Geometry-Aware Dual-LiDAR Pose Estimation of Power Lines for UAV Physical Interaction

Robust UAV Landing via Uncertainty-Aware Vision–LiDAR Sensor Fusion

Autonomous landings of uncrewed aerial vehicles (UAV) on moving ground vehicles remains a challenging issue, since the reliability of onboard sensors varies during flights. Camera vision measurements may be blurred by motion, partially obscure the target, and be distorted by changes in light. LiDAR height measurements are influenced by slope of the ground and vehicle attitude. The majority of the existing strategies combine these sensors with constant weights, restricting the reliability to varying situations. The uncertainty-aware adaptive vision-LiDAR fusion architecture proposed in this paper is used to track and land a UAV in a GPS-denied region. The primary innovation of this work is that sensor confidence is adjusted dynamically via an adaptive extended Kalman filter that uses online measurements quality and observability (as opposed to predetermined fusion parameters). The approach ensures that the relative state estimation is stable and accurate, by automatically eliminating the effect of unreliable sensor measurements. Simulations demonstrate improved landing precision, reduced failures, and increased robustness in comparison to traditional fixed-weight fusion, in high-speed motion and in degraded sensing environment.

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.

Multi-LiDAR SLAM and intelligent navigation for autonomous logistic robots in ROS2 environments

Traditional automated guided vehicles (AGVs) are restricted by their reliance on predefined paths, limiting adaptability in dynamic warehouse environments. While autonomous mobile robots (AMRs) overcome this limitation through on-board simultaneous localization and mapping (SLAM) and autonomous navigation, standard configurations often suffer from top mounted-sensor blind spots when loads are carried on the chassis. To address these coverage gaps, an indoor logistic AMR based on the robot operating system 2 (ROS2) was designed and evaluated. The platform was developed by combining a multi-LiDAR perception stack with low-cost industrial actuation and a lightweight, fleet-style user interface. Within the system architecture, data from two light detection and ranging (LiDAR) sensors were merged at the topic level into a single virtual scan for SLAM toolbox and Nav2. Additionally, actuation and wheel odometry were driven by an RS-485 Modbus-based brushless DC (BLDC) motor controller, while ultrasonic sensors for short-range safety, an inertial measurement unit (IMU) for orientation, and a network of microcontroller calling stations communicating via message queuing telemetry transport (MQTT) were integrated into the platform. Experimental validation demonstrated successful multi-LiDAR fusion, with the Modbus motor driver achieving a motion-control error of 0.36 % and a speed-retrieval error of 0.43 %. Furthermore, calling-station commands were reliably executed over MQTT, and a point-to-point navigational repeatability of 10.3 cm was achieved. These findings indicate that an integrated multi-LiDAR ROS2 AMR provides a highly practical solution for indoor logistics. Through the proposed sensor merger and calling-station handshake, two recurring vulnerabilities of standard ROS2 deployments—single-LiDAR coverage gaps and Nav2 goal-overwriting behavior—were successfully resolved.

Autonomous 3D Object Geolocation on UAVs Using Monocular Camera and Gimbaled Laser

Deployment-Ready UWB Localization for Industrial Ground Robots with Automatic Anchor Calibration and Terrain-Aware Fusion

Ultra-Wideband (UWB) ranging has become a viable option for industrial Autonomous Mobile Robot (AMR) localization due to improved accuracy and low cost. However, real-world deployments remain limited by two recurring challenges: calibrating static anchors can be time-consuming and error-prone, and integrating UWB with existing onboard sensors requires careful design to ensure robust and consistent pose estimation. Addressing these challenges, this paper presents an end-to-end pipeline that combines automatic anchor calibration with a generic multi-sensor estimator tailored to surface-bound vehicle motion. It targets existing AMR stacks in scenarios where robot pose priors are available for initialization. The calibration stage estimates anchor positions and range biases, while the localization stage fuses UWB with proprioceptive sensing in a bias-aware Extended Kalman Filter to improve consistency without extensive parameter tuning. Experiments on a commercial logistics AMR in a warehouse setting demonstrate accurate positioning indoors and across outdoor transitions, with improved consistency compared to an earlier estimator formulation. Evaluation on an independent forklift dataset further indicates transferability to other platforms. The method remains effective in test cases with limited line-of-sight and sparse anchor coverage. These results show that UWB localization can be deployed with substantially reduced manual effort while preserving the accuracy required for industrial AMRs. The collected warehouse dataset is made publicly available.

Implementation of a ROS-Based LiDAR Target Following System for Smart Cart

As an important branch of robotics, intelligent vehicles have become a major research focus for improving the flexibility, adaptability, and autonomy of material handling in modern manufacturing facilities. Benefiting from advances in electromagnetic sensing and real-time signal processing, LiDAR-based perception technologies provide reliable environmental information for autonomous navigation and target tracking. This paper investigates the development of a LiDAR target-following intelligent vehicle system based on the Robot Operating System (ROS), with particular emphasis on the design and implementation of node communication and data transmission mechanisms within the ROS framework. The system accomplishes target-following tasks through multiple functionally distinct yet collaboratively operating ROS nodes, including a LiDAR driver node, a target detection node, a tracking node, and an Extended Kalman Filter (EKF)-based pose estimation node. Experimental results demonstrate stable and reliable target-tracking performance across diverse environments. The proposed system provides both a theoretical basis and a practical engineering solution for intelligent vehicles in autonomous navigation, medical applications, and automated material transport, while offering useful insights into electromagnetic perception and real-time sensing systems.