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The process of combining sensor data in order to achieve precise robot localization typically involves four main steps:

1. Data Acquisition: The robot must first acquire data from multiple sensors, which may include cameras, LiDAR, wheel encoders, and inertial measurement units (IMUs).

2. Sensor Fusion: The robot then uses algorithms to fuse the data from all the sensors in a process called sensor fusion. The goal of sensor fusion is to create a single, integrated model of the environment that incorporates data from all the sensors.

3. Localization: The robot uses this integrated model to determine its position and orientation relative to the environment. This process is called localization, and it can be achieved using techniques such as odometry, landmark-based tracking, or probabilistic algorithms such as the Kalman filter.

4. Control: Finally, the robot uses this information to plan and execute its movements. This may involve adjusting its trajectory to avoid obstacles, determining the optimal path to a target location, or controlling its speed and orientation to achieve a specific task.