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.
By R. Yang, Y. Bar-Shalom, H. A. J. Huang
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.
By Zeev Yampolsky, Itzik Klein
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.
By Abinav Kalyanasundaram, Karthikeyan Chandra Sekaran, Wolfgang Utschick, Michael Botsch
Accurate six-degree-of-freedom (6-DOF) motion estimation is essential for robotic manipulation, autonomous systems, and structural displacement monitoring. Conventional 3D-2D methods estimate absolute camera poses independently at each time and recover platform motion through camera-to-platform extrinsics, making them sensitive to extrinsic calibration errors, especially for micromotion.
arXiv:2507. 21245v2 Announce Type: replace-cross Abstract: An accurate initial heading angle is essential for efficient and safe navigation across diverse domains.
By Gershy Ben-Arie, Daniel Engelsman, Rotem Dror, Itzik Klein
arXiv:2608. 15621v1 Announce Type: new Abstract: Human Activity Recognition (HAR) with self-administered wearables, such as at-home rehabilitation and exercise monitoring, often requires reattaching inertial measurement units (IMUs) across sessions.
By Seungyeol Baek, Yoonbyung Chai, Yonghyeon Lee, Sungjoon Choi, Sungho Suh
The paper investigates how processing parameters affect the accuracy of UAV photogrammetry. By testing 768 processing variants on ten datasets over 1.5 years, the study finds that the best configuration yields an RMSE of 16 mm, while the worst reaches 303 mm. Key factors include the number of ground control points, camera calibration corrections, and the use of Post‑Processing Kinematic GNSS for camera center determination, which together reduce systematic errors by more than half.
By Pawe{\l} \'Cwi\k{a}ka{\l}a, Edyta Puniach, El\.zbieta Pastucha, Wojciech Gruszczy\'nski
arXiv:2606. 28475v1 Announce Type: cross Abstract: Nowadays mobile robots have wide engineering applications.
By Seyed Farzad Bahreinian, Maziar Palhang, Mohammad Reza Taban, Hasan Enami Eraghi
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.
By Minhyeok Ko, Abdollah Shafieezadeh
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.
By Arup Kumar Sahoo, Itzik Klein
arXiv:2608. 20056v1 Announce Type: new Abstract: Inertial measurement units (IMUs) are now standard in most consumer devices, such as smartphones, drones, and extended reality (XR) headsets.
By Marcus Valtonen \"Ornhag, Alberto Jaenal, Stefan Adalbj\"ornsson
The paper introduces Free-Init, a high‑frequency, resilient initialization framework for LiDAR‑inertial systems that uses FMCW Doppler LiDAR to capture both point range and Doppler velocity. By fusing point‑wise Doppler velocity with inertial measurements, Free‑Init eliminates the need for motion undistortion, excitation motions, and map correspondences during initialization, making it plug‑and‑play for a wide range of initial motions, including stationary, dynamic, and violent movements. Experiments on diverse platforms and motion scenarios demonstrate that Free‑Init achieves fast convergence and high‑frequency performance, delivering outputs exceeding 10 kHz and outperforming existing methods.
By Mingle Zhao, Jiahao Wang, Tianxiao Gao, Chengzhong Xu, Hui Kong