In the context of robotics and particularly in systems like Simultaneous Localization and Mapping (SLAM) or Visual SLAM (vSLAM), "tracking" typically refers to the process of continuously estimating the robot's motion and position over time based on its sensor data.
Here's how tracking might work in a vSLAM system:
- The robot captures a sequence of images with its camera as it moves through the environment.
- For each new image, the robot identifies features (distinctive patterns or points in the image), and matches these to features identified in the previous image.
- Based on how these matched features have moved from one image to the next, the robot estimates its own motion. For instance, if a feature moves to the left in the image, that suggests the robot has moved to the right.
- The robot uses this motion estimate to update its estimated position and orientation (its "pose"). This tracking process is repeated for each new image, allowing the robot to maintain a continuous estimate of its pose over time.
In addition to tracking its own motion, a robot might also use similar techniques to track other objects in the environment. For instance, a rescue robot might use visual tracking to keep track of a human survivor as they move through the scene, or to track a moving hazard like a falling debris.
Tracking is a fundamental capability for most autonomous robots. By continuously tracking its own motion, a robot can navigate through its environment, avoid obstacles, and perform tasks like picking up objects or following a path. In the context of a rescue operation, effective tracking can be crucial for tasks like navigating through debris, locating and following survivors, or returning to base.