Visual Marker Search for Autonomous Drone Landing in Diverse Urban Environments

• URL: http://arxiv.org/abs/2601.11078v1
• Date: Fri, 16 Jan 2026 08:24:23 GMT
• Title: Visual Marker Search for Autonomous Drone Landing in Diverse Urban Environments
• Abstract summary: Marker-based landing is widely used in drone delivery and return-to-base systems for its simplicity and reliability.<n>We present a simulation-based evaluation suite on the AirSim platform with systematically varied urban layouts, lighting, and weather to replicate realistic operational diversity.<n>Results underscore the need to evaluate marker-based autonomous landing under diverse, sensor-relevant conditions to guide the development of reliable aerial navigation systems.
• Score: 16.158903922376137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Marker-based landing is widely used in drone delivery and return-to-base systems for its simplicity and reliability. However, most approaches assume idealized landing site visibility and sensor performance, limiting robustness in complex urban settings. We present a simulation-based evaluation suite on the AirSim platform with systematically varied urban layouts, lighting, and weather to replicate realistic operational diversity. Using onboard camera sensors (RGB for marker detection and depth for obstacle avoidance), we benchmark two heuristic coverage patterns and a reinforcement learning-based agent, analyzing how exploration strategy and scene complexity affect success rate, path efficiency, and robustness. Results underscore the need to evaluate marker-based autonomous landing under diverse, sensor-relevant conditions to guide the development of reliable aerial navigation systems.

Related papers

• DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction [54.94692733670454]
  DESCENT is a transformer-based architecture designed to handle heterogeneous dynamics and strict topological constraints.<n>Our approach features a Potential Reachable Set (PRS) context sampling mechanism that adaptively collects airfield environment context.<n>Our evaluations on the Amelia-10 benchmark demonstrate significant performance improvements over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-08-26T16:49:50Z)
• TRISTAR: Triple-Signal Stair Recognition and Vision-Only Indoor Navigation for Search-and-Rescue Micro-UAVs [0.0]
  This paper presents a complete autonomous indoor navigation framework for low-cost unmanned aerial vehicles based exclusively on monocular vision.<n>The framework consists of two independent behaviors: (i) corridor exploration with automatic door detection, room entry, OCR-based room identification, and victim inspection; and (ii) autonomous stair ascent based on TRISTAR.
  arXiv  Detail & Related papers  (2026-07-04T11:06:00Z)
• The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy [13.105188128582567]
  We introduce and open-source the Unified Autonomy Stack, a system-level solution that enables resilient autonomy across aerial and ground robot morphologies.<n>The architecture centers on three modules -- multi-modal perception, multi-behavior planning, and multi-layered safe navigation.<n>The stack has been field-tested and validated on both aerial (rotorcraft) and ground (legged) robots.
  arXiv  Detail & Related papers  (2026-05-12T20:39:35Z)
• Perception-Aware Autonomous Exploration in Feature-Limited Environments [9.046854275196605]
  We propose a hierarchical perception-aware exploration framework for a stereo-equipped unmanned aerial vehicle (UAV)<n>Our approach associates each candidate frontier with an expected feature quality using a global feature map, and prioritises visually informative subgoals.<n>Compared to baselines that ignore feature quality and/or do not optimise continuous yaw, our method maintains more reliable feature tracking, reduces odometry drift, and on average 30% higher coverage before the odometry error exceeds specified thresholds.
  arXiv  Detail & Related papers  (2026-03-16T17:55:56Z)
• Development and Testing for Perception Based Autonomous Landing of a Long-Range QuadPlane [0.0]
  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.
  arXiv  Detail & Related papers  (2025-12-10T06:02:25Z)
• Predictive Uncertainty for Runtime Assurance of a Real-Time Computer Vision-Based Landing System [32.06417142452719]
  We present a vision-based pipeline for aircraft pose estimation from runway images.<n>Our approach features three key innovations: (i) an efficient, flexible neural architecture based on a spatial Soft Argmax operator for probabilistic keypoint regression, supporting diverse vision backbones with real-time inference; (ii) a principled loss function producing predictive uncertainties, which are evaluated via sharpness and calibration metrics; and (iii) an adaptation of Residual-based Receiver Autonomous Integrity Monitoring (RAIM) enabling runtime detection and rejection of faulty model outputs.
  arXiv  Detail & Related papers  (2025-08-13T11:56:22Z)
• 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)
• Reinforcement Learning-Based Monocular Vision Approach for Autonomous UAV Landing [0.0]
  This paper introduces an innovative approach for the autonomous landing of Unmanned Aerial Vehicles (UAVs) using only a front-facing monocular camera.<n> Drawing on the inherent human estimating process, the proposed method reframes the landing task as an optimization problem.
  arXiv  Detail & Related papers  (2025-05-11T12:23:37Z)
• Multi-modal Multi-platform Person Re-Identification: Benchmark and Method [58.59888754340054]
  MP-ReID is a novel dataset designed specifically for multi-modality and multi-platform ReID.<n>This benchmark compiles data from 1,930 identities across diverse modalities, including RGB, infrared, and thermal imaging.<n>We introduce Uni-Prompt ReID, a framework with specific-designed prompts, tailored for cross-modality and cross-platform scenarios.
  arXiv  Detail & Related papers  (2025-03-21T12:27:49Z)
• Open-World Drone Active Tracking with Goal-Centered Rewards [62.21394499788672]
  Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations.<n>We propose DAT, the first open-world drone active air-to-ground tracking benchmark.<n>We also propose GC-VAT, which aims to improve the performance of drone tracking targets in complex scenarios.
  arXiv  Detail & Related papers  (2024-12-01T09:37:46Z)
• RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving [67.09546127265034]
  Road surface reconstruction helps to enhance the analysis and prediction of vehicle responses for motion planning and control systems. We introduce the Road Surface Reconstruction dataset, a real-world, high-resolution, and high-precision dataset collected with a specialized platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of stereo images, original point clouds, and ground-truth depth/disparity maps.
  arXiv  Detail & Related papers  (2023-10-03T17:59:32Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• Visual-based Safe Landing for UAVs in Populated Areas: Real-time Validation in Virtual Environments [0.0]
  We propose a framework for real-time safe and thorough evaluation of vision-based autonomous landing in populated scenarios. We propose to use the Unreal graphics engine coupled with the AirSim plugin for drone's simulation. We study two different criteria for selecting the "best" SLZ, and evaluate them during autonomous landing of a virtual drone in different scenarios.
  arXiv  Detail & Related papers  (2022-03-25T17:22:24Z)
• Benchmarking high-fidelity pedestrian tracking systems for research, real-time monitoring and crowd control [55.41644538483948]
  High-fidelity pedestrian tracking in real-life conditions has been an important tool in fundamental crowd dynamics research. As this technology advances, it is becoming increasingly useful also in society. To successfully employ pedestrian tracking techniques in research and technology, it is crucial to validate and benchmark them for accuracy. We present and discuss a benchmark suite, towards an open standard in the community, for privacy-respectful pedestrian tracking techniques.
  arXiv  Detail & Related papers  (2021-08-26T11:45:26Z)
• Towards bio-inspired unsupervised representation learning for indoor aerial navigation [4.26712082692017]
  This research displays a biologically inspired deep-learning algorithm for simultaneous localization and mapping (SLAM) and its application in a drone navigation system. We propose an unsupervised representation learning method that yields low-dimensional latent state descriptors, that mitigates the sensitivity to perceptual aliasing, and works on power-efficient, embedded hardware. The designed algorithm is evaluated on a dataset collected in an indoor warehouse environment, and initial results show the feasibility for robust indoor aerial navigation.
  arXiv  Detail & Related papers  (2021-06-17T08:42:38Z)
• Occupancy Anticipation for Efficient Exploration and Navigation [97.17517060585875]
  We propose occupancy anticipation, where the agent uses its egocentric RGB-D observations to infer the occupancy state beyond the visible regions. By exploiting context in both the egocentric views and top-down maps our model successfully anticipates a broader map of the environment. Our approach is the winning entry in the 2020 Habitat PointNav Challenge.
  arXiv  Detail & Related papers  (2020-08-21T03:16:51Z)

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.