Published on Sep 16, 2019
As the name suggests, this kind of navigation is restricted to indoor arenas. Here the environment is generally well structured and map of the part from the robot to the target is known many a times. The most prominent examples that fall into this category are guiding a robot through a hallway , reaching specified locations on user commands, performing scheduled tasks operations in hazardous situations or inside nuclear power plants and many more.
Some of the first vision systems developed for mobile robot navigation relied heavily on the geometry of the space and other metrical information for driving the vision process and performing self localization.
In particular, the interior space was represented by CAD models of varying complexity. With time, these were replaced by simpler models like occupancy maps, topological maps and even sequences of images. All these and subsequent efforts fall into three broad categories.
• Map-Based Navigation. These are systems relying on user created geometricmodels or topological models of the environment.
• Map-Building based Navigation. These are systems that use sensors of various kinds to construct their own geometrical or topological models and use the same for navigation
• Mapless Navigation. These are systems that do not use any explicit representation of the environment space but rather recognize objects found in the surrounding or resort to tracking them by generating motions based on visual observations.
Navigation using Object Recognition
Symbolic navigation means that the robot is not commanded to go to the locations given by specific coordinates but by symbolic commands such as ”go to the desk in front of you, go to the open door on the left of the desk, and go to the next room through the open door”. Such symbolic commands are very convenient for a human to use since he or she uses the same commands with the other person in everyday life.
The recognition method used here exploits the functionality of objects. An object is described by its significant surfaces and functional evidence. Significant surfaces are chosen based on their functional role.
For instance, the primary role of a desk is thus characterized by a work surface and some surfaces that correspond to the support structures, like the ”legs”. Functional evidence can be generated when the object is used for the function for which it is intended. For example, we may observe a desktop supporting some other objects. Such other objects constitute functional evidence.