Statistical techniques used to approximate the above equations include Kalman filters, particle filters (aka.
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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.
From a SLAM perspective, these may be viewed as location sensors whose likelihoods are so sharp that they completely dominate the inference.
However GPS sensors may go down entirely or in performance on occasions, especially during times of military conflict which are of particular interest to some robotics applications.
New SLAM algorithms remain an active research area, and are often driven by differing requirements and assumptions about the types of maps, sensors and models as detailed below.
Many SLAM systems can be viewed as combinations of choices from each of these aspects.
They provide a set which encloses the pose of the robot and a set approximation of the map.
Bundle adjustment is another popular technique for SLAM using image data, which jointly estimates poses and landmark positions, increasing map fidelity, and is used in commercialized SLAM systems such as Google's Project Tango.
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.