# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 6 Discussion and Future Research  
Deep learning techniques have gradually made their way into radar-based odometry and localization methods, primarily serving as spatial and temporal feature encoders. These approaches often employ CNNs for spatial encoding and temporal feature encoders in the form of RNN networks, such as LSTMs and GRUs, to gain temporal information about the same object in different scenes. Regardless of task, the modules and layers implemented differ in the configuration of the radar. Automotive radar configurations focus on PointNet and PointNet++ feature encoders to work with a point cloud representation. These feature encoders are chosen due to their simple architecture, enabling a fast inference time due to the shared MLP structure of PointNet. PointNet and its modifications have also been applied extensively for various state-of-the-art methods for point cloud processing in other fields [139; 140; 141; 142; 143; 144]. Spinning radar focuses on CNNs to encode the representation. Similar to the occlusion issue in computer vision, most models try to generate a mask or weight function to deal with the noisy and potentially faulty radar data [145]. Compared to other radar perception tasks, the adoption of deep learning in this context has been somewhat limited. Although state-of-the-art deep learning is used, the number of papers published for radar odometry and localization is far below the number of pages published for object detection and segmentation for radars [5; 9]. Similar to [107], the results from segmentation papers [145; 146; 147] can be used to mask out irrelevant detections and areas from scans that do not contribute to the odometry or localization of the system. In a complete pipeline, segmentation, classification, odometry, and ego-localization should be performed in an end-to-end approach, where the results from one module are \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(\delta\)**x (m)** & \(\delta\)**y (m)** & \(\delta\)**\(\theta\) (deg)** \\ \hline RadarLoc [98] & 2.53 & 2.53 & 2.81 \\ RaLL [102] & 1.04 & **0.69** & 1.26 \\ RaLF [105] & **0.97** & **0.69** & **1.15** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: List of deep learning methods applied for radar based metric localization on the Oxford Radar dataset. (*) only predicts a single 6 DOF transformation matrix with a displacement error. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Recall@1** & **F1** \\ \hline Automplace [92] & 90.45 & 0.95 \\ LCPR* [138] & 97.34 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Automotive localization comparison between Automplace [92] and a vision-LiDAR localization model [138] marked with (*) on the NuScenes dataset. integrated, e.g. as a mask, for another module. In a similar fashion, semantic networks for unsupervised classification have been successfully applied in the fields of computer vision [148] and natural language processing [149]. Despite the availability of processed radar representations like BEV images or point clouds, which are more intuitive for odometry and localization tasks compared to pre-CFAR representations, there is a limited application of deep learning approaches to solve radar-based odometry or localization directly from less processed representations. While the ADC or RAD representation may contain irrelevant information, such as clutter or dynamic objects, deep neural networks have the capability to learn to mask these regions and focus on ego-motion and unique global descriptors based on these representations. As masking is a huge part in spinning radar odometry, it is surprising that no similar methods are applied to pre-CFAR representations for automotive radar. Such an approach could potentially overcome the issue of discarding relevant objects during pre-processing, offering an advantage in scenarios where radar data are available as RAD tensors or as ADC signals, such as in K-Radar or RadIAL datasets.