# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 6 Discussion and Future Research
In contrast, a notable trend in other radar perception tasks involves a shift away from heavily pre-processed 2D/3D data (e.g., BEV or point cloud representations) and toward less processed representations such as ADC [150] or RAD [74]. The rationale behind this trend is to equip deep learning models to discern relevant features within the input space and mask out irrelevant elements. While this trend is observable in radar-based odometry, particularly in processing RA maps for spinning radars, the automotive radar field has not yet followed a similar trajectory, as it utilizes the post CFAR detections for PointNet feature encoding. Moreover, while the usage of 4D radar data is increasing for tasks like object detection and segmentation, their adoption in odometry and localization methods remains limited to a handful of approaches. Another trend observed is the dominance of spinning radar configurations in the investigated sensor setups. For odometry and localization tasks, the number of papers utilizing spinning radar data outweighs those using automotive radar. However, given the lower cost and easier integration of automotive radars into existing vehicles, there is a potential for a greater research focus on such radar sensors. Furthermore, we observed that many models evaluating on the same dataset follow vastly different evaluation criteria, both in terms of their evaluation method and the inference speed of the model. Unlike object detection and segmentation, where NMS and IOU scores are widely accepted, odometry and place recognition tasks lack standardized evaluation criteria, leading to variations based on external parameters such as the true pose parameter in determining a true recognized place. In the realm of ego*localization systems, most papers mention that the system performs faster than the recording frequency. However, in real-time applications, additional factors need consideration, such as data transfer during place recognition and pose refinement. For odometry applications, the hardware components used differ significantly among papers, making it challenging to compare results directly. For deep learning approaches, the inference time is especially important in regard to optimization and fine-tuning [151; 152]. If a model does not work in real time on a platform, there are various steps that can be taken, e.g., lowering the amount of parameters in the network or changing the hardware of the system. It is also possible that some hardware in the system will be upgraded, or some properties of the radar will change. Then, the model needs to quickly adapt to these changes, and it needs to be controlled to ensure that the network is still compatible with the new properties of the data, signaling the importance of transfer learning. Additionally, in contrast to radar-based odometry and localization, the broader field of sensor perception is witnessing a focus on advanced deep learning techniques. These include variations in transformers [153; 154], the utilization of various point feature encoder [112; 155] architectures, and the incorporation of rotational invariance into deep learning modules [156; 157] through model architectures instead of augmentations during training. These advancements are influencing not only general radar perception but also odometry and localization models across other modalities. Conceptualization: M.B. and A.P. ; writing--original draft preparation, M.B. ; supervision, A.P. and T.M. ; Review, A.P. and T.M. ; project administration, A.P. and T.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data sharing not applicable. The authors declare no conflict of interest.