A Visual-inertial Localization Algorithm using Opportunistic Visual Beacons and Dead-Reckoning for GNSS-Denied Large-scale Applications

• URL: http://arxiv.org/abs/2411.19845v2
• Date: Sat, 14 Dec 2024 07:36:15 GMT
• Title: A Visual-inertial Localization Algorithm using Opportunistic Visual Beacons and Dead-Reckoning for GNSS-Denied Large-scale Applications
• Authors: Liqiang Zhang, Ye Tian, Dongyan Wei,
• Abstract summary: Augmented reality (AR) allows pedestrians to acquire real-time visual information. We propose a low-cost visual-inertial positioning solution.
• Score: 1.6599082439305994
• License:
• Abstract: With the development of smart cities, the demand for continuous pedestrian navigation in large-scale urban environments has significantly increased. While global navigation satellite systems (GNSS) provide low-cost and reliable positioning services, they are often hindered in complex urban canyon environments. Thus, exploring opportunistic signals for positioning in urban areas has become a key solution. Augmented reality (AR) allows pedestrians to acquire real-time visual information. Accordingly, we propose a low-cost visual-inertial positioning solution. This method comprises a lightweight multi-scale group convolution (MSGC)-based visual place recognition (VPR) neural network, a pedestrian dead reckoning (PDR) algorithm, and a visual/inertial fusion approach based on a Kalman filter with gross error suppression. The VPR serves as a conditional observation to the Kalman filter, effectively correcting the errors accumulated through the PDR method. This enables the entire algorithm to ensure the reliability of long-term positioning in GNSS-denied areas. Extensive experimental results demonstrate that our method maintains stable positioning during large-scale movements. Compared to the lightweight MobileNetV3-based VPR method, our proposed VPR solution improves Recall@1 by at least 3\% on two public datasets while reducing the number of parameters by 63.37\%. It also achieves performance that is comparable to the VGG16-based method. The VPR-PDR algorithm improves localization accuracy by more than 40\% compared to the original PDR.

Related papers

• FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentation [52.89847760590189]
  3D scene understanding is a critical yet challenging task in autonomous driving. Recent methods leverage the range-view representation to improve processing efficiency. We re-design the workflow for range-view-based LiDAR semantic segmentation.
  arXiv  Detail & Related papers  (2025-02-13T12:39:26Z)
• POPoS: Improving Efficient and Robust Facial Landmark Detection with Parallel Optimal Position Search [34.50794776762681]
  This paper introduces Parallel Optimal Position Search (POPoS), a high-precision encoding-decoding framework. POPoS employs three key contributions: Pseudo-range multilateration is utilized to correct heatmap errors, improving landmark localization accuracy. A single-step parallel computation algorithm is introduced, boosting computational efficiency and reducing processing time.
  arXiv  Detail & Related papers  (2024-10-12T16:28:40Z)
• GSPR: Multimodal Place Recognition Using 3D Gaussian Splatting for Autonomous Driving [9.023864430027333]
  multimodal place recognition has gained increasing attention due to their ability to overcome weaknesses of uni sensor systems. We propose a 3D Gaussian-based multimodal place recognition neural network dubbed GSPR.
  arXiv  Detail & Related papers  (2024-10-01T00:43:45Z)
• Local positional graphs and attentive local features for a data and runtime-efficient hierarchical place recognition pipeline [11.099588962062937]
  This paper proposes a runtime and data-efficient hierarchical VPR pipeline that extends existing approaches and presents novel ideas. First, we propose Local Positional Graphs (LPG), a training-free and runtime-efficient approach to encode spatial context information of local image features. Second, we present Attentive Local SPED (ATLAS), an extension of our previous local features approach with an attention module. Third, we present a hierarchical pipeline that exploits hyperdimensional computing to use the same local features as holistic HDC-descriptors for fast candidate selection and for candidate reranking.
  arXiv  Detail & Related papers  (2024-03-15T13:26:39Z)
• Deep Homography Estimation for Visual Place Recognition [49.235432979736395]
  We propose a transformer-based deep homography estimation (DHE) network. It takes the dense feature map extracted by a backbone network as input and fits homography for fast and learnable geometric verification. Experiments on benchmark datasets show that our method can outperform several state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-25T13:22:17Z)
• ReLoc-PDR: Visual Relocalization Enhanced Pedestrian Dead Reckoning via Graph Optimization [4.188058836787458]
  This work proposes ReLoc-PDR, a fusion framework combining pedestrian dead reckoning and visual relocalization. A graph optimization-based fusion mechanism with the Tukey kernel effectively corrects cumulative errors and mitigates the impact of abnormal visual observations. Real-world experiments demonstrate that our ReLoc-PDR surpasses representative methods in accuracy and robustness.
  arXiv  Detail & Related papers  (2023-09-04T14:54:47Z)
• Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning [58.79085525115987]
  Local methods are one of the promising approaches to reduce communication time. We show that the communication complexity is better than non-local methods when the local datasets is smaller than the smoothness local loss.
  arXiv  Detail & Related papers  (2022-02-12T15:12:17Z)
• Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach [59.17191114000146]
  LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task. We show that LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps.
  arXiv  Detail & Related papers  (2021-06-23T17:27:04Z)
• STA-VPR: Spatio-temporal Alignment for Visual Place Recognition [17.212503755962757]
  We propose an adaptive dynamic time warping algorithm to align local features from the spatial domain while measuring the distance between two images. A local matching DTW algorithm is applied to perform image sequence matching based on temporal alignment. The results show that the proposed method significantly improves the CNN-based methods.
  arXiv  Detail & Related papers  (2021-03-25T03:27:42Z)
• Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks [151.27147513363502]
  This paper studies the problem of the trajectory design for a group of energyconstrained drones operating in dynamic wireless network environments. A value based reinforcement learning (VDRL) solution and a metatraining mechanism is proposed.
  arXiv  Detail & Related papers  (2020-12-06T01:30:12Z)
• Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution [82.1739023587565]
  We propose a novel spectral-spatial residual network for hyperspectral image super-resolution (SSRNet) Our method can effectively explore spatial-spectral information by using 3D convolution instead of 2D convolution, which enables the network to better extract potential information. In each unit, we employ spatial and temporal separable 3D convolution to extract spatial and spectral information, which not only reduces unaffordable memory usage and high computational cost, but also makes the network easier to train.
  arXiv  Detail & Related papers  (2020-01-14T03:34:55Z)

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.