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In robotic mapping, 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 and extended Kalman filter. 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, newly emerging domestic robots and even inside the human body. == Problem definition == Given a series of sensor observations over discrete time steps , the SLAM problem is to compute an estimate of the agent's location and a map of the environment . All quantities are usually probabilistic, so the objective is to compute: : Applying Bayes' rule gives a framework for sequentially updating the location posteriors, given a map and a transition function , : Similarly the map can be updated sequentially by : 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. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Simultaneous localization and mapping」の詳細全文を読む スポンサード リンク
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