# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 2 Problem Formulation
### Odometry  
Various sensors can serve to establish an odometry edge within the graph representation. An inertial measurement unit (IMU) achieves this by utilizing internal sensors like gyroscopes, accelerometers, and magnetometers. Similarly, GNSS sensors like a GPS, differential GPS (DGPS) or INS with integrated GPS can also provide odometry measurements from the pose difference computed at two different locations. However, passive IMU and GNSS sensors lack environmental context and are thus vulnerable to external factors such as signal interference or incorrect readings, for example due to a stuck vehicle, despite wheel movement detected by the IMU.  
On the other hand, scan-based odometry methods deduce the vehicle's ego-motion by analyzing environmental data from perception sensors like radar, lidar, and/or vision. Unlike IMU-predicted odometry, sensor-based odometry aligns consecutive scans, leading to a consistent odometry estimate that is grounded in the external environment. However, relying on external sensors introduces its own set of challenges, which can potentially yield inaccurate estimations depending on the specific sensor. In practice, the IMU/GNSS data and scan-matching results are often fused in a Kalman-Filter approach [9]. Here, it needs to be mentioned that any IMU needs to be re-calibrated from time to time due to various factors, including mechanical deterioration, change in environment, or temperature changes [33]. As such, odometry estimations from other sensors are crucial in a SLAM method, allowing accurate prediction of the ego-motion.  
In the realm of radar-based scan matching, a prevalent technique involves range-based feature matching [9]. Radar data are typically represented in the form of a point cloud or a 2D bird's-eye view (BEV) image. The goal is to align consecutive scans to identify the most probable transformation. While deep learning methods can be used to determine the ego-transformation between successive radar scans, traditional techniques continue to find more widespread application. These conventional methods entail the use of manually designed features. Examples include Oriented FAST and rotated BRIEF (ORB)-features [34] and scale-invariant feature transform (SIFT)-features [35] for visual data, as well as Conservative Filtering for Efficient and Accurate Radar Odometry (CFEAR) [36] features tailored for radar data. These features are expressed as point detections. The process of scan matching involves identifying these features in single scans and matching them across consecutive scans. This is accomplished through an iterative closest point (ICP)-based approach [37; 38], which is often integrated with a random sample consensus (RANSAC) [39] technique to discard outliers.  
For laser scan observations, alternate methods like the normal distribution transform (NDT) [40] are commonly employed to calculate the ego-transformation matrix between successive scans. For accurate alignment of sensor measurements, it is essential to exclusively include relevant stationary features. Introducing non-stationary features into the ICP process can distort the vehicle's ego-motion estimation, yielding inaccurate outcomes [41]. Consequently, it is crucial to filter out detections originating from irrelevant features. While this issue has been extensively addressed for cameras and lidar sensors, radar sensors largely evade this concern due to the captured Doppler feature. Although the Doppler component solely provides the radial velocity of a feature, it can be employed to deduce the true velocity of a detection. This allows the removal of non-stationary features before applying an ICP-based approach, ensuring more accurate results [41; 42].