# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 5 Deep Learning Approaches
### Automotive Radar
#### 5.4.2 Ego-Localization
Table 6 presents the place recognition performance of various deep learning models designed for spinning radar, assessed using the Oxford dataset and evaluated with metrics such as Recall@1, F1, and mAP. For comparison, the non-learning-based descriptor method RaPlace [137] is included. However, challenges arise in the evaluation criteria due to variations in positive boundary definitions, with [94] using a stricter 10 m boundary compared to the less strict 25 m boundaries employed by other models. Different models excel depending on the evaluation metric. RaLF [105] achieves the highest Recall@1 metric, surpassing pure radar models and a similar model [104] that utilizes a LiDAR submap during training. Off the Radar [97] demonstrates superior performance in terms of mAP and F1 score, outperforming even [104]. Notably, the model with the best Recall@1 fails to provide additional metrics, highlighting the need for a uniform localization metric across models. Table 6 also reveals a lack of information regarding inference rates for the models, with RaPlace being the only exception. Additionally, there is limited detail on the hardware used for running the models. Considering that the classical approach closely matches the spinning radar frequency (4 Hz), it is crucial to assess whether a model can operate effectively in an online environment. Some models include a fine pose-matching layer, often utilizing a lidar submap, where the transformation matrix between a pose and a keypose is estimated after place recognition. The evaluation metric for these models aligns with odometry assessments. However, in \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Recall@1** & **mAP** & **F1** & **Inference (s)** & **Device** \\ \hline Kidnapped radar [93] & 90.49 & 0.75 & 0.7 & - & - \\ Look around you * [94] & - & 0.53 & 0.53 & - & - \\ Contrastive Learning [95] & 85.39 & 0.95 & 0.89 & - & - \\ Off the Radar [97] & 93.67 & **0.99** & **0.95** & - & - \\ Radar, LCD [96] & - & 0.94 & - & - & - \\ Radar-to-Lidar * [104] & 93.18 & - & - & - & - \\ RaLF * [105] & **97.0** & - & - & - & 2 NVIDIA TITAN-X GPU (Santa Clara, CA, USA) \\ RaPlace * [137] & 78.0 & - & - & 0.24 & 16-Core Intel i7-11700 CPU (Santa Clara, CA, USA) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: List of deep learning methods applied to spinning radar-based localization on the Oxford radar dataset. Notably, (*) has a positive boundary of 10 m, while the other methods have a boundary of 25 m. Additionally, (***)** incorporates an additional LiDAR submap during training. Best performing models are highlighted with **bold**. this context, pose differences are more significant than those close to the identity matrix, reflecting a larger average displacement between poses and making translation and rotation ATE more meaningful. Table 7 indicates that models incorporating scan-matching techniques, such as RaLL [102] and RaLF [105], outperform RadarLoc, emphasizing the significance of including additional information beyond the scan data. This underscores the importance of implementing a place-recognition head, as relying solely on a single 6 DOF head may not be sufficient for effective localization, particularly in large and possibly repeating environments. In conclusion, Table 8 highlights the disparity between an automotive radar-only localization models and a state-of-the-art approach employing camera and LiDAR sensors. The latter outperforms the radar-only pipeline by approximately 7% points for Recall@1 and 4% points for the F1 score. While the state-of-the-art approach showcases superior performance, the results still underscore the reliability of the radar-only model. Further investigation could explore the potential benefits of sensor fusion models, which have demonstrated their utility in odometry estimation.