5 accepted papers
Abstract— Cross-View Geo-Localization (CVGL) localizes a query image via retrieval from georeferenced satellite imagery,yet severe viewpoint variation remains a central challenge. Recent advances often rely on heavy backbones or add-on modules that achieve high accuracy but are impractical on resour…
Cross-modal Visual Geo-localization often aims to retrieve a satellite visible-light image of the same geographic lo cation from a large-scale database using an infrared image cap tured by an unmanned aerial vehicle (UAV), thereby achieving precise localization. This capability is crucial for autono…
The task of UAV-view geo-localization is to match a query image with database images to estimate the current geographic location of the query image. This is particularly useful in environments where GPS is not available or when the device fails. Although deep learning methods make sufficient progres
Most current LiDAR-based odometry methods use point-to-local plane registration to constrain poses, ignoring the explicit plane structure in the environment. Due to noise interference and uneven distribution of point cloud, local planes are prone to tilt, resulting in registration errors. Therefore,
The data fusion of camera, IMU, and wheel encoder measurements has proved its effectiveness in localizing ground robots, and obtaining accurate sensor extrinsic parameters is its premise. We propose an extrinsic parameter calibration algorithm and a multi-sensor-based pose estimation algorithm for t…