<P> In robotic mapping and navigation, simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it . While this initially appears to be a chicken - and - egg problem there are several algorithms known for solving it, at least approximately, in tractable time for certain environments . Popular approximate solution methods include the particle filter, extended Kalman filter, and GraphSLAM . </P> <P> SLAM algorithms are tailored to the available resources, hence not aimed at perfection, but at operational compliance . Published approaches are employed in self - driving cars, unmanned aerial vehicles, autonomous underwater vehicles, planetary rovers, newer domestic robots and even inside the human body . </P>

What does simultaneous localization and mapping (slam) software do (select all that applies.)