Sky-GVIO: an enhanced GNSS/INS/Vision navigation with FCN-based sky-segmentation in urban canyon

• URL: http://arxiv.org/abs/2404.11070v2
• Date: Mon, 5 Aug 2024 05:13:06 GMT
• Title: Sky-GVIO: an enhanced GNSS/INS/Vision navigation with FCN-based sky-segmentation in urban canyon
• Authors: Jingrong Wang, Bo Xu, Ronghe Jin, Shoujian Zhang, Kefu Gao, Jingnan Liu,
• Abstract summary: In urban canyon environments, the vulnerability of a stand-alone sensor and non-line-of-sight (NLOS) caused by high buildings seriously affect positioning results. To address these challenges, a sky-view images segmentation algorithm based on Fully Convolutional Network (FCN) is proposed for NLOS detection. A novel NLOS detection and mitigation algorithm (named S-NDM) is extended to the tightly coupled Global Navigation Satellite Systems (GNSS), Inertial Measurement Units (IMU), and visual feature system called Sky-GVIO.
• Score: 3.870599032599567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate, continuous, and reliable positioning is a critical component of achieving autonomous driving. However, in complex urban canyon environments, the vulnerability of a stand-alone sensor and non-line-of-sight (NLOS) caused by high buildings, trees, and elevated structures seriously affect positioning results. To address these challenges, a sky-view images segmentation algorithm based on Fully Convolutional Network (FCN) is proposed for GNSS NLOS detection. Building upon this, a novel NLOS detection and mitigation algorithm (named S-NDM) is extended to the tightly coupled Global Navigation Satellite Systems (GNSS), Inertial Measurement Units (IMU), and visual feature system which is called Sky-GVIO, with the aim of achieving continuous and accurate positioning in urban canyon environments. Furthermore, the system harmonizes Single Point Positioning (SPP) with Real-Time Kinematic (RTK) methodologies to bolster its operational versatility and resilience. In urban canyon environments, the positioning performance of S-NDM algorithm proposed in this paper is evaluated under different tightly coupled SPP-related and RTK-related models. The results exhibit that Sky-GVIO system achieves meter-level accuracy under SPP mode and sub-decimeter precision with RTK, surpassing the performance of GNSS/INS/Vision frameworks devoid of S-NDM. Additionally, the sky-view image dataset, inclusive of training and evaluation subsets, has been made publicly accessible for scholarly exploration at https://github.com/whuwangjr/sky-view-images .

Related papers

• Real-time loosely coupled GNSS and IMU integration via Factor Graph Optimization [3.306326078788103]
  An important component of positioning, navigation, and timing (PNT) is the Global Navigation Satellite System (GNSS)<n>Modern research directions have pushed the performance of localization to new heights by fusing measurements with other sensory information.<n>We propose a loosely coupled architecture to integrate measurements using a Factor Graph Optimization (FGO) framework.
  arXiv  Detail & Related papers  (2026-03-03T21:59:55Z)
• OptiVote: Non-Coherent FSO Over-the-Air Majority Vote for Communication-Efficient Distributed Federated Learning in Space Data Centers [68.73273027298625]
  megaconstellations are driving the long-term vision of space data centers (SDCs)<n>AirComp is an in-network aggregation framework for learning free-space (FSO)<n>AirVote integrates sign gradient (SGD) with a majority-signposition modulation (PPM), where each satellite conveys local gradient by activating PPM time slots.<n>OptiVote mitigates phase-sensitive field superposition into phase-agnostic optical intensity combining.
  arXiv  Detail & Related papers  (2025-12-30T16:40:02Z)
• Aerial Vision-Language Navigation with a Unified Framework for Spatial, Temporal and Embodied Reasoning [5.517595398768408]
  We present a unified aerial VLN framework that operates solely on ego monocular RGB observations and natural language instructions.<n>This task holds promise for real-world applications such as low-altitude inspection, search-and-rescue, and autonomous aerial delivery.
  arXiv  Detail & Related papers  (2025-12-09T14:25:24Z)
• Stable Multi-Drone GNSS Tracking System for Marine Robots [7.911692711262891]
  We present a scalable multi-drone-based tracking system for surface and near-surface marine robots.<n>Our approach combines efficient visual detection, lightweight multi-object tracking, and a confidence-weighted Extended Kalman Filter (EKF) to provide stable estimation in real time.
  arXiv  Detail & Related papers  (2025-11-24T02:28:31Z)
• Green Learning for STAR-RIS mmWave Systems with Implicit CSI [53.03358325565645]
  Green learning (GL)-based precoding framework is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided millimeter-wave (mmWave) broadcasting systems.<n>Motivated by the emphasis on environmental sustainability in future 6G networks, this work adopts a transmission framework for scenarios where multiple users share identical information, improving spectral efficiency and reducing redundant transmissions and power consumption.
  arXiv  Detail & Related papers  (2025-09-08T15:56:06Z)
• AI-Driven Collaborative Satellite Object Detection for Space Sustainability [29.817805350971366]
  The growing density of satellites in low-Earth orbit (LEO) presents serious challenges to space sustainability.<n>Traditional ground-based tracking systems are constrained by latency and coverage limitations.<n>We propose a novel satellite clustering framework that enables the collaborative execution of deep learning (DL)-based space object detection tasks across multiple satellites.
  arXiv  Detail & Related papers  (2025-08-01T16:31:55Z)
• 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)
• Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark [15.405137983083875]
  Aerial-ground cooperation offers a promising solution by integrating UAVs' aerial views with ground vehicles' local observations. This paper presents a comprehensive solution for aerial-ground cooperative 3D perception through three key contributions.
  arXiv  Detail & Related papers  (2025-03-10T07:00:07Z)
• Exploring Fully Convolutional Networks for the Segmentation of Hyperspectral Imaging Applied to Advanced Driver Assistance Systems [1.8874331450711404]
  We explore the use of hyperspectral imaging (HSI) in Advanced Driver Assistance Systems (ADAS) This paper describes some experimental results of the application of fully convolutional networks (FCN) to the image segmentation of HSI for ADAS applications. We use the HSI-Drive v1.1 dataset, which provides a set of labelled images recorded in real driving conditions with a small-size snapshot NIR-HSI camera.
  arXiv  Detail & Related papers  (2024-12-05T08:58:25Z)
• Bridging Domain Gap for Flight-Ready Spaceborne Vision [4.14360329494344]
  This work presents Spacecraft Pose Network v3 (SPNv3), a Neural Network (NN) for monocular pose estimation of a known, non-cooperative target spacecraft. SPNv3 is designed and trained to be computationally efficient while providing robustness to spaceborne images that have not been observed during offline training and validation on the ground. Experiments demonstrate that the final SPNv3 can achieve state-of-the-art pose accuracy on hardware-in-the-loop images from a robotic testbed while having trained exclusively on computer-generated synthetic images.
  arXiv  Detail & Related papers  (2024-09-18T02:56:50Z)
• Global 4D Ionospheric STEC Prediction based on DeepONet for GNSS Rays [14.934920001287962]
  We propose a high-precision STEC model named DeepONet-STEC, which learns nonlinear operators to predict the 4D temporal-spatial integrated parameter for specified ground station - satellite ray path globally. As a demonstration, we validate the performance of the model based on observation data for global and US-CORS regimes under ionospheric quiet and storm conditions. The DeepONet-STEC model results show that the three-day 72 hour prediction in quiet periods could achieve high accuracy using observation data by the Precise Point Positioning (PPP) with temporal resolution 30s.
  arXiv  Detail & Related papers  (2024-03-12T10:51:38Z)
• Extending RAIM with a Gaussian Mixture of Opportunistic Information [1.9688858888666714]
  Original receiver autonomous integrity monitoring (RAIM) was not designed for securing. We extend RAIM by incorporating all opportunistic information, i.e., measurements from terrestrial infrastructures and onboard sensors. The objective is to assess the likelihood of spoofing by analyzing locations derived from extended RAIM solutions.
  arXiv  Detail & Related papers  (2024-02-05T19:03:18Z)
• 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)
• FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks [18.213174641216884]
  A large number of Low Earth Orbit (LEO) satellites have been launched and deployed successfully in space by commercial companies, such as SpaceX. Due to multimodal sensors equipped by the LEO satellites, they serve not only for communication but also for various machine learning applications, such as space modulation recognition, remote sensing image classification, etc. We propose FedSN as a general FL framework to tackle the above challenges, and fully explore data diversity on LEO satellites.
  arXiv  Detail & Related papers  (2023-11-02T14:47:06Z)
• Learning-based NLOS Detection and Uncertainty Prediction of GNSS Observations with Transformer-Enhanced LSTM Network [2.798138034569478]
  This work proposes a deeplearning-based method to detect NLOS and predict errors by analyzing pseudo-temporal modeling problem. We use datasets from Hong Kong and Aachen to train and evaluate the proposed network. We show that the proposed method avoids trajectory divergence in real-world vehicle localization by classifying and excluding NLOS observations.
  arXiv  Detail & Related papers  (2023-09-01T14:17:02Z)
• 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)
• Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting [70.66710698485745]
  We propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting. AHSTN exploits the spatial hierarchy and modeling multi-scale spatial correlations. Experiments on two real-world datasets show that AHSTN achieves better performance over several strong baselines.
  arXiv  Detail & Related papers  (2023-06-15T14:50:27Z)
• Automated classification of pre-defined movement patterns: A comparison between GNSS and UWB technology [55.41644538483948]
  Real-time location systems (RTLS) allow for collecting data from human movement patterns. The current study aims to design and evaluate an automated framework to classify human movement patterns in small areas.
  arXiv  Detail & Related papers  (2023-03-10T14:46:42Z)
• 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)
• Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms [53.38353133198842]
  Air quality significantly affects human health, it is increasingly important to accurately and timely predict the Air Quality Index (AQI) This paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. For ground sensing systems, we propose a Graph Convolutional neural network-based Long Short-Term Memory (GC-LSTM) model to achieve accurate, real-time and future AQI inference.
  arXiv  Detail & Related papers  (2020-07-23T13:32:47Z)

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.