A component of a mobile robot system is the ability to localize itself accurately and simultaneously, to build a map of the environment. Most of the existing algorithms are based on laser range finders, sensors or artificial landmarks. It is a vision-based mobile robot localization and mapping algorithm, which uses scale-invariant image features as natural landmarks in unmodified environments. The invariance of these features to image translation, scaling and rotation makes them suitable landmarks for mobile robot localization and map building.
Mobile robot localization and mapping, the process of simultaneously tracking the position of a mobile robot relative to its environment and building a map of the environment, has been a central research topic in mobile robotics. Accurate localization is a prerequisite for building a good map, and having an accurate map is essential for good localization. Therefore, simultaneous localization and map building (SLAM) is critical underlying factor for successful mobile robot navigation in a large environment, irrespective of what the high-level goals or applications are.
To achieve the SLAM, there are different types of sensor modalities such as sonar, laser range finders and vision. Sonar is fast and cheap but usually very crude, whereas a laser scanning system is active, accurate but slow. Vision systems are passive and high resolution.
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