## 1 Introduction  
Mobility is a basic feature of human intelligence. From the beginnings of robot technology, people have been imagining building a human-like machine. Smart movement is an obvious feature of a robot and an important manifestation of its intelligence. Self-moving of a robot mainly includes navigation and control, such as perceiving the surrounding environment, knowing where it wants to go and where it is at a given time, and further controlling its movement and adjusting the travel path to reach its destination. In this review, we focus only on the navigation, that is, the robot's awareness of where it is in relation to the surrounding environment. When we walk on the street, we can see that a car is moving and that the leaves on a tree are moving because of wind. Fortunately, we know that the house will not move. Although we see the house moving backward as our forward movement proceeds, we also know that the house is not moving. According to our position relative to that of the house, we also can judge our speed and that of the car, predict the location of the car, and determine whether we need to avoid the car. For robots, the so-called mobile intelligence means that they can move in the same manner as human beings and exercise the same judgment. A robot on a road, for example, an autonomous car, needs to answer three questions constantly during movement: where am I, where am I going, and how do I go? It first needs to judge its movement relative to all other kinds of movement (cars, trees, and houses in the field of vision) and then has to avoid other cars. Among these tasks, the ability to understand surroundings is critical. The performance of an autonomous car heavily depends on the accuracy and reliability of its environmental perception technologies, including self-localization and perception of obstacles. For humans, these actions and judgments are dominated by human intelligence, but for machines, they are extremely complex processes. We first define robotic mobile intelligence. When moving, a robot should have two basic capabilities: (1) to localize itself and navigate; and (2) to understand the environment. The core technologies enabling these capabilities are denoted as simultaneous localization and mapping (SLAM), tracking, and navigation. SLAM constructs or updates the map of an unknown environment while simultaneously keeping track of the robot's location. It is a complex pipeline that consists of many computation-intensive stages, each performing a unique task. Tracking refers to the automatic estimation of the trajectory of a target as it moves. The tracking algorithms are based mainly on estimation theory. Here, the target is a general one, either the robot itself or others. When the robot wants to \"track itself\", which means that the robot wants to know its own motion, called \"navigation\", by which the robot determines its own position and direction of movement. We now define foreground and background targets. We usually set the target to track as a foreground target, such as the cars. The house does not need to be tracked, nor do other stationary objects, so it is set as the background. Some mobile aspects that do not need to be tracked, such as the movement of leaves, are called background targets. The most difficult aspect of video tracking is to distinguish the difference between foreground and background targets. In current navigation systems, researchers are trying to replicate such a complicated process by using a variety of sensors in combination with information processing. For example, vision sensors are commonly used sensors in robotic systems. In the video from a camera fixed on a robot moving on the street, the houses are moving, the cars are moving, and even the trees on the roof have their own motion because of the robot's own movement. How to obtain the mobile relationship between the robot and foreground target from so many moving targets has always been an area of intense research interest.