Simultaneous localization and mapping (SLAM)

sources:

1-http://www.doc.ic.ac.uk/~ajd/Robotics/RoboticsResources/SLAMTutorial1.pdf
2-http://www.doc.ic.ac.uk/~ajd/Robotics/RoboticsResources/SLAMTutorial2.pdf
3-http://en.wikipedia.org/wiki/Simultaneous_localization_and_mapping


SLAM is a technique used by robots and autonomous vehicles to build up a map within an unknown environment (without a priori knowledge) or to update a map within a known environment (with a priori knowledge from a given map) while at the same time keeping track of their current location.
Mapping is the problem of integrating the information gathered for and /or with an autonomous entity (the robot's) sensors into a complex model and depicting with a given representation.
by the first characteristic question What does the world look like? Central aspects in mapping are the representation of the environment and the interpretation of sensor data.

• Mapping
• Sensing
• Locating
• Modeling

The lesser the precision of any locating process might result, the more the support of a locally relevant map improves the assessing of the real location. However, maps generally represent the status at the time of drawing the map, not necessarily consistent with the actual status of the environment at the time of using the map.
Complexity of the technical processes of locating and mapping under conditions of errors and of noise do not allow for a coherent solution of both tasks. Simultaneous localization and mapping (SLAM) is a concept to bind these processes in a loop and therefore supports the contiguity of both aspects in separated processes. Iterative feedback from one process to the other one enhances the results of both consecutive steps.localization is the problem of estimating the place (and pose) of the robot relative to a map. In other words, the robot has to answer the second characteristic question, Where am I? Typically, solutions comprise tracking, where the initial place of the robot is known, and global localization, in which no or just some a priori knowledge about the ambience of the starting position is given.

SLAM is therefore defined as the problem of building a model leading to a new map or repetitively improving an existing map while at the same time localizing the robot within that map.

To find what the environment looks like given a set of observations, a robot needs to know the robot's own kinematicswhich qualities the autonomous acquisition of information has, from which sources additional supporting observations have been made.