# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 5 Deep Learning Approaches
### Radar Based Loop Closure
In a subsequent work, [PERSON] et al. [95; 117] focused on unsupervised radar place recognition, employing an embedding learning approach to ensure that features of distinct instances are separated while features of augmented instances remain invariant. Using a VGG-19 as a frontend backbone, they extract local features that should be similar regardless of total augmentation. Similar to [94], difference matrices are created for localization based on a neighborhood search. [PERSON] et al. [97] implemented a generative model, namely a variational autoencoder (VAE) [118], to create a place recognition method that incorporates the uncertainty to highlight the differences between positive sample pairs. The VAE is used to untangle the feature representation into variance of prediction-irrelevant uncertainty sources and semantic invariant descriptor. For negative pairs, the distance between the semantically invariant descriptors is maximized. For positive pairs, a Gaussian distribution is sampled with the found mean and variance of the VAE and added to the descriptor to take the uncertainty into consideration. This is especially relevant in highly noisy radar scans. The uncertainty also allows for a consisting mapping maintenance, rejecting samples generated with high uncertainty compared to the already present map data. [PERSON] et al. [96] extended the work by [PERSON] et al. [88] by sampling from local descriptors with relevant keypoints to perform place recognition, shown in Figure 7. These descriptors are then processed through a NetVLAD layer adapted for point-wise global localization, originally implemented for a lidar approach [119]. They also used the cosine distance metric to evaluate the triplet loss, enhancing the discriminative power of the extracted features. Unlike prior works, [PERSON] et al. [98] implemented RadarLoc that takes a single cartesian map from spinning radar scans as input and outputs a single pose \(\mathbf{T}\) based on GT information. The architecture of the convolutional network comprises a self-attention masking module, followed by a DenseNet [120] feature extractor, and finally two separate multi-layer perception (MLP) layers for the untangled determination of the translational and rotational component. This configuration facilitates the extraction of a feature map  
Figure 6: Similar place retrieval for Autoplace model [92]. Initially, a 2D point cloud, accompanied by its radial velocity and RCS score, serves as input. The first step involves filtering the point cloud to eliminate moving targets. The resultant filtered point cloud undergoes processing through a spatial–temporal encoder, comprising CNNs as spatial encoders and LSTMs as temporal encoders. The final representation is then fed through a NetVLAD layer, facilitating the extraction of essential features and spatial relationships. Subsequently, similar places, as determined by the NetVLAD layer, are further refined by re-ranking based on the Kullback–Leibler divergence of each RCS histogram. This comprehensive process enhances the model’s ability to understand and differentiate scenes based on radar features. Figure 7: Training pipeline of RadarLCD for loop closure detection [96]. The model adopts the keypoint extraction and encoding structure from [87], with a modification in which the mean of the descriptor map is computed to generate an importance guidance map. This importance guidance map is then sampled at the keypoint locations and processed through a NetVLAD layer to generate a global descriptor. This pipeline ensures that keypoint information, coupled with an importance-guided global representation, is leveraged for effective perform radar-based loop closure detection. Reproduced with permission from [96]. from the cartesian map input. Notably, a generalized max-pooling layer [121] is incorporated as a pooling mechanism, contributing to the extraction of global descriptors in the DenseNet module. [PERSON] et al.