Robust Subpixel Localization of Diagonal Markers in Large-Scale Navigation via Multi-Layer Screening and Adaptive Matching

• URL: http://arxiv.org/abs/2601.08161v1
• Date: Tue, 13 Jan 2026 02:51:31 GMT
• Title: Robust Subpixel Localization of Diagonal Markers in Large-Scale Navigation via Multi-Layer Screening and Adaptive Matching
• Authors: Jing Tao, Banglei Guan, Yang Shang, Shunkun Liang, Qifeng Yu,
• Abstract summary: This paper proposes a robust, high-precision positioning methodology to address localization failures in large-scale flight navigation.<n>The proposed methodology employs a three-tiered framework incorporating multi-layer corner screening and adaptive template matching.<n> Experimental results demonstrate the method's effectiveness in extracting and localizing diagonal markers in complex, large-scale environments.
• Score: 18.710429100680006
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper proposes a robust, high-precision positioning methodology to address localization failures arising from complex background interference in large-scale flight navigation and the computational inefficiency inherent in conventional sliding window matching techniques. The proposed methodology employs a three-tiered framework incorporating multi-layer corner screening and adaptive template matching. Firstly, dimensionality is reduced through illumination equalization and structural information extraction. A coarse-to-fine candidate selection strategy minimizes sliding window computational costs, enabling rapid estimation of the marker's position. Finally, adaptive templates are generated for candidate points, achieving subpixel precision through improved template matching with correlation coefficient extremum fitting. Experimental results demonstrate the method's effectiveness in extracting and localizing diagonal markers in complex, large-scale environments, making it ideal for field-of-view measurement in navigation tasks.

Related papers

• Tail-Aware Post-Training Quantization for 3D Geometry Models [58.79500829118265]
  Post-Training Quantization (PTQ) enables efficient inference without retraining.<n>PTQ fails to transfer effectively to 3D models due to intricate feature distributions and prohibitive calibration overhead.<n>We propose TAPTQ, a Tail-Aware Post-Training Quantization pipeline for 3D geometric learning.
  arXiv  Detail & Related papers  (2026-02-02T07:21:15Z)
• CogniEdit: Dense Gradient Flow Optimization for Fine-Grained Image Editing [88.9067184995168]
  We propose a unified framework CogniEdit, combining multi-modal reasoning with dense reward optimization.<n>Our method achieves state-of-the-art performance in balancing fine-grained instruction following with visual quality and editability preservation.
  arXiv  Detail & Related papers  (2025-12-15T12:36:50Z)
• An Efficient Deep Template Matching and In-Plane Pose Estimation Method via Template-Aware Dynamic Convolution [5.201850165450502]
  In industrial inspection and component alignment tasks, template matching requires efficient estimation of a target's position and geometric state.<n>We propose a lightweight end-to-end framework that reformulates template matching as joint localization and geometric regression.<n>Experiments show our 3.07M model achieves high precision and 14ms inference under compound transformations.
  arXiv  Detail & Related papers  (2025-10-02T05:05:43Z)
• Adaptive Guidance Semantically Enhanced via Multimodal LLM for Edge-Cloud Object Detection [9.198326035948613]
  This paper proposes an adaptive guidance-based semantic enhancement edge-cloud collaborative object detection method.<n>It can reduce latency by over 79% and computational cost by 70% in low-light and highly occluded scenes.
  arXiv  Detail & Related papers  (2025-09-24T08:25:37Z)
• Geometry-Aware Feature Matching for Large-Scale Structure from Motion [10.645087195983201]
  We introduce geometry cues in addition to color cues to fill gaps when there is less overlap in large-scale scenarios.<n>Our approach ensures that the denser correspondences from detector-free methods are geometrically consistent and more accurate.<n>It outperforms state-of-the-art feature matching methods on benchmark datasets.
  arXiv  Detail & Related papers  (2024-09-03T21:41:35Z)
• SPARE: Symmetrized Point-to-Plane Distance for Robust Non-Rigid 3D Registration [77.13381026159111]
  We propose SPARE, a novel formulation that utilizes a symmetrized point-to-plane distance for robust non-rigid registration.<n>The proposed method greatly improves the accuracy of non-rigid registration problems and maintains relatively high solution efficiency.
  arXiv  Detail & Related papers  (2024-05-30T15:55:04Z)
• Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion [62.269219152425556]
  segmentation-based scene text detection methods have drawn extensive attention in the scene text detection field. We propose a Differentiable Binarization (DB) module that integrates the binarization process into a segmentation network. An efficient Adaptive Scale Fusion (ASF) module is proposed to improve the scale robustness by fusing features of different scales adaptively.
  arXiv  Detail & Related papers  (2022-02-21T15:30:14Z)
• An Adaptive Framework for Learning Unsupervised Depth Completion [59.17364202590475]
  We present a method to infer a dense depth map from a color image and associated sparse depth measurements. We show that regularization and co-visibility are related via the fitness of the model to data and can be unified into a single framework.
  arXiv  Detail & Related papers  (2021-06-06T02:27:55Z)
• Deep Magnification-Flexible Upsampling over 3D Point Clouds [103.09504572409449]
  We propose a novel end-to-end learning-based framework to generate dense point clouds. We first formulate the problem explicitly, which boils down to determining the weights and high-order approximation errors. Then, we design a lightweight neural network to adaptively learn unified and sorted weights as well as the high-order refinements.
  arXiv  Detail & Related papers  (2020-11-25T14:00:18Z)
• Deep-3DAligner: Unsupervised 3D Point Set Registration Network With Optimizable Latent Vector [15.900382629390297]
  We propose to develop a novel model that integrates the optimization to learning, aiming to address the technical challenges in 3D registration. In addition to the deep transformation decoding network, our framework introduce an optimizable deep underlineSpatial underlineCorrelation underlineRepresentation.
  arXiv  Detail & Related papers  (2020-09-29T22:44:38Z)
• Towards Lightweight Lane Detection by Optimizing Spatial Embedding [31.26216243950601]
  pixel embedding in proposal-free instance segmentation based lane detection is difficult to optimize. We propose a lane detection method based on proposal-free instance segmentation, directly optimizing spatial embedding of pixels using image coordinate. The proposed method enables real-time lane detection through the simplicity of post-processing and the adoption of a lightweight backbone.
  arXiv  Detail & Related papers  (2020-08-19T07:37:04Z)

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.