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.
Reliable localization without GPS is a prerequisite for autonomous unmanned aerial vehicles (UAVs) operating inside warehouses, where a lightweight monocular camera paired with an inertial measurement unit (IMU) and onboard computer are the minimal sensing and processing an onboard platform can carry. 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. The system is developed as the initial estimation stage of an autonomous stock-management UAV under development for indoor logistics warehouses. The decisive design element is the bootstrap: the metric, gravity-aligned initialization of a monocular estimator is well conditioned only under a translation-rich trajectory, a condition the near-zero-baseline pickup and takeoff transient that opens every indoor flight violates. Building on the visual-inertial alignment of VINS-Mono, we harden this step with a pre-bundle-adjust conditioning gate and a continuous-window initialization that refines the whole bootstrap window inside the smoother instead of freezing a single seed. On all eleven EuRoC MAV sequences, indoor flight tests recorded onboard a micro air vehicle in an industrial hall and two instrumented rooms, one fixed configuration per operating environment converges on every sequence, including three that otherwise diverge by tens to thousands of meters, and, driven by the same feature stream as locally run VINS-Mono and PL-VINS baselines, attains the better pure-odometry accuracy on nine of the eleven, with ATE RMSE of 0.12–0.37 m on the Machine Hall, a margin a paired signed-rank test confirms against VINS-Mono and leaves unconfirmed against PL-VINS at this sample size. We identify the stock fixed-lag marginalization as the principal consistency limitation and outline First-Estimates-Jacobian marginalization as the route to a more consistent estimator, establishing a characterized point-only baseline on one public benchmark as the starting point for subsequent on-platform work.
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) sensing, wheel odometry, and an inertial measurement unit (IMU). In this work, 2.5D refers to a 2D grid map with discretized vertical occupancy bins for each grid cell, rather than a full continuous 3D reconstruction. The system integrates multi-sensor synchronization, motion correction, error-state Kalman filter (ESKF)-based state estimation, normal distributions transform (NDT) registration, and pose graph optimization to reconstruct a pose-consistent global map. Based on this map, an offline dynamic refinement module estimates temporal voxel support across keyframes, extracts low-support candidate regions, and applies geometric clustering and isolated-point filtering to suppress transient residual artifacts while preserving stable structures. A 24-bit RGB occupancy encoding is further proposed to store the discretized vertical occupancy state in a compact three-channel image format. The proposed framework emphasizes system-level deployment value by combining sparse multi-sensor mapping, conservative offline refinement, and compact height-aware map export on a low-cost indoor robot platform. Experiments on public datasets, embedded hardware, and self-collected indoor sequences evaluate odometry reference performance, resource usage, platform-specific 2.5D mapping, dynamic refinement, and height-aware encoding.
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 (EKF); (ii) a dual-stage sequential EKF that refines LiDAR-Inertial Odometry (LIO) before the final fusion stage; and (iii) a hybrid learning-filtering approach in which modality-specific learned motion and position estimates are incorporated into an EKF. All evaluations were conducted in ROS-Gazebo under nominal operation and controlled sensor-degradation/dropout conditions. Relative to the controller-derived reference trajectory, the standard EKF achieved 0.2235 m RMSE and the dual-stage EKF achieved 0.2029 m RMSE, a descriptive reduction of approximately 9.2% for the reported run. The hybrid learning-EKF achieved 0.212 m RMSE under nominal sensing and 0.384 m RMSE during the tested failure sequence. These results support the evaluated fusion designs under the reported simulation conditions, but they do not establish statistical generalization or universal real-world resilience; independent ground truth, repeated trials, GPS ablation, and physical validation remain necessary.
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-sequential hybrid error-state iterated Kalman filter (ESIKF). Specifically, inertial data are used for prediction, leg kinematics are processed asynchronously at a high rate and provide proprioceptive constraints, while exteroception is updated sequentially, first by registering LiDAR points for geometric priors and then by updating the visual component via photometric errors. Moreover, the framework is elaborately designed with multimodal adaptation for resilience to sensor failures. A compact contact estimation module is also developed, sharing information with state estimation without additional sensors. Extensive experiments on public datasets and in the real world across multiple humanoid robots, gait patterns, and scenarios demonstrate that KILVO achieves highly competitive accuracy, efficiency, and output rates, with strong robustness against sensor degradation and failures, making it more suitable for humanoid robots than state-of-the-art fusion methods. Our code and datasets are released on GitHub.
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 repeatable keypoints and descriptors from a forward-facing camera and matches them across frames. These tracked visual features are fused with inertial measurements in a Maximum a Posteriori optimization to jointly estimate the ROV trajectory (pose over time) and a sparse visual landmark map. Camera calibration is performed to estimate the intrinsic parameters and lens distortion required for accurate feature projection. Acoustic sensing is integrated through an altimeter and sonar measurements, providing range-to-seafloor and to landmarks, enhancing vertical motion and overall 3-D trajectory estimation in visually degraded underwater conditions. System performance in the ocean setting is evaluated using fixed, moored reference points with known GPS locations to provide ground-truth trajectory validation.
Hala Abualsaud, Ying-Tsong Lin, Peter Gerstoft· Journal of the Acoustical So...· 0 citations
Unmanned aerial vehicles (UAVs) increasingly require centimetre- to decimetre-level position accuracy and reliable velocity estimates under conditions where no single onboard sensor is dependable throughout a mission. Real-time kinematic (RTK) GNSS delivers drift-free, globally referenced fixes but is vulnerable to multipath, obstruction, and correction-link loss; inertial measurement units (IMUs) propagate motion at high rate but accumulate unbounded bias-driven drift; and LiDAR supplies dense, satellite-independent relative-motion constraints at the cost of dependence on environmental structure and higher computational load. This paper synthesizes the literature on RTK-GNSS, IMU, and LiDAR sensing and on loosely coupled, tightly coupled, and factor-graph multi-sensor fusion architectures for UAV state estimation, and develops an error-state estimation framework in which RTK-GNSS, IMU, and LiDAR-inertial odometry are combined within a tightly coupled, factor-graph-augmented iterated Kalman filter. Building on verified prior work — LOAM-family and FAST-LIO/LIO-SAM lidar-inertial odometry, GNSS-aided factor-graph systems (GVINS, GLIO), and UAV-specific RTK accuracy studies — four concrete literature gaps are identified: scarce simultaneous RTK-GNSS/IMU/LiDAR UAV datasets, imbalanced velocity- versus position-accuracy evaluation, sparse embedded-hardware latency reporting, and limited characterization of GNSS-transition behaviour. A proposed real-time architecture, a five-scenario experimental methodology, and a quantitative evaluation framework are presented, together with a real-data case study — computed from a publicly verifiable, peer-reviewed RTK-GNSS dataset — that empirically grounds the position- and velocity-error characteristics discussed. Because no new UAV flight experiments were conducted, projected UAV outcomes are explicitly presented as an Expected Results framework rather than measured findings, and the manuscript's own methodology and novelty are critically self-assessed.
S. Saiki, Saadu Olayinka Isiaka, Agu Victor Emezie et al.· Global Journal of Engineerin...· 0 citations
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