Published approaches are employed in self-driving cars, unmanned aerial vehicles, autonomous underwater vehicles, planetary rovers, newly emerging domestic robots and even inside the human body.
Like many inference problems, the solutions to inferring the two variables together can be found, to a local optimum solution, by alternating updates of the two beliefs in a form of EM algorithm.
They provide an estimation of the posterior probability function for the pose of the robot and for the parameters of the map.
Set-membership techniques are mainly based on interval constraint propagation.
In contrast, grid maps use arrays (typically square or hexagonal) of discretized cells to represent a topological world, and make inferences about which cells are occupied.
Typically the cells are assumed to be statistically independent in order to simplify computation. Modern self driving cars mostly simplify the mapping problem to almost nothing, by making extensive use of highly detailed map data collected in advance.
Statistical techniques used to approximate the above equations include Kalman filters, particle filters (aka.
Monte Carlo methods) and scan matching of range data.
Sensor models divide broadly into landmark-based and raw-data approaches.
Landmarks are uniquely identifiable objects in the world whose location can be estimated by a sensor—such as wifi access points or radio beacons.
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.