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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.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.It is only during boot that I've seen this error over the weekend.And yes, I have updated my Nvidia drivers to the latest.Statistical techniques used to approximate the above equations include Kalman filters, particle filters (aka.

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Typically the cells are assumed to be statistically independent in order to simplify computation. Modern self driving cars mostly simplify the mapping problem to almost nothing, by making extensive use of highly detailed map data collected in advance.

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.

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