Development and Testing for Perception Based Autonomous Landing of a Long-Range QuadPlane

• URL: http://arxiv.org/abs/2512.09343v2
• Date: Thu, 11 Dec 2025 04:44:06 GMT
• Title: Development and Testing for Perception Based Autonomous Landing of a Long-Range QuadPlane
• Authors: Ashik E Rasul, Humaira Tasnim, Ji Yu Kim, Young Hyun Lim, Scott Schmitz, Bruce W. Jo, Hyung-Jin Yoon,
• Abstract summary: This work presents a lightweight QuadPlane system for efficient vision-based autonomous landing and visual-inertial odometry.<n>It describes the hardware platform, sensor configuration, and embedded computing architecture designed to meet demanding real-time, physical constraints.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: QuadPlanes combine the range efficiency of fixed-wing aircraft with the maneuverability of multi-rotor platforms for long-range autonomous missions. In GPS-denied or cluttered urban environments, perception-based landing is vital for reliable operation. Unlike structured landing zones, real-world sites are unstructured and highly variable, requiring strong generalization capabilities from the perception system. Deep neural networks (DNNs) provide a scalable solution for learning landing site features across diverse visual and environmental conditions. While perception-driven landing has been shown in simulation, real-world deployment introduces significant challenges. Payload and volume constraints limit high-performance edge AI devices like the NVIDIA Jetson Orin Nano, which are crucial for real-time detection and control. Accurate pose estimation during descent is necessary, especially in the absence of GPS, and relies on dependable visual-inertial odometry. Achieving this with limited edge AI resources requires careful optimization of the entire deployment framework. The flight characteristics of large QuadPlanes further complicate the problem. These aircraft exhibit high inertia, reduced thrust vectoring, and slow response times further complicate stable landing maneuvers. This work presents a lightweight QuadPlane system for efficient vision-based autonomous landing and visual-inertial odometry, specifically developed for long-range QuadPlane operations such as aerial monitoring. It describes the hardware platform, sensor configuration, and embedded computing architecture designed to meet demanding real-time, physical constraints. This establishes a foundation for deploying autonomous landing in dynamic, unstructured, GPS-denied environments.

Related papers

• Expert Switching for Robust AAV Landing: A Dual-Detector Framework in Simulation [0.0]
  Two YOLOv8 experts are trained on scale-specialized versions of the HelipadCat dataset.<n>During inference, both experts operate in parallel, and a geometric gating mechanism selects the expert whose prediction is most consistent with the AAV's viewpoint.<n>Results show substantial improvements in alignment, landing accuracy, and overall robustness compared to single-detector baselines.
  arXiv  Detail & Related papers  (2025-12-16T03:41:59Z)
• Optimizing Earth-Moon Transfer and Cislunar Navigation: Integrating Low-Energy Trajectories, AI Techniques and GNSS-R Technologies [0.0]
  cislunar activities, including lunar landings, require cost-efficient trajectory design and reliable integration of navigation and remote sensing.<n>Traditional Earth-Moon transfers suffer from rigid launch windows and high propellant demands, while Earth-based systems provide little to no coverage beyond geostationary orbit.<n>This review compares four major transfer strategies by evaluating velocity requirements, flight durations, and fuel efficiency, and by identifying their suitability for both crewed and robotic missions.
  arXiv  Detail & Related papers  (2025-11-05T04:41:43Z)
• Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach [62.11847362756054]
  Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN)<n>We propose a digital twin (DT)-assisted training and deployment framework.<n>In this framework, the UAV transmits integrated sensing and communication signals to provide communication services to ground users, while simultaneously collecting echoes that are uploaded to the DT server to progressively construct virtual environments (VEs)<n>These VEs accelerate model training and are continuously updated with real-time UAV sensing data during deployment, supporting decision-making and enhancing flight safety.
  arXiv  Detail & Related papers  (2025-10-28T10:05:53Z)
• NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments [56.35569661650558]
  We introduce NOVA, a fully onboard, object-centric framework that enables robust target tracking and collision-aware navigation.<n>Rather than constructing a global map, NOVA formulates perception, estimation, and control entirely in the target's reference frame.<n>We validate NOVA across challenging real-world scenarios, including urban mazes, forest trails, and repeated transitions through buildings with intermittent GPS loss.
  arXiv  Detail & Related papers  (2025-06-23T14:28:30Z)
• SLAM-Based Navigation and Fault Resilience in a Surveillance Quadcopter with Embedded Vision Systems [0.6138671548064356]
  We present an autonomous aerial surveillance platform, Veg, designed as a fault-tolerant quadcopter system.<n>It integrates visual SLAM for GPS-independent navigation, advanced control architecture for dynamic stability, and embedded vision modules for real-time object and face recognition.
  arXiv  Detail & Related papers  (2025-04-18T17:10:26Z)
• Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios [66.05091704671503]
  We present a novel angle navigation paradigm to deal with flight deviation in point-to-point navigation tasks. We also propose a model that includes the Adaptive Feature Enhance Module, Cross-knowledge Attention-guided Module and Robust Task-oriented Head Module.
  arXiv  Detail & Related papers  (2024-02-04T08:41:20Z)
• Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting [0.0]
  We present an approach for mapping of satellites on orbit based on 3D Gaussian Splatting. We demonstrate model training and 3D rendering performance on a hardware-in-the-loop satellite mock-up. Our model is shown to be capable of training on-board and rendering higher quality novel views of an unknown satellite nearly 2 orders of magnitude faster than previous NeRF-based algorithms.
  arXiv  Detail & Related papers  (2024-01-05T00:49:56Z)
• VPAIR -- Aerial Visual Place Recognition and Localization in Large-scale Outdoor Environments [49.82314641876602]
  We present a new dataset named VPAIR. The dataset was recorded on board a light aircraft flying at an altitude of more than 300 meters above ground. The dataset covers a more than one hundred kilometers long trajectory over various types of challenging landscapes.
  arXiv  Detail & Related papers  (2022-05-23T18:50:08Z)
• Deep Learning for Real Time Satellite Pose Estimation on Low Power Edge TPU [58.720142291102135]
  In this paper we propose a pose estimation software exploiting neural network architectures. We show how low power machine learning accelerators could enable Artificial Intelligence exploitation in space.
  arXiv  Detail & Related papers  (2022-04-07T08:53:18Z)
• Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy [48.51396198176273]
  We propose an integrated system that can perform large-scale autonomous flights and real-time semantic mapping in challenging under-canopy environments. We detect and model tree trunks and ground planes from LiDAR data, which are associated across scans and used to constrain robot poses as well as tree trunk models. A drift-compensation mechanism is designed to minimize the odometry drift using semantic SLAM outputs in real time, while maintaining planner optimality and controller stability.
  arXiv  Detail & Related papers  (2021-09-14T07:24:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.