RaSCL: Radar to Satellite Crossview Localization
This work addresses the need for reliable localization in autonomous field applications where GNSS is insufficient, offering a solution that integrates radar and satellite data.
The paper tackles the problem of GNSS-free global localization for autonomous systems by registering ground-based imaging radar with overhead RGB imagery, achieving effective localization across diverse geographic conditions and robotic platforms.
GNSS is unreliable, inaccurate, and insufficient in many real-time autonomous field applications. In this work, we present a GNSS-free global localization solution that contains a method of registering imaging radar on the ground with overhead RGB imagery, with joint optimization of relative poses from odometry and global poses from our overhead registration. Previous works have used various combinations of ground sensors and overhead imagery, and different feature extraction and matching methods. These include various handcrafted and deep-learning-based methods for extracting features from overhead imagery. Our work presents insights on extracting essential features from RGB overhead images for effective global localization against overhead imagery using only ground radar and a single georeferenced initial guess. We motivate our method by evaluating it on datasets in diverse geographic conditions and robotic platforms, including on an Unmanned Surface Vessel (USV) as well as urban and suburban driving datasets.