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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.At the opposite extreme, tactile sensors are extremely sparse as they contain only information about points very close to the agent, so they require strong prior models to compensate in purely tactile SLAM.In Windows 8/10, the error “Location is not available” seems to affect only users who upgraded from older Windows versions to Windows 8/10.Surprisingly, many users of pre – installed Windows 8/10 have not met with the same predicament.Statistical independence is the mandatory requirement to cope with metric bias and with noise in measures.Different types of sensors give rise to different SLAM algorithms whose assumptions are which are most appropriate to the sensors.Location-tagged visual data such as Google's Street View may also be used as part of maps.

They provide a set which encloses the pose of the robot and a set approximation of the map.Monte Carlo methods) and scan matching of range data.They provide an estimation of the posterior probability function for the pose of the robot and for the parameters 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.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.

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