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Raw-data approaches make no assumption that landmarks can be identified, and instead model directly as a function of the location.
Optical sensors may be one-dimensional (single beam) or 2D- (sweeping) laser rangefinders, 3D High Definition Li DAR, 3D Flash LIDAR, 2D or 3D sonar sensors and one or more 2D cameras.
Topological maps are a method of environment representation which capture the connectivity (i.e., topology) of the environment rather than creating a geometrically accurate map.
Topological SLAM approaches have been used to enforce global consistency in metric SLAM algorithms.
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.
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.
The term represents the kinematics of the model, which usually include information about action commands given to a robot.
Different types of sensors give rise to different SLAM algorithms whose assumptions are which are most appropriate to the sensors.
At one extreme, laser scans or visual features provide details of a great many points within an area, sometimes rendering SLAM inference unnecessary because shapes in these point clouds can be easily and unambiguously aligned at each step via image registration.