GV-Bench: Benchmarking Local Feature Matching for Geometric Verification of Long-term Loop Closure Detection

• URL: http://arxiv.org/abs/2407.11736v2
• Date: Wed, 17 Jul 2024 01:46:29 GMT
• Title: GV-Bench: Benchmarking Local Feature Matching for Geometric Verification of Long-term Loop Closure Detection
• Authors: Jingwen Yu, Hanjing Ye, Jianhao Jiao, Ping Tan, Hong Zhang,
• Abstract summary: Loop closures correct drifts in trajectory estimation to build a globally consistent map. A false loop closure can be fatal, so verification is required as an additional step to ensure robustness. This paper proposes a unified benchmark targeting geometric verification of loop closure detection under long-term conditional variations.
• Score: 32.54601978758081
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual loop closure detection is an important module in visual simultaneous localization and mapping (SLAM), which associates current camera observation with previously visited places. Loop closures correct drifts in trajectory estimation to build a globally consistent map. However, a false loop closure can be fatal, so verification is required as an additional step to ensure robustness by rejecting the false positive loops. Geometric verification has been a well-acknowledged solution that leverages spatial clues provided by local feature matching to find true positives. Existing feature matching methods focus on homography and pose estimation in long-term visual localization, lacking references for geometric verification. To fill the gap, this paper proposes a unified benchmark targeting geometric verification of loop closure detection under long-term conditional variations. Furthermore, we evaluate six representative local feature matching methods (handcrafted and learning-based) under the benchmark, with in-depth analysis for limitations and future directions.

Related papers

• Memory Management for Real-Time Appearance-Based Loop Closure Detection [1.1279808969568252]
  We present a novel real-time loop closure detection approach for large-scale and long-term SLAM. Our approach is based on a memory management method that keeps time for each new observation under a fixed limit.
  arXiv  Detail & Related papers  (2024-07-22T00:24:12Z)
• Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation [1.1279808969568252]
  In appearance-based localization and mapping, loop closure detection is the process used to determinate if the current observation comes from a previously visited location or a new one. This paper presents an online loop closure detection approach for large-scale and long-term operation.
  arXiv  Detail & Related papers  (2024-07-22T00:13:00Z)
• Unsupervised Landmark Discovery Using Consistency Guided Bottleneck [63.624186864522315]
  We introduce a consistency-guided bottleneck in an image reconstruction-based pipeline. We propose obtaining pseudo-supervision via forming landmark correspondence across images. The consistency then modulates the uncertainty of the discovered landmarks in the generation of adaptive heatmaps.
  arXiv  Detail & Related papers  (2023-09-19T10:57:53Z)
• Towards View-invariant and Accurate Loop Detection Based on Scene Graph [33.68204049028845]
  Loop detection plays a key role in visual SLAM by correcting the accumulated pose drift. Current semantic-aided loop detection methods face challenges in dealing with ambiguous semantic instances and drastic viewpoint differences. This paper introduces a novel loop detection method based on an incrementally created scene graph.
  arXiv  Detail & Related papers  (2023-05-24T08:34:43Z)
• Rethinking Spatial Invariance of Convolutional Networks for Object Counting [119.83017534355842]
  We try to use locally connected Gaussian kernels to replace the original convolution filter to estimate the spatial position in the density map. Inspired by previous work, we propose a low-rank approximation accompanied with translation invariance to favorably implement the approximation of massive Gaussian convolution. Our methods significantly outperform other state-of-the-art methods and achieve promising learning of the spatial position of objects.
  arXiv  Detail & Related papers  (2022-06-10T17:51:25Z)
• FD-SLAM: 3-D Reconstruction Using Features and Dense Matching [18.577229381683434]
  We propose an RGB-D SLAM system that uses dense frame-to-model odometry to build accurate sub-maps. We incorporate a learning-based loop closure component based on 3-D features which further stabilises map building. The approach can also scale to large scenes where other systems often fail.
  arXiv  Detail & Related papers  (2022-03-25T18:58:46Z)
• Automatic Vocabulary and Graph Verification for Accurate Loop Closure Detection [21.862978912891677]
  Bag-of-Words (BoW) builds a visual vocabulary to associate features and then detect loops. We propose a natural convergence criterion based on the comparison between the radii of nodes and the drifts of feature descriptors. We present a novel topological graph verification method for validating candidate loops.
  arXiv  Detail & Related papers  (2021-07-30T13:19:33Z)
• Dense Label Encoding for Boundary Discontinuity Free Rotation Detection [69.75559390700887]
  This paper explores a relatively less-studied methodology based on classification. We propose new techniques to push its frontier in two aspects. Experiments and visual analysis on large-scale public datasets for aerial images show the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-11-19T05:42:02Z)
• Making Affine Correspondences Work in Camera Geometry Computation [62.7633180470428]
  Local features provide region-to-region rather than point-to-point correspondences. We propose guidelines for effective use of region-to-region matches in the course of a full model estimation pipeline. Experiments show that affine solvers can achieve accuracy comparable to point-based solvers at faster run-times.
  arXiv  Detail & Related papers  (2020-07-20T12:07:48Z)
• Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps [78.2581910688094]
  This work introduces a novel self-enhancement method to harvest accurate object localization maps and object boundaries with only category labels as supervision. In particular, the proposed Self-Enhancement Maps achieve the state-of-the-art localization accuracy of 54.88% on ILSVRC.
  arXiv  Detail & Related papers  (2020-06-09T12:35:55Z)
• Scope Head for Accurate Localization in Object Detection [135.9979405835606]
  We propose a novel detector coined as ScopeNet, which models anchors of each location as a mutually dependent relationship. With our concise and effective design, the proposed ScopeNet achieves state-of-the-art results on COCO.
  arXiv  Detail & Related papers  (2020-05-11T04:00:09Z)

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.