# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 1 Introduction
An extra sensor, such as lidar or camera, can also provide additional spatial information to the system. The framework for classifying SLAM approaches was introduced in 1995 through the seminal works by [PERSON] et al. [11; 12]. In the realm of perception-based SLAM methods, a central concept involved manually designing features to facilitate the alignment of keypoints within image or pointcloud representations. However, much like the progress witnessed in various other fields, such as natural language processing [13] and computer vision [14], deep learning methods have been developed to address the challenges faced in a SLAM method. In the field of computer vision, deep learning has been applied thoroughly to address a variety of issues for the SLAM problem, including keypoint detection, ego-motion estimation, structure-from-motion, and ego-localization [15; 16; 17; 18]. Analog methods have been developed for lidar data that work on a point cloud representation rather than a grid representation [19; 20; 21]. The type of deep learning architecture consists of a range of modules from standard spatial and temporal layers such as convolutional neural networks (CNNs) [22] and recurrent neural network (RNN) such as long short-term Memory layers (LSTMs) [23] and Gated recurrent units (GRUs) [24], to sophisticated graph neural-networks (GNNs) [25] and transformer architectures [26]. Other optimization methods, such as formulating an appropriate loss, multi-task learning, and optimization learning techniques have been thoroughly investigated for cameras and lidar. The exact architecture and training routine depends on the available data, and on the sensors present in the system [27; 28; 29]. Given the increasing prominence of radar as a robust, cost-effective, and dependable perception sensor [5; 9; 30], there is a distinct need to undertake a comprehensive review of deep learning approaches specifically tailored toward radar-based SLAM methodologies. Our main contributions are as follows:We provide an introduction to the formulation of the SLAM problem and radar processing. We delve into a detailed examination of the latest deep learning techniques employed in radar-based odometry and localization. We explore the current methods to integrate radar data into deep learning modules to enhance visual/lidar odometry and ego-localization accuracy. Figure 1 provides an overview of the survey paper proposed in this study. Section 2 introduces the overarching problem of sensor-based odometry and ego-localization. Moving forward, Section 3 references existing survey papers, setting the context for our work. In Section 4, our attention is directed towards fundamental radar processing, exploring diverse setups and methods of data representation for radar data. Section 5 consolidates the latest trends by examining the application of deep learning methods in radar-based odometry and ego-localization pipelines. Notably, we extend our discussion beyond solely radar data to include the integration of fusion and cascading network structures to account for the necessity for a complete and reliable SLAM approach. We also compare the evaluation methods of the models. To conclude, Section 6 offers a comprehensive summary and discussion of potential future trends in the field.