2026· Materials Research Proceedings· Vol 69, pp. 1012-1017· 0 citations
TL;DR
A robotic testbed installed at the Microsatellites and Space Microsystems Laboratory of the University of Bologna for hardware-in-the-loop validation of vision-based spacecraft pose estimation algorithms is presented.
Abstract
Abstract. Vision-based navigation is a key enabling technology for autonomous in-orbit operations. However, current deep learning-based pose estimation algorithms face a critical sim-to-real gap, where performance degrades significantly when transitioning from synthetic training images to real ones. This has driven the need for ground-based validation facilities to bridge this gap. This paper presents a robotic testbed installed at the Microsatellites and Space Microsystems Laboratory of the University of Bologna for hardware-in-the-loop validation of vision-based spacecraft pose estimation algorithms. The facility features a 7-DoF robotic system, an optical table with a spacecraft mockup, a sun simulator, and an RGB camera, all integrated within a controlled dark room environment. The software architecture is based on ROS 2, with custom nodes enabling coordinated control of all system components. An initial application demonstrates the facility's capability through the development of a dataset generation pipeline for the ALMASat spacecraft mockup. While designed within constrained budget and space requirements, the testbed provides high precision and supports both fundamental research and educational activities in vision-based spacecraft navigation.
A scalable framework for real-time, 6-DOF pose estimation of uncooperative space objects using monocular cameras, addressing dataset scarcity by generating mission-specific synthetic data augmented with style randomization to mitigate domain shift.
Matteo Forasassi· Materials Research Proceedin...· 0 citations
Reliable mobile-robot deployment requires mapping, localization, global planning, and real-time obstacle avoidance to operate consistently under sensor noise and physical constraints. This study presents a vision-based hybrid navigation framework for a differential-drive mobile robot in a warehouse-like environment. The main contribution lies in the system-level integration and experimental validation of established techniques rather than the development of a new standalone navigation algorithm. A ceiling-mounted camera converts top-view images into an occupancy-grid map and world coordinates, while a convolutional neural network (CNN) recognizes goal markers. Odometry–augmented reality University of Cordoba (ArUco) fusion is used to correct accumulated localization drift. Particle swarm optimization (PSO) generates smooth global paths offline, whereas the dynamic window approach (DWA) performs real-time local motion control. Experiments in a 3.3 m × 2.4 m workspace achieved average obstacle-localization errors of 0.959 cm and 0.696 cm along the x- and y-axes, respectively, and a goal-recognition accuracy of 99%. The DWA controller required 12.61±2.42 ms per cycle, while rapidly-exploring random tree (RRT) and PSO required 4–7 s and 477–692 s, respectively, for global path generation. Simulation and physical experiments confirmed collision-free navigation and successful quick response (QR)-code-based goods inspection, demonstrating the feasibility of the proposed framework for small, structured indoor environments.
T. Q. Le, T. Luu· IAES International Journal o...· 0 citations
This research entails the development of a deep learning-based framework to localize a steerable mobile robot using the following sensors: two-dimensional laser scanner, inertial measurement unit (IMU) and wheel encoders. Current methods for sensor fusion such as the Extended Kalman Filter (EKF) which rely heavily on linear approximations and sensor input quality are sensitive to large angular rotations and accelerations resulting in increased error. The results of this work will provide an accurate deep learning framework for precise nonlinear positioning. The localization problem is structured as a time series pose regression problem. Estimates for pose are provided using two successive laser scans and a designed Recurrent Convolutional Neural Network (RCNN) to fuse the remaining sensors which receive measurement data at varying rates. This framework is composed of one convolutional network block for the laser scanning data alongside several recurrent network blocks to accommodate the remaining sensors. Furthermore, the robot’s selected trajectory is decomposed into sequences for computational efficiency during investigation. The framework is first developed in simulation using MATLAB with PyTorch and then a physical experiment is performed using a front wheel steering, rear wheel drive robot, known as the Patrolbot outfitted with two LIDARs, wheel encoders and IMU with positioning results compared to the ground truth. It was seen that the global pose of the robot accumulates error and suffers from drift over time but can be improved with an optimization implementation comparing position points to a generated submap which is planned as a future research direction. This research has various applications and can be used to localize robots in a laboratory setting with moving obstacles (environment obstacle position varies) or for the robot to successfully park itself at a goal point with high accuracy. In addition, there minimal research investigations in the area of deep learning sensor fusion with virtually no investigations performed prior to 2021.
M. Peiris, H. Lang, M. El-Gindy et al.· Journal of Physics, Conferen...· 0 citations
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.
Hector M. Gutierrez, Jose Cornejo, Ivan R. Bertaska· Drones· 1 citation
: The evolution of Unmanned Aerial Vehicles (UAVs) into self-governing robotic entities is currently limited by the high computational overhead of modern neural networks. This review investigates the intersection of high-fidelity deep learning and the stringent resource limitations of edge-based aerial hardware. We present a systematic classification of recent breakthroughs in real-time perception, specifically evaluating stereo image processing and Simultaneous Localization and Mapping (SLAM) through the lens of computational economy. Drawing on extensive industrial experience in drone manufacturing and AI department leadership, this paper analyzes the efficacy of specialized optimization frameworks—such as multi-threaded frame tiling and hardware-concurrency mapping—designed to maximize inference speed on embedded CPU/GPU architectures. We further examine the role of spatio-temporal modeling and LSTM-based architectures in navigating unpredictable environments, while synthesizing the requirements for safety-critical, responsible AI deployment. By aligning theoretical algorithmic pruning with the practical realities of the product lifecycle, this survey provides a definitive technical roadmap for engineers and researchers aiming to achieve robust, on-board autonomy in the next generation of intelligent flight systems.
Rayan Abri, Sara Abri, S. Cetin· Proceedings of the 15th Inte...· 0 citations
This work addresses the problem of increasing the temporal resolution of robot positioning in outdoor inspection tasks by leveraging high-frequency inertial measurements. A learning-based approach is proposed to estimate incremental displacement from IMU data combined with GNSS/RTK positioning, using data collected along a predefined trajectory with a mobile robotic platform. Two neural architectures, LSTM and Transformer, are evaluated under different data preparation strategies. Offline validation shows that variations in hyperparameters have limited impact on performance, while the adopted data representation plays a more significant role, with high-resolution IMU–GNSS alignment outperforming feature-based approaches. The selected models were deployed on the robotic platform and tested in the same environment used for data collection, demonstrating real-time operation at the IMU sampling rate and achieving mean errors of approximately 0.140 m and 0.113 m for LSTM and Transformer, respectively. These results indicate that the proposed approach can enhance positioning update rates and support real-time motion estimation in robotic inspection tasks.
José Mario Nishihara de Albuquerque, João Henrique Campos Soares, Giovanni Braglia et al.· 2026 IEEE/ASME International...· 0 citations