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In this paper we describe an approach to feature representation for simultaneous localization and mapping, SLAM. It is a general representation for features that addresses symmetries and constraints in the feature coordinates. Furthermore, the representation allows for the features to be added to the map with partial initialization. This is an important property when using oriented vision features where angle information can be used before their full pose is known. The number of the dimensions for a feature can grow with time as more information is acquired. At the same time as the special properties of each type of feature are accounted for, the commonalities of all map features are also exploited to allow SLAM algorithms to be interchanged as well as choice of sensors and features. In other words the SLAM implementation need not be changed at all when changing sensors and features and vice versa. Experimental results both with vision and range data and combinations thereof are presented.