Features for Ground Texture Based Localization -- A Survey

• URL: http://arxiv.org/abs/2002.11948v2
• Date: Tue, 3 Mar 2020 09:58:42 GMT
• Title: Features for Ground Texture Based Localization -- A Survey
• Authors: Jan Fabian Schmid, Stephan F. Simon, Rudolf Mester
• Abstract summary: Ground texture based vehicle localization using feature-based methods is a promising approach to achieve infrastructure-free high-accuracy localization. We provide the first extensive evaluation of available feature extraction methods for this task, using separately taken image pairs as well as synthetic transformations. We identify AKAZE, SURF and CenSurE as best performing keypoint detectors, and find pairings of CenSurE with the ORB, BRIEF and LATCH feature descriptors to achieve greatest success rates for incremental localization.
• Score: 12.160708336715489
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ground texture based vehicle localization using feature-based methods is a promising approach to achieve infrastructure-free high-accuracy localization. In this paper, we provide the first extensive evaluation of available feature extraction methods for this task, using separately taken image pairs as well as synthetic transformations. We identify AKAZE, SURF and CenSurE as best performing keypoint detectors, and find pairings of CenSurE with the ORB, BRIEF and LATCH feature descriptors to achieve greatest success rates for incremental localization, while SIFT stands out when considering severe synthetic transformations as they might occur during absolute localization.

Related papers

• Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization [52.87635234206178]
  This paper proposes a new framework, namely MoNFAP, specifically tailored for multi-face manipulation detection and localization. The framework incorporates two novel modules: the Forgery-aware Unified Predictor (FUP) Module and the Mixture-of-Noises Module (MNM)
  arXiv  Detail & Related papers  (2024-08-05T08:35:59Z)
• A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature Upsampling [54.05517338122698]
  We propose an explicitly controllable query-key feature alignment from both semantic-aware and detail-aware perspectives. We also develop a fine-grained neighbor selection strategy on HR features, which is simple yet effective for alleviating mosaic artifacts. Our proposed ReSFU framework consistently achieves satisfactory performance on different segmentation applications.
  arXiv  Detail & Related papers  (2024-07-02T14:12:21Z)
• GPGait: Generalized Pose-based Gait Recognition [11.316545213493223]
  Recent works on pose-based gait recognition have demonstrated the potential of using such simple information to achieve results comparable to silhouette-based methods. To improve the generalization ability of pose-based methods across datasets, we propose a textbfGeneralized textbfPose-based textbfGait recognition framework.
  arXiv  Detail & Related papers  (2023-03-09T13:17:13Z)
• SuperGF: Unifying Local and Global Features for Visual Localization [13.869227429939423]
  SuperGF is a transformer-based aggregation model that operates directly on image-matching-specific local features. We provide implementations of SuperGF using various types of local features, including dense and sparse learning-based or hand-crafted descriptors.
  arXiv  Detail & Related papers  (2022-12-23T13:48:07Z)
• On the Effective Usage of Priors in RSS-based Localization [56.68864078417909]
  We propose a Received Signal Strength (RSS) fingerprint and convolutional neural network-based algorithm, LocUNet. In this paper, we study the localization problem in dense urban settings. We first recognize LocUNet's ability to learn the underlying prior distribution of the Rx position or Rx and transmitter (Tx) association preferences from the training data, and attribute its high performance to these.
  arXiv  Detail & Related papers  (2022-11-28T00:31:02Z)
• Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition [54.23513799338309]
  We present an Adaptive Local-Component-aware Graph Convolutional Network for skeleton-based action recognition. Our method provides a stronger representation than the global embedding and helps our model reach state-of-the-art.
  arXiv  Detail & Related papers  (2022-09-21T02:33:07Z)
• Bagging Regional Classification Activation Maps for Weakly Supervised Object Localization [11.25759292976175]
  BagCAMs is a plug-and-play mechanism to better project a well-trained classifier for the localization task. Our BagCAMs adopts a proposed regional localizer generation strategy to define a set of regional localizers. Experiments indicate that adopting our proposed BagCAMs can improve the performance of baseline WSOL methods.
  arXiv  Detail & Related papers  (2022-07-16T03:03:01Z)
• 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)
• 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)
• Probabilistic Visual Place Recognition for Hierarchical Localization [22.703331060822862]
  We propose two methods which adapt image retrieval techniques used for visual place recognition to the Bayesian state estimation formulation for localization. We demonstrate significant improvements to the localization accuracy of the coarse localization stage using our methods, whilst retaining state-of-the-art performance under severe appearance change.
  arXiv  Detail & Related papers  (2021-05-07T07:39:14Z)
• Domain-invariant Similarity Activation Map Contrastive Learning for Retrieval-based Long-term Visual Localization [30.203072945001136]
  In this work, a general architecture is first formulated probabilistically to extract domain invariant feature through multi-domain image translation. And then a novel gradient-weighted similarity activation mapping loss (Grad-SAM) is incorporated for finer localization with high accuracy. Extensive experiments have been conducted to validate the effectiveness of the proposed approach on the CMUSeasons dataset. Our performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision.
  arXiv  Detail & Related papers  (2020-09-16T14:43:22Z)

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.