Advancements in Localization and Mapping

The field of localization and mapping is witnessing significant advancements with the development of innovative methods and techniques. Researchers are exploring new approaches to improve the accuracy and efficiency of localization systems, including the use of deep learning-based methods, geometric constraints, and attentional graph neural networks. The integration of these techniques is enabling the creation of more robust and scalable localization systems, capable of handling complex environments and sparse data. Noteworthy papers in this area include:

  • The work on Rotation Invariance in Floor Plan Digitization using Zernike Moments, which improves the rotation invariance of floor plan digitization.
  • The proposal of the Keypoint Localization Framework, which achieves accurate robot localization using a learned keypoint detector and descriptor.
  • The development of the Attentional Graph Meta-Learning model, which enhances indoor localization using extremely sparse fingerprints.

Sources

Rotation Invariance in Floor Plan Digitization using Zernike Moments

Robot Localization Using a Learned Keypoint Detector and Descriptor with a Floor Camera and a Feature Rich Industrial Floor

Semi-Self Representation Learning for Crowdsourced WiFi Trajectories

Using Mobile Relays to Strongly Connect a Minimum-Power Network between Terminals Complying with No-Transmission Zones

SELC: Self-Supervised Efficient Local Correspondence Learning for Low Quality Images

Learning Affine Correspondences by Integrating Geometric Constraints

Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints

Scalable Routing in a City-Scale Wi-Fi Network for Disaster Recovery

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