uVGS-2: The Micro Video Guidance Sensor: A 6-DoF Robust Pose Estimator for Autonomous Proximity Maneuvers in Drones, Spacecraft and Mobile Robot Navigation
This paper presents the Micro Video Guidance Sensor Version 2 (uVGS-2), a ROS-based vision navigation framework for real-time six-degrees-of-freedom pose estimation in drones, spacecraft, and autonomous robotic platforms operating in GNSS-denied environments. The system evolves from the previous Smartphone Video Guidance Sensor (SVGS) architecture through a modular C++ implementation, including advanced image preprocessing, deterministic blob sorting, and an optimized perspective-4-point solver using a Lie-algebra-based analytical Jacobian formulation. The proposed architecture achieves computationally efficient photogrammetric state estimation using onboard camera and processor resources, enabling deployment in resource-constrained systems. Experimental validation was conducted in NASA’s Astrobee free-flying robot, both at the International Space Station (ISS), for SVGS, and by ground testing through real-time sensor-fusion with Astrobee’s graph-based localizer (Astroloc), for uVGS-2. Results demonstrate robust centimeter-level accuracy in relative position and attitude estimation under illumination disturbances, partial occlusions, and intermittent loss of line-of-sight. The framework can be used in robotic platforms and autonomous UAV operations, including precision landing, formation flight, and cooperative navigation in environments where GNSS signals are unavailable or intermittent.
This paper presents a tightly coupled monocular point-feature visual-inertial odometry (VIO) system for that setting, realized on a GTSAM fixed-lag factor graph with inverse-depth landmarks, on-manifold IMU preintegration, and an online loop-closure pose graph.
G. Araújo, Ruben Santos, J. J. Martins et al.· Drones· 0 citations
Indoor mobile robots equipped with low-cost and sparse sensors often suffer from limited vertical perception and dynamic residual artifacts in the final map. This paper presents a lightweight 2.5D simultaneous localization and mapping (SLAM) framework using a single-line laser distance sensor (LDS), time-of-flight (ToF...
Guitao Yu, Yuping Zhang, Zhiao Qi et al.· Italian National Conference...· 0 citations
Reliable localization is required for autonomous mobile robots when individual sensing streams become noisy, intermittent, or unavailable. This study evaluates a multi-sensor fusion framework that combines LiDAR, monocular vision, GPS, UWB, and IMU data using three strategies: (i) a baseline Extended Kalman Filter (E...
Muhammad Shahzad Alam Khan, Anas Bin Aqeel, Hassan Elahi et al.· Scientific Reports· 0 citations
This article presents a kinematic-inertial-LiDAR-visual odometry for humanoid robots, called KILVO. Tailored to the platform features, requirements, and real-world complexity, it fully utilizes the sensors commonly equipped on humanoid robots, including joint encoders, IMU, LiDAR, and camera, within an asynchronous-seq...
Jixin Gao, Fucheng Liu, Teng Zhang et al.· IEEE/ASME transactions on me...· 0 citations
This work presents real-world experimental results from a Bayesian underwater simultaneous localization and mapping (SLAM) system deployed on a remotely operated vehicle (ROV) and evaluated in both controlled pool experiments and ocean settings. The SLAM system uses a feature-based visual SLAM approach that extracts re...
Hala Abualsaud, Ying-Tsong Lin, Peter Gerstoft· Journal of the Acoustical So...· 0 citations
An error-state estimation framework is developed in which RTK-GNSS, IMU, and LiDAR-inertial odometry are combined within a tightly coupled, factor-graph-augmented iterated Kalman filter for UAV state estimation.
S. Saiki, Saadu Olayinka Isiaka, Agu Victor Emezie et al.· Global Journal of Engineerin...· 0 citations
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