arXiv subjects

David Cabecinhas

Publications and source records attributed to David Cabecinhas.

6 recordsLinked to original sources

arXiv2026

Acoustic-based Guidance for Automatic Docking of Holonomic AUVs

This paper describes a system to automatically dock an AUV onto a docking station without precise knowledge of the position and orientation of the latter, in the presence of unknown ocean currents, using a fully acoustic sensing architecture. The system relies on a pair of Ultrashort Baseline sensors, one onboard the vehicle and one installed on a seabed-resident docking station, enabling operation in low-visibility environments where cameras are ineffective. Relative orientation is estimated by a nonlinear complementary filter on $SO(3)$, while an Extended Kalman Filter provides relative position, supplying pose estimates to a geometric controller on $SE(3)$ that executes the docking manoeuvre. The complete system is implemented in a dedicated software suite and validated in simulation and water trials.

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arXiv2026

Distributed ToA Localization of Acoustic Sources with Unknown Time of Emission via Operator Splitting

Localization of non-cooperative acoustic sources using multiple spatially distributed receivers is critical for applications such as marine-life tracking, search-and-rescue operations, and maritime security in underwater environments. In conventional Time of Arrival (ToA) systems, the emission time is typically known explicitly or implicitly through clock synchronization or two-way communication, so the problem reduces to range-based localization. In passive settings, however, only reception timestamps are available. Thus, the emission time must be eliminated through Time Difference of Arrival (TDoA) preprocessing or estimated jointly with the source position. For the case of a single source, we address the distributed localization problem over a receiver network by reformulating passive localization directly as a ToA problem with unknown time of signal emission. This yields a distributed consensus optimization problem, which we solve using an operator-splitting method, namely an edge-based Distributed Alternating Direction Method of Multipliers (DADMM) scheme that decomposes the estimation task into local subproblems coupled through agreement constraints. We derive closed-form local update equations for the local DADMM subproblems and establish convergence properties for a smoothed approximation of the measurement model. Numerical simulations illustrate the efficacy of the proposed approach.

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arXiv2026

Moving Horizon Estimation for Underwater Target Tracking Based on Time-Difference-of-Arrival Measurements

There has been a flurry of activity in the development of robotic systems to localize and track underwater man-made or natural targets based on sparse acoustic data. Compelling examples include the development of surface tracking systems to aid in the navigation of groups of underwater vehicles performing environmental monitoring missions or to study the motion patterns of large underwater fauna. With current technology, the latter case can only be tackled using Time-Difference-of-Arrival (TDoA) techniques. Recent progress in nonlinear state estimation indicates that optimization-based methods may overcome the limitations of classical recursive filtering. However, achieving reliable estimator performance in the case of nonlinear target dynamics and sparse measurements remains a key challenge. In this paper, we study a Moving Horizon Estimation (MHE) approach to TDoA-based underwater target tracking. Through a 2D simulation environment capturing typical marine conditions, we show that the MHE-based estimator maintains reliable tracking in the considered scenarios even when the classical EKF becomes unreliable. The results highlight that multi-step trajectory coupling and physically consistent constraints, which are key advantages of the MHE approach, significantly enhance estimator robustness. It is shown that the MHE approach offers promise as a practical and scalable building block for future multi-agent tracking systems based on TDoA measurements operating in real underwater missions.

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arXiv2025

Depth Jitter: Seeing through the Depth

Depth information is essential in computer vision, particularly in underwater imaging, robotics, and autonomous navigation. However, conventional augmentation techniques overlook depth aware transformations, limiting model robustness in real world depth variations. In this paper, we introduce Depth-Jitter, a novel depth-based augmentation technique that simulates natural depth variations to improve generalization. Our approach applies adaptive depth offsetting, guided by depth variance thresholds, to generate synthetic depth perturbations while preserving structural integrity. We evaluate Depth-Jitter on two benchmark datasets, FathomNet and UTDAC2020 demonstrating its impact on model stability under diverse depth conditions. Extensive experiments compare Depth-Jitter against traditional augmentation strategies such as ColorJitter, analyzing performance across varying learning rates, encoders, and loss functions. While Depth-Jitter does not always outperform conventional methods in absolute performance, it consistently enhances model stability and generalization in depth-sensitive environments. These findings highlight the potential of depth-aware augmentation for real-world applications and provide a foundation for further research into depth-based learning strategies. The proposed technique is publicly available to support advancements in depth-aware augmentation. The code is publicly available on \href{https://github.com/mim-team/Depth-Jitter}{github}.

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arXiv2021

Quadrotor going through a window and landing: An image-based visual servo control approach

This paper considers the problem of controlling a quadrotor to go through a window and land on a planar target, the landing pad, using an Image-Based Visual Servo (IBVS) controller that relies on sensing information from two on-board cameras and an IMU. The maneuver is divided into two stages: crossing the window and landing on the pad. For the first stage, a control law is proposed that guarantees that the vehicle will not collide with the wall containing the window and will go through the window with non-zero velocity along the direction orthogonal to the window, keeping at all times a safety distance with respect to the window edges. For the landing stage, the proposed control law ensures that the vehicle achieves a smooth touchdown, keeping at all time a positive height above the plane containing the landing pad. For control purposes, the centroid vectors provided by the combination of the spherical image measurements of a collection of landmarks (corners) for both the window and the landing pad are used as position measurement. The translational optical flow relative to the wall, window edges, and landing plane is used as velocity cue. To achieve the proposed objective, no direct measurements nor explicit estimate of position or velocity are required. Simulation and experimental results are provided to illustrate the performance of the presented controller.

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arXiv2017

LiDAR-based Control of Autonomous Rotorcraft for the Inspection of Pier-like Structures: Proofs

This is a complementary document to the paper presented in [1], to provide more detailed proofs for some results. The main paper addresses the problem of trajectory tracking control of autonomous rotorcraft in operation scenarios where only relative position measurements obtained from LiDAR sensors are possible. The proposed approach defines an alternative kinematic model, directly based on LiDAR measurements, and uses a trajectory-dependent error space to express the dynamic model of the vehicle. An LPV representation with piecewise affine dependence on the parameters is adopted to describe the error dynamics over a set of predefined operating regions, and a continuous-time $H_2$ control problem is solved using LMIs and implemented within the scope of gain-scheduling control theory.

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