# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 5 Deep Learning Approaches
### Fusion and Cascading Architectures
#### 5.3.3 Cascading Architectures  
Next, we discuss two exemplary methods that employ cascading techniques involving the utilization of results from other models, such as the annotation of radar points during object detection and labeling in a sensor fusion approach. This is to motivate the usage of  
Figure 9: Overview of the RaLL architecture [102]. Three networks, denoted as \(\mathcal{F}_{tt}\), \(\mathcal{F}_{l}\), and \(\mathcal{F}_{r}\), collectively generate a difference tensor \(\Delta E\) across a predefined search space. Subsequently, another network, \(\mathcal{F}_{p}\), referred to as the patch network, is employed to construct a single-valued number for each patch and potential offset combination. This can be expressed in a single tensor \(V(\Delta\mathbf{x}_{ijk})\). Probability distributions are then formed based on this tensor across each dimension. Finally, the pose offsets are generated from these distributions. The obtained pose offsets can be utilized within a Kalman-Filter approach, complemented by IMU data. This comprehensive architecture integrates multiple networks to effectively generate and utilize pose information for enhanced localization. Reproduced with permission from [102].  
deep learning networks, e.g., as a pre-processing network that can assist in filtering out irrelevant detections with a non-learning based matching approach.  
The work conducted by [PERSON] et al. [107] integrates semantic segmentation results from other sensors to categorize radar detections into different classes. Subsequently, the segmented radar detections are filtered to retain only those belonging to relevant classes. The annotated radar point clouds are then subjected to NDT scan matching for alignment. Notably, deep learning is not directly applied to the radar data; instead, deep learning serves to enhance the radar features by incorporating information from other sensors. This approach showcases the synergistic integration of information from diverse sensors, leveraging deep learning to enrich the radar feature set with insights derived from semantic segmentation results. The cascading methodology enhances the discriminate power of radar data, ultimately contributing to improved matching accuracy in scenarios where collaborative sensor information is available.  
[PERSON] et al. [134] use a GAN architecture to create synthetic pointclouds from a RD map. The CNN-based generator creates a mask for RD matrix, and the CNN-based discriminator is trained to differentiate between generator masks and Ground Truth masks created from lidar and synchronous radar data. The resulting synthetic point cloud can be used in object detection task or tracking, and in a SLAM approach for odometry estimation or ego-localization.  
In fusion architectures, radar data is commonly utilized to generate denser synthetic lidar representations through techniques like GAN or VAE for odometry and ego-localization. Such approaches enable the use of synthetic data for odometry and localization methods tailored for denser lidar representations. However, generative networks operate on pre-processed radar representations, like point clouds or scan representations. This pre-processing may result in the loss of relevant information from the raw representation, replaced by synthetic data, potentially leading to inaccurate results.