vision-based navigation

This work addresses the problem of estimating the relative pose and velocity of an unmanned aerial vehicle (UAV) with respect to an arbitrarily moving planar platform in 3D space, enabling precise approach and landing. The proposed method introduces a tightly coupled estimator that fuses measurements from two inertial measurement units (IMUs) and monocular vision—specifically, the platform’s center-line-of-sight direction and surface normal vector. A novel architecture combines an SO(3) Lie-group complementary filter with a linear Riccati-based cascaded observer; critically, it recovers unobservable attitude angles using only the platform’s linear acceleration under known normal-axis rotational constraints—a first in the literature. Theoretical analysis establishes local exponential convergence and almost-global asymptotic stability of the estimation error. Extensive simulations demonstrate high accuracy and strong robustness against dynamic disturbances, sensor noise, and large initial state errors.

This work addresses the challenges of unstable visual tracking, camera self-occlusion, and terminal impact-angle control encountered by fixed-wing unmanned aerial vehicles (UAVs) during the full “detect–track–strike” mission against moving targets. To overcome these issues, a three-stage cooperative strategy is proposed: initial target acquisition via YOLO-based vision fused with inertial measurements, stable tracking with self-occlusion avoidance using a constraint-aware nonlinear model predictive controller (NMPC) enhanced by control barrier functions (CBFs), and high-precision interception under impact-angle constraints through a switch to quaternion-based biased proportional navigation guidance (BPNG) upon satisfaction of terminal conditions. High-fidelity simulations demonstrate that the proposed framework significantly improves tracking robustness and terminal strike accuracy while rigorously respecting vehicle dynamics and field-of-view constraints.

This paper addresses the problem of full onboard state estimation and trajectory tracking for unmanned aerial vehicle (UAV) payload slung-load transportation. Methodologically, it proposes a lightweight, highly robust autonomous control framework that fuses RTK-GNSS and IMU measurements within a coupled dynamic model; employs a hierarchical predictive control architecture; integrates linear Kalman filtering for state estimation; and combines model-predictive contour control with incremental MPC for high-precision trajectory tracking. The key contribution lies in eliminating reliance on external motion-capture systems or specialized hardware—achieving closed-loop control using only standard onboard sensors. Simulation results show less than 6% performance degradation under nominal conditions, while outdoor experiments demonstrate strong robustness against modeling uncertainties and parametric perturbations in complex environments. This significantly enhances the practicality and deployment flexibility of slung-load UAV systems.

This work addresses the problem of estimating the relative pose and velocity of a moving target using an IMU-equipped observer, assuming availability of either relative position or bearing measurements. By modeling the relative dynamics on the SE₂(3) Lie group and embedding them into ℝ¹⁵ to form a linear time-varying system, the authors propose a hybrid estimation architecture that combines a Riccati observer with an SO(3) nonlinear complementary filter. This approach achieves, for the first time within the SE₂(3) framework, a unified fusion of dual-IMU and relative sensing data, establishing a consistency observability condition that relies solely on target acceleration excitation. The method theoretically guarantees global exponential convergence of the estimation error and almost global asymptotic stability of the orientation estimate. Numerical simulations validate the effectiveness of the proposed technique.

Existing video generation methods suffer from insufficient precision in camera motion control and neglect explicit modeling of subject motion dynamics, failing to meet professional-grade controllability requirements. To address this, we propose a high-precision, disentangled framework for joint camera and subject control. Our approach introduces 3D point trajectories in the camera coordinate system as control signals, explicitly modeling high-order motion dynamics—including acceleration and jerk—and incorporates an adjustable motion scaling operator. We adopt a lightweight, base-model-agnostic Adapter-based fine-tuning architecture. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art approaches on both static and dynamic scenes. Quantitative evaluations show consistent improvements across key metrics (e.g., CAM-PSNR, Motion-FID), while qualitative results exhibit markedly more accurate camera choreography and natural, fine-grained controllability over subject motion.

This work addresses the challenge of validating autonomous shipboard UAV operations under realistic maritime conditions, which is typically hindered by high costs, safety risks, and environmental constraints. To bridge the gap between pure simulation and real-ship trials, the authors present a high-fidelity hardware-in-the-loop simulation framework that faithfully replicates dynamic sea environments indoors while preserving the perception latency and asynchrony inherent to embedded platforms. The system employs a deep Transformer-based monocular visual pose estimator, tightly fused with IMU measurements via a delayed Kalman filter to achieve robust state estimation. This pipeline drives a geometric controller enabling fully autonomous flight. Experimental results demonstrate stable takeoff, accurate trajectory tracking, and successful landing under computational constraints and perceptual delays, thereby establishing a critical validation intermediary between simulation and at-sea testing.

Monocular visual-inertial odometry struggles to recover metric scale from vision alone, and the relationship between scale observability and trajectory characteristics remains unclear. This work addresses this gap through an observability analysis, revealing that translational acceleration induced by trajectory curvature is the key factor coupling scale with inertial states. Leveraging the asymmetry between gravity and linear acceleration in the IMU model, the study establishes a theoretical framework and, for the first time, explicitly identifies trajectory curvature as the decisive contributor to scale observability. A lightweight trajectory excitation metric is proposed, computable directly from raw IMU data. Experiments on straight, circular, and figure-eight trajectories yield scale errors of 9.2%, 6.4%, and 4.8%, respectively, with excitation levels spanning four orders of magnitude, thereby validating the effectiveness of trajectory design in enhancing scale recovery accuracy.

Existing relative pose estimation algorithms incur high computational costs and rely heavily on numerous feature matches, making them ill-suited for the real-time and robustness demands of autonomous driving. This work proposes a unified and efficient framework for relative pose estimation that introduces a novel translation parameterization and a first-order rotation approximation to derive three minimal solvers tailored for ground vehicles. By integrating multi-source priors—such as IMU-provided gravity direction, rotational axis constraints during steering, and the planar motion assumption—the method substantially reduces both the required number of point correspondences and algebraic complexity. Experiments on synthetic data and the KITTI benchmark demonstrate that the proposed approach achieves a superior trade-off between accuracy and speed compared to state-of-the-art methods.

This study addresses the challenge of sustained lane-following for low-cost monocular-camera-equipped unmanned ground vehicles when the guidance line is occluded or lost. The authors propose a lightweight, two-stage self-recovery method: first attempting to reacquire the line through in-place rotation and dynamically relaxed HSV color thresholds; if unsuccessful, the system leverages monocular visual odometry to backtrack to a previously saved “breadcrumb” waypoint and restart tracking. Notably, this approach achieves closed-loop, self-healing visual recovery without LiDAR, GPS, or GPU support, embedding a complete MAPE-K control loop within a 50 ms cycle and operating entirely on CPU at 20 Hz. Experimental results in Webots across three tracks with 119 injected failures demonstrate an 86.6% successful recovery rate and a median recovery time of 3.26 seconds.

This work addresses the high computational complexity and substantial point correspondence requirements that often hinder practical deployment of relative pose estimation in multi-camera systems. The authors propose two efficient minimal solvers that, for the first time, incorporate IMU-derived priors—either the vertical direction or a known rotation axis—into a minimal solver framework. Requiring only four point correspondences, the approach leverages a novel parametrization and algebraic geometry techniques to reduce the problem to solving a univariate sextic polynomial, a significant simplification over existing octic formulations. Integrated within a RANSAC-based robust estimation pipeline, extensive experiments on synthetic data and the KITTI benchmark demonstrate that the proposed method achieves comparable accuracy while substantially lowering computational cost and data requirements.