arXiv:2606. 31216v1 Announce Type: cross Abstract: The Doppler velocity log (DVL) velocity measurements are critical to the accuracy of autonomous underwater vehicle (AUV) navigation solutions and, consequently, to mission success.
arXiv:2607. 05669v1 Announce Type: cross Abstract: Reliable localization in GNSS-denied environments remains a fundamental challenge for intelligent vehicles, as inertial navigation systems accumulate unbounded drift without external correction.
The paper tackles the problem of calibrating a stationary camera’s yaw, pitch, and roll using a drone’s GPS trajectory, which suffers from altitude bias, time offset, and discrete sampling. It formulates a parameter estimation problem that jointly estimates the GPS altitude bias, time offset, and camera orientation biases, and proposes a maximum likelihood estimator based on Iterated Least Squares to handle the asynchronous, discrete GPS data. Simulation results show the estimator achieves accuracy close to the Cramér–Rao Lower Bound, with a recommended drone trajectory yielding calibration errors within 14% of the measurement error standard deviation.
arXiv:2606. 19711v1 Announce Type: cross Abstract: Field-based modeling from onboard measurements can produce autonomous underwater vehicle (AUV) maneuvering models that reflect real operating characteristics.
arXiv:2603. 08457v2 Announce Type: replace-cross Abstract: Robust single-vessel tracking from fixed coastal platforms is hindered by modality-specific degradations: cameras suffer from illumination and visual clutter, while LiDAR performance drops with range and intermittent returns.
arXiv:2608. 04201v1 Announce Type: new Abstract: State estimation for nonlinear dynamical systems is commonly performed with the Unscented Kalman filter (UKF), which propagates the state moments through deterministic sigma points and reports a posterior covariance at every step.
arXiv:2608. 04201v2 Announce Type: replace Abstract: Nonlinear state estimation requires sequentially fusing model-based predictions with noisy measurements.
ALS boresight calibration has relied for two decades on dedicated flight patterns over structured scenes containing planar surfaces of varied aspect and slope. While reliable, this approach imposes constraints on the scene content and operations, which limits its applicability to boresight recovery within routine mapping missions.
arXiv:2601. 03040v2 Announce Type: replace-cross Abstract: A fundamental requirement for full autonomy is the ability to sustain accurate navigation in the absence of external data, such as GNSS signals or visual information.
arXiv:2606. 19176v1 Announce Type: cross Abstract: Autonomous UAV operations on ships require reliable vision-based relative pose estimation, yet at-sea validation is costly, weather-dependent, and risky.
RiverVLN introduces the first benchmark for long‑horizon vision‑language navigation (VLN) of unmanned surface vehicles (USVs) in continuous riverine motion. The PGT‑NAV framework converts navigation instructions into an ordered sequence of visually verifiable semantic phases, maintaining an active phase online through grounded visual and motion evidence. This phase‑grounded approach reduces recursive position and heading drift, achieving a 0.79 success rate in Unity‑ROS closed‑loop tests and demonstrating transfer to real‑world USV deployment.
arXiv:2609.00730v1 Announce Type: new Abstract: Accurate tilt angle estimation is important in many engineering applications, such as robotics, motion tracking, and embedded control systems. However,...
FMCW-LIO introduces a novel LiDAR‑inertial odometry framework that exploits the Doppler velocity measurements provided by Frequency Modulated Continuous Wave (FMCW) Doppler LiDARs. By incorporating a motion compensation scheme and a Doppler‑aided observation model, the method effectively removes dynamic points and enhances geometric consistency. Experiments across diverse scenes demonstrate that FMCW‑LIO delivers more accurate state estimation and robust static mapping, outperforming existing algorithms in both accuracy and resilience.