Bi-directional Loop Closure for Visual SLAM

• URL: http://arxiv.org/abs/2204.01524v1
• Date: Fri, 1 Apr 2022 14:06:42 GMT
• Title: Bi-directional Loop Closure for Visual SLAM
• Authors: Ihtisham Ali, Sari Peltonen, Atanas Gotchev
• Abstract summary: We propose an approach for bi-directional loop closure. This will provide us with the capability to relocalize to a location even when traveling in the opposite direction. We present a technique to select training data from large datasets in order to make them usable for the bi-directional problem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A key functional block of visual navigation system for intelligent autonomous vehicles is Loop Closure detection and subsequent relocalisation. State-of-the-Art methods still approach the problem as uni-directional along the direction of the previous motion. As a result, most of the methods fail in the absence of a significantly similar overlap of perspectives. In this study, we propose an approach for bi-directional loop closure. This will, for the first time, provide us with the capability to relocalize to a location even when traveling in the opposite direction, thus significantly reducing long-term odometry drift in the absence of a direct loop. We present a technique to select training data from large datasets in order to make them usable for the bi-directional problem. The data is used to train and validate two different CNN architectures for loop closure detection and subsequent regression of 6-DOF camera pose between the views in an end-to-end manner. The outcome packs a considerable impact and aids significantly to real-world scenarios that do not offer direct loop closure opportunities. We provide a rigorous empirical comparison against other established approaches and evaluate our method on both outdoor and indoor data from the FinnForest dataset and PennCOSYVIO dataset.

Related papers

• ROVER: Robust Loop Closure Verification with Trajectory Prior in Repetitive Environments [29.075108845201164]
  Loop detection is important for simultaneous localization and closure (SLAM)<n>We propose ROVER, a loop closure verification method that leverages the historical trajectory as a prior constraint to reject false loops.<n> Benchmark comparisons and real-world experiments demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2025-08-19T03:34:08Z)
• Sliced-Wasserstein Distance-based Data Selection [0.0]
  We propose a new unsupervised anomaly detection method based on the sliced-Wasserstein distance. Our filtering technique is interesting for decision-making pipelines deploying machine learning models in critical sectors. We present the filtering patterns of our method on synthetic datasets and numerically benchmark our method for training data selection.
  arXiv  Detail & Related papers  (2025-04-17T13:07:26Z)
• Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors [53.6277160912059]
  We propose a method to promote pros of data-driven based and overfitting-based methods for better generalization, faster inference, and higher accuracy in learning neural SDFs. We introduce a novel statistical reasoning algorithm in local regions which is able to finetune data-driven based priors without signed distance supervision, clean point cloud, or point normals.
  arXiv  Detail & Related papers  (2024-10-25T16:48:44Z)
• Comparing Optical Flow and Deep Learning to Enable Computationally Efficient Traffic Event Detection with Space-Filling Curves [0.6322312717516407]
  We compare Optical Flow (OF) and Deep Learning (DL) to feed computationally efficient event detection via space-filling curves on video data from a forward-facing, in-vehicle camera. Our results yield that the OF approach excels in specificity and reduces false positives, while the DL approach demonstrates superior sensitivity.
  arXiv  Detail & Related papers  (2024-07-15T13:44:52Z)
• Cross-Camera Trajectories Help Person Retrieval in a Camera Network [124.65912458467643]
  Existing methods often rely on purely visual matching or consider temporal constraints but ignore the spatial information of the camera network. We propose a pedestrian retrieval framework based on cross-camera generation, which integrates both temporal and spatial information. To verify the effectiveness of our method, we construct the first cross-camera pedestrian trajectory dataset.
  arXiv  Detail & Related papers  (2022-04-27T13:10:48Z)
• A Two-Block RNN-based Trajectory Prediction from Incomplete Trajectory [14.725386295605666]
  We introduce a two-block RNN model that approximates the inference steps of the Bayesian filtering framework. We show that the proposed model improves the prediction accuracy compared to the three baseline imputation methods. We also show that our proposed method can achieve better prediction compared to the baselines when there is no miss-detection.
  arXiv  Detail & Related papers  (2022-03-14T13:39:44Z)
• 6D Rotation Representation For Unconstrained Head Pose Estimation [2.1485350418225244]
  We address the problem of ambiguous rotation labels by introducing the rotation matrix formalism for our ground truth data. This way, our method can learn the full rotation appearance which is contrary to previous approaches that restrict the pose prediction to a narrow-angle. Experiments on the public AFLW2000 and BIWI datasets demonstrate that our proposed method significantly outperforms other state-of-the-art methods by up to 20%.
  arXiv  Detail & Related papers  (2022-02-25T08:41:13Z)
• Light Field Saliency Detection with Dual Local Graph Learning andReciprocative Guidance [148.9832328803202]
  We model the infor-mation fusion within focal stack via graph networks. We build a novel dual graph modelto guide the focal stack fusion process using all-focus pat-terns.
  arXiv  Detail & Related papers  (2021-10-02T00:54:39Z)
• LCDNet: Deep Loop Closure Detection for LiDAR SLAM based on Unbalanced Optimal Transport [8.21384946488751]
  We introduce the novel LCDNet that effectively detects loop closures in LiDAR point clouds. LCDNet is composed of a shared encoder, a place recognition head that extracts global descriptors, and a relative pose head that estimates the transformation between two point clouds. Our approach outperforms state-of-the-art techniques by a large margin even while dealing with reverse loops.
  arXiv  Detail & Related papers  (2021-03-08T20:19:37Z)
• Diverse Knowledge Distillation for End-to-End Person Search [81.4926655119318]
  Person search aims to localize and identify a specific person from a gallery of images. Recent methods can be categorized into two groups, i.e., two-step and end-to-end approaches. We propose a simple yet strong end-to-end network with diverse knowledge distillation to break the bottleneck.
  arXiv  Detail & Related papers  (2020-12-21T09:04:27Z)
• STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction [24.855059537779294]
  We present a novel end-to-end two-stage network: Spatio--Interactive Network (STINet) In addition to 3D geometry of pedestrians, we model temporal information for each of the pedestrians. Our method predicts both current and past locations in the first stage, so that each pedestrian can be linked across frames.
  arXiv  Detail & Related papers  (2020-05-08T18:43:01Z)
• Detection in Crowded Scenes: One Proposal, Multiple Predictions [79.28850977968833]
  We propose a proposal-based object detector, aiming at detecting highly-overlapped instances in crowded scenes. The key of our approach is to let each proposal predict a set of correlated instances rather than a single one in previous proposal-based frameworks. Our detector can obtain 4.9% AP gains on challenging CrowdHuman dataset and 1.0% $textMR-2$ improvements on CityPersons dataset.
  arXiv  Detail & Related papers  (2020-03-20T09:48:53Z)
• Tracking Road Users using Constraint Programming [79.32806233778511]
  We present a constraint programming (CP) approach for the data association phase found in the tracking-by-detection paradigm of the multiple object tracking (MOT) problem. Our proposed method was tested on a motorized vehicles tracking dataset and produces results that outperform the top methods of the UA-DETRAC benchmark.
  arXiv  Detail & Related papers  (2020-03-10T00:04:32Z)

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.