# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 1 Introduction  
Simultaneous Localization and Mapping (SLAM) stands as a foundational technology in the realms of robotics and autonomous systems. Its primary function involves enabling an autonomous system to not only construct a comprehensive map of its surroundings, but also to accurately determine its own position within this map. Central to any SLAM algorithm are two pivotal concepts: odometry, which forecasts the system's ego-motion, and ego-localization, the skill to establish its position within the map using external sensor data. Passive and active perception-based sensors, such as cameras, lidars, sonars, and radars, are commonly applied in a SLAM approach [1]. These sensors have the advantage of deriving accuracy from observations. Maps can be constructed from these sensors to gain a spatial understanding of the environment. In an ideal world, information derived from active sensors, such as lidars, sonars, and radars, denote absolute precision for a measurement, and the odometry and ego-localization of a system can be determined solely from this information. However, due to various factors, the detections are not always consistent or accurate [2]. Passive positional sensors, such as a Global Navigation Satellite System (GNSS) or an Inertial Navigation System (INS) can provide additional information about the position and dynamics of the system, but are unable to capture the spatial environment on their own. Additionally, GNSS sensors also suffer from poor performance in common application areas, such as indoor scenarios, tunnels, and in between sky scrapers due to poor signal reception [3]. In such scenarios, perception-based ego-localization is preferred. Positional sensors such as GNSS and INS are often included in autonomous vehicle applications besides a perception sensor, which will be discussed later. Perception-based SLAM approaches can be categorized according to the type of sensor they rely on, with a primary distinction being between active range-based and passive visual-based methodologies. Visual SLAM (VSLAM) methods specifically employ onboard cameras to depict their surroundings. Despite the considerable research on VSLAM techniques, they encounter difficulties in adverse weather conditions like heavy rain or fog, and in varying lighting situations such as intense sunlight or during the night. These challenges extend to issues concerning accurate depth perception [4; 5]. Range-based techniques utilize sensors such as lidar, sonar, and radar to operate. Sonar finds its main utility in underwater scenarios [6; 7] whereas lidars and radars are predominantly employed in both indoor and outdoor applications, particularly in the context of autonomous vehicles. While lidars offer denser detection capabilities, radars bring their own set of distinctive advantages. Lidars tend to be more expensive than radars and are generally less resilient in adverse weather conditions. Although hybrid approaches that leverage both radars and lidars for SLAM tasks exist, the focus in literature and industry remains on the employment of a solitary range-based sensor [5; 8]. In practice, a range-based sensor is often integrated beside visual sensors to provide depth information and assist in visually impaired scenarios. Another motivation in incorporating multiple sensors into a complete SLAM is to avoid the occlusion problem that arises when using a solitary perception sensor. When an object is obstructed by another object, or an object moves out of the frame of one sensor, another sensor might be able to spot the object. Radars are especially relevant for such a task, since radars can detect objects that appear occluded in lidars or visual data [9]. However, radar data are very susceptible to \"ghost detections\" [9], where a radar detects objects that are not in the scene. These detections are mostly caused by multi-path propagation [10] effects. Similar to the occlusion problem in computer vision, a deep neural network can learn a mask to filter out non-relevant objects and regions.