# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 3 Related Work  
A plethora of survey papers offer in-depth insights into a diverse array of scan matching and odometry estimation methods, addressing different sensor and method types, as illustrated in Table 1. These survey papers were found by combining the terms \"Survey\" or \"Review\" with a task or sensor-specific term such as: \"SLAM\", \"SLAM\", \"lidar\", \"Survey\", \"Ego-localization\", \"Localization\", \"Deep learning\", \"Radar\", \"Odometery\" using Google Scholar and Web of Science. Additional papers were found with a backward search on the found surveys. While there are numerous survey papers dedicated to odometry and localization in SLAM, none specifically delve into the application of deep learning to the radar modality. A recent contribution by [PERSON] et al. [44] lays the groundwork for a foundational understanding of classic radar-based odometry. [PERSON] et al. [45] discuss advancements in deep learning applied to radar perception, although their focus does not specifically center on odometry or place recognition. Conversely, the review conducted by [PERSON] et al. [4] revolves around radar-vision fusion specifically for object detection and segmentation. The survey papers authored by [PERSON] et al. [9] and [PERSON] et al. [5] provide thorough insights into radar pre-processing schemes and deliver a detailed overview of state-of-the-art methods for radar perception tasks within the domain of autonomous driving. Notably, these reviews predominantly concentrate on radar perception concerning object detection and segmentation. They touch upon only a limited number of deep learning-based methods for odometry estimation and localization. The work by [PERSON] et al. [29] closely aligns with our survey paper, as these authors delve into the fundamentals of SLAM issues and present numerous deep learning techniques applied to lidar and visual sensors. It is noteworthy, however, that their coverage of radar is confined to potential fusion scenarios with lidar or visual sensors. Besides these survey papers focusing on radar, many others focus on either visual or lidar-based SLAM methodology. [PERSON] et al. [46] concentrate on non-learning based localization methods for various perception sensors. Extending this work, [PERSON] et al. [47] investigate how deep learning techniques facilitate loop closure identification in lidar and VSLAM. Shifting focus, [PERSON] et al. [48] specialize in deep learning techniques tailored for indoor localization and embedded systems. Additionally, [PERSON] et al. [49] provide an extensive coverage of place recognition and loop closure detection across sensor modalities and application domains. In the realm of sensor fusion, [PERSON] et al. [50] highlight the integration of lidar-based sensors with other modalities, primarily leveraging visual features. On a similar note, [PERSON] et al. [51] concentrate on lidar and VSLAM, briefly addressing the fusion of radar with other modalities. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Paper** & **Year** & **Primary Sensor** & **Deep Learning Focus** & **Odometry** & **Localization** \\ \hline
[44] & 2022 & R & \(\times\) & ✓ & \(\times\) \\ \hline
[46] & 2018 & R,L,C,GPS & \(\times\) & \(\times\) & ✓ \\ \hline
[47] & 2021 & L,C & ✓ & \(\times\) & ✓ \\ \hline  
[PERSON] et al. [52] and [PERSON] et al. [53] predominantly center their reviews on lidar-SLAM. In the context of VSLAM, several comprehensive review papers, including [54; 55; 56; 57; 60], provide detailed overviews, but lack any mention of radar sensors. The exploration of deep learning across diverse sensor modalities is addressed in the works by [PERSON] et al. [58] and [PERSON] et al. [8], with a specific focus on lidar and VSLAM. They particularly emphasize belief-space planning and deep reinforcement learning. In a related context, [PERSON] et al. [27] and [PERSON] et al. [59] delve into the role of deep learning in VSLAM approaches, investigating its applications in fundamental SLAM tasks such as pose optimization and mapping. [PERSON] et al.