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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.

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.Popular approximate solution methods include the particle filter, extended Kalman filter, and Graph SLAM.SLAM algorithms are tailored to the available resources, hence not aimed at perfection, but at operational compliance.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.This can include map annotations to the level of marking locations of individual white line segments and curbs on the road.

Location-tagged visual data such as Google's Street View may also be used as part of maps.and wifi-SLAM (sensing by strengths of nearby wifi access points).Recent approaches apply quasi-optical wireless ranging for multi-lateration (RTLS) or multi-angulation in conjunction with SLAM as a tribute to erratic wireless measures.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.

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