Robust Visual SLAM for UAV Navigation in GPS-Denied and Degraded Environments: A Multi-Paradigm Evaluation and Deployment Study

• URL: http://arxiv.org/abs/2605.03678v1
• Date: Tue, 05 May 2026 12:16:25 GMT
• Title: Robust Visual SLAM for UAV Navigation in GPS-Denied and Degraded Environments: A Multi-Paradigm Evaluation and Deployment Study
• Abstract summary: This paper presents a systematic comparative evaluation of five V-SLAM systems.<n>Experiments are conducted on curated sequences from four public benchmarks.<n>Mast3R achieves the lowest degraded ATE (0.027 m) and DUSt3R the highest tracking success (96.5%)
• Score: 3.40000540644179
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reliable localization in GPS-denied, visually degraded environments is critical for autonomous UAV opera- tions. This paper presents a systematic comparative evaluation of five V-SLAM systems ORB-SLAM3, DPVO, DROID-SLAM, DUSt3R, and MASt3R spanning classical, deep learning, recurrent, and Vision Transformer (ViT) paradigms. Experiments are conducted on curated sequences from four public benchmarks (TUM RGB-D, EuRoC MAV, UMA-VI, SubT-MRS) and a custom monocular indoor dataset under five controlled degradation conditions (normal, low light, dust haze, motion blur, and combined), with sub-millimeter Vicon ground truth. Results show that ORB-SLAM3 fails critically under severe degradation (62.4% overall TSR; 0% under dense haze), while learning-based methods remain robust: MASt3R achieves the lowest degraded ATE (0.027 m) and DUSt3R the highest tracking success (96.5%). DPVO offers the best efficiency robustness trade-off (18.6 FPS, 3.1 GB GPU memory, 86.1% TSR), making it the preferred choice for memory-constrained embedded platforms. Embedded deployment analysis across NVIDIA Jetson platforms provides actionable guidelines for SLAM selection under SWaP-constrained UAV scenarios.

Related papers

• Drone-Assisted UAV-UGV Collaboration for Autonomous Navigation in Snow-Covered Terrain [0.0]
  This paper presents a collaborative UAV-UGV navigation framework for high-altitude, snow-covered terrain.<n>We introduce a custom efficient U-Net architecture that falls under the computational constraints for real-time road segmentation.<n>For UAV localization, we implement an Extended Kalman Filter (EKF) fusing onboard GPS and IMU data, achieving a maximum observed positional error of +0.5 meters.
  arXiv  Detail & Related papers  (2026-08-07T22:41:19Z)
• LocalNav: Distilling Frontier VLMs and Embodied RL for On-Device Object Goal Navigation [53.88286427894783]
  Vision Language Models (VLMs) have emerged in the robotic domain as a powerful tool that enables environmental perception with language context.<n>Yet, their computational footprint typically confines them to cloud execution, hindering low-latency inference with local deployment on resource-constrained robots.<n>We present a distillation strategy that transfers complex spatial-semantic reasoning from large frontier models into a lightweight, 4B- parameter local VLM for edge execution on embedded GPU devices.
  arXiv  Detail & Related papers  (2026-06-26T09:11:59Z)
• Instance-level Visual Active Tracking with Occlusion-Aware Planning [61.982298426203165]
  Visual Active Tracking (VAT) aims to control cameras to follow a target in 3D space.<n>VAT faces two key bottlenecks in real-world deployment: confusion from visually similar distractors and severe failure under occlusions.<n>We propose OA-VAT, a unified pipeline with three complementary modules.
  arXiv  Detail & Related papers  (2026-04-23T09:11:50Z)
• Lightweight 3D LiDAR-Based UAV Tracking: An Adaptive Extended Kalman Filtering Approach [2.3646560222366695]
  This paper presents a lightweight LiDAR-based UAV tracking system incorporating an Adaptive Extended Kalman Filter (AEKF) framework.<n>Our approach effectively addresses the challenges posed by sparse, noisy, and nonuniform point cloud data generated by non-repetitive scanning 3D LiDARs.
  arXiv  Detail & Related papers  (2026-03-10T15:19:34Z)
• Fly, Track, Land: Infrastructure-less Magnetic Localization for Heterogeneous UAV-UGV Teaming [5.61837936726601]
  We present a complete infrastructure-less magneto-inductive (MI) localization system enabling a lightweight UAV to autonomously hover, track, and land with centimeter precision.<n>This work advances the vision of heterogeneous robot collaboration, where ultra-lightweight flying robots serve as mobile perception agents for ground-based Unmanned Ground Vehicles.
  arXiv  Detail & Related papers  (2026-03-09T20:52:51Z)
• UAV-MM3D: A Large-Scale Synthetic Benchmark for 3D Perception of Unmanned Aerial Vehicles with Multi-Modal Data [47.317955428393134]
  We introduce UAV-MM3D, a high-fidelity multimodal synthetic dataset for low-altitude UAV perception and motion understanding.<n>It comprises 400K synchronized frames across diverse scenes (urban areas, suburbs, forests, coastal regions) and weather conditions.<n>Each frame provides 2D/3D bounding boxes, 6-DoF poses, and instance-level annotations, enabling core tasks related to UAVs such as 3D detection, pose estimation, target tracking, and short-term trajectory forecasting.
  arXiv  Detail & Related papers  (2025-11-27T12:30:28Z)
• 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)
• Real-Time Dynamic Scale-Aware Fusion Detection Network: Take Road Damage Detection as an example [3.334973867478745]
  Road Damage Detection (RDD) is important for daily maintenance and safety in cities. Current UAV-based RDD research is still faces many challenges. We design a multi-scale, adaptive road damage detection model with the ability to automatically remove background interference.
  arXiv  Detail & Related papers  (2024-09-04T09:03:47Z)
• ASV Station Keeping under Wind Disturbances using Neural Network Simulation Error Minimization Model Predictive Control [5.039813366558306]
  This work proposes a Model Predictive Controller using Neural Network Simulation Error Minimization (NNSEM-MPC) to accurately predict the dynamics of the ASV under wind disturbances. In terms of execution speed, the proposed NNSEM-MPC is at least 36% faster than the rest of MPC controllers.
  arXiv  Detail & Related papers  (2023-10-11T20:55:13Z)
• Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights [50.52704854147297]
  We present a new vision transformer (ViT) model optimized with a classification (discrete) and a continuous loss function. This model achieves better accuracy than previously used convolutional based approaches (ConvNets) optimized with only a continuous loss function.
  arXiv  Detail & Related papers  (2023-04-22T22:39:03Z)
• MetaBEV: Solving Sensor Failures for BEV Detection and Map Segmentation [104.12419434114365]
  In real-world applications, sensor corruptions and failures lead to inferior performances. We propose a robust framework, called MetaBEV, to address extreme real-world environments. We show MetaBEV outperforms prior arts by a large margin on both full and corrupted modalities.
  arXiv  Detail & Related papers  (2023-04-19T16:37:17Z)

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.