Frank Holzmüller

• This master’s thesis tackles the challenging task of indoor localization using high-resolution synthetic aperture Radio Detection And Ranging (radar) images. In the course of this thesis, different approaches to localization revolving around the detector-descriptor architecture are evaluated. The proposed pipeline acts as a flexible backbone for the implementation of different detection, description and matching algorithms. By leveraging typical characteristics of radar images through dedicated, radar-specific keypoint detectors, the algorithm is able to detect reliable keypoints for tracking. Through the use of a state of the art standalone machine learning descriptor model named Detect Don’t Describe - Describe Don’t Detect (DeDoDe), the pipeline is able to match keypoints across different radar images reliably. Since DeDoDe can work on any kind of generated images, the pipeline is not limited to radar images and can be used with other types of inputs as well. The pipeline parameters are adapted and evaluated on a custom dataset of Synthetic Aperture Radar (SAR) images generated by four synchronized radar sensors mounted on an Unmanned Ground Vehicle (UGV) in an indoor environment. With a combination of the Constant False Alarm Rate (CFAR) algorithm and the descriptor named DeDoDe, this thesis proposes a radar-visual hybrid approach for localization based on radar images. On the custom dataset, the pipeline acts like a black box with just a Random Sample Consensus (RANSAC) threshold as the sole tunable parameter. The architecture for this pipeline is created with the intent of providing a solid base for loop closure detection in radar Simultaneous Localization And Mapping (SLAM) systems. Since the descriptors of the DeDoDe model already offer great performance without any need for retraining or fine tuning, the pipeline is able to perform well out of the box. For estimation of the relative error between two correlated SAR images, a median Euclidean translational error of less than 1 cm was achieved over all scenarios.