific blinking sequences are assigned to the LEDs of each of the UAVs in order to uniquely identify them. This camera-based relative localization and identification system is immune to Radio Frequency (RF) electromagnetic interference and operates in Global Navigation satellite (GNSS) denied environments. In addition, since many UAVs are already equipped with cameras, the implementation of this system is inexpensive. In this article, we study in detail the capacity of this system and its limitations. Furthermore, we show how to construct blinking sequences for UAV LEDs in order to improve system performance. Finally, experimental results are presented to corroborate the analytical derivations.

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arXiv2025

Towards agile multi-robot systems in the real world: Fast onboard tracking of active blinking markers for relative localization

A novel onboard tracking approach enabling vision-based relative localization and communication using Active blinking Marker Tracking (AMT) is introduced in this article. Active blinking markers on multi-robot team members improve the robustness of relative localization for aerial vehicles in tightly coupled multi-robot systems during real-world deployments, while also serving as a resilient communication system. Traditional tracking algorithms struggle with fast-moving blinking markers due to their intermittent appearance in camera frames and the complexity of associating multiple of these markers across consecutive frames. AMT addresses this by using weighted polynomial regression to predict the future appearance of active blinking markers while accounting for uncertainty in the prediction. In outdoor experiments, the AMT approach outperformed state-of-the-art methods in tracking density, accuracy, and complexity. The experimental validation of this novel tracking approach for relative localization and optical communication involved testing motion patterns motivated by our research on agile multi-robot deployment.

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arXiv2025

Swarming Without an Anchor (SWA): Robot Swarms Adapt Better to Localization Dropouts Then a Single Robot

In this paper, we present the Swarming Without an Anchor (SWA) approach to state estimation in swarms of Unmanned Aerial Vehicles (UAVs) experiencing ego-localization dropout, where individual agents are laterally stabilized using relative information only. We propose to fuse decentralized state estimation with robust mutual perception and onboard sensor data to maintain accurate state awareness despite intermittent localization failures. Thus, the relative information used to estimate the lateral state of UAVs enables the identification of the unambiguous state of UAVs with respect to the local constellation. The resulting behavior reaches velocity consensus, as this task can be referred to as the double integrator synchronization problem. All disturbances and performance degradations except a uniform translation drift of the swarm as a whole is attenuated which is enabling new opportunities in using tight cooperation for increasing reliability and resilience of multi-UAV systems. Simulations and real-world experiments validate the effectiveness of our approach, demonstrating its capability to sustain cohesive swarm behavior in challenging conditions of unreliable or unavailable primary localization.

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