mlsanityData to 13 Oct 2026

Vision-based localization methods under GPS-denied conditions

arXiv 2211.11988 ↗First posted 22 Nov 2022cs.CV cs.ROcited by 0

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Abstract (arXiv)

This paper reviews vision-based localization methods in GPS-denied environments and classifies the mainstream methods into Relative Vision Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss the broad application of optical flow in feature extraction-based Visual Odometry (VO) solutions and introduce advanced optical flow estimation methods. For AVL, we review recent advances in Visual Simultaneous Localization and Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman Filter (EKF) based methods. We also introduce the application of offline map registration and lane vision detection schemes to achieve Absolute Visual Localization. This paper compares the performance and applications of mainstream methods for visual localization and provides suggestions for future studies.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 25 of its 110 references that are in the map. First posted on arXiv 22 Nov 2022.

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25 of 110 references.

References · 25

  1. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,435
  2. 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,057
  3. 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 984
  4. 2017PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volumecited by 967
  5. 2020ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAMcited by 866
  6. 2016ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Camerascited by 803
  7. 2016FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networkscited by 782
  8. 2016Direct Sparse Odometrycited by 480
  9. 2017VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimatorcited by 420
  10. 2018GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Posecited by 342
  11. 2018The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perceptioncited by 253
  12. 2018EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Camerascited by 224
13 more
  1. 2020What Matters in Unsupervised Optical Flowcited by 124
  2. 2020Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimationcited by 120
  3. 2017Robust Stereo Visual Inertial Odometry for Fast Autonomous Flightcited by 27
  4. 2021Avoiding Degeneracy for Monocular Visual SLAM with Point and Line Featurescited by 13
  5. 2016Efficient Optical flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Dronecited by 11
  6. 2021Deep Online Correction for Monocular Visual Odometrycited by 9
  7. 2017Google Map Aided Visual Navigation for UAVs in GPS-denied Environmentcited by 6
  8. 2021Run Your Visual-Inertial Odometry on NVIDIA Jetson: Benchmark Tests on a Micro Aerial Vehiclecited by 6
  9. 2018Trifo-VIO: Robust and Efficient Stereo Visual Inertial Odometry using Points and Linescited by 5
  10. 2021Visual Navigation Using Sparse Optical Flow and Time-to-Transitcited by 4
  11. 2018Fast Gaussian Process Occupancy Mapscited by 3
  12. 2018Low-cost Autonomous Navigation System Based on Optical Flow Classificationcited by 1
  13. 2021An Equivariant Filter for Visual Inertial Odometrycited by 1

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