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. 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.
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.
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: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,...
arXiv:2602. 20958v2 Announce Type: replace-cross Abstract: Vision-based Unmanned Aerial Vehicles (UAVs) frameworks aid human search tasks by detecting and recognizing specific individuals, then tracking and following them while maintaining a safe distance.
arXiv:2606. 13509v1 Announce Type: cross Abstract: Indoor vision-based localization systems are affected by detection noise, occlusions, and limited camera coverage, leading to uncertainty at multiple stages of the pipeline.
The paper introduces CalfVO, a monocular visual odometry system that operates without camera intrinsics, test‑time optimization, bundle adjustment, or loop closure. Using a transformer, it predicts relative poses with separate rotation and translation confidences over overlapping image windows, then aggregates these predictions via a confidence‑weighted module to produce a single trajectory. CalfVO achieves the highest accuracy among calibration‑free methods across five benchmarks and runs at 53 FPS, outperforming all baselines.
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.
The paper introduces an on-the-fly homography calibration system for multi-camera tracking that starts from coarse manual homographies and refines them using a centroid-based projection optimization (PO) on live detection metadata. PO continuously aligns ground-plane geometry without adding computational latency, enabling the system to adapt automatically to camera movements or environmental changes. The refined geometry feeds a bird's-eye-view tracker that fuses detections and unifies trajectories across zones while maintaining privacy safety and zero overhead.
arXiv:2509.04600v2 Announce Type: replace Abstract: Reconstructing global human motion from monocular video is fundamental to VR, graphics, and robotics, yet remains ill-posed due to depth ambiguity,...
Precise multi-camera tracking traditionally relies on rigorous 3D site calibration, yet this requirement is often operationally impossible in large-scale deployments. Privacy regulations frequently pr...
The paper presents a new RSS‑based localization system that tracks ultra‑light, low‑power receivers across complex landscapes. By using a minimal number of RSS measurements from rotating high‑gain transmitters and probabilistic modeling, the system achieves about 15 m accuracy for 38 mg receivers over a 300 m range while consuming less than 180 µW. Increasing RSS measurements improves accuracy to roughly 10 m at under 600 µW, and the method is demonstrated on tracking Bombus terrestris nest return flights.