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Current approaches to feature detection and matching in images strive to increase the repeatability of the detector and minimize the degree of outliers in the matching. In this paper we present a new approach; we suggest that a lower performance feature detector can produce a result more than adequate for robot navigation irrespectively of the amount of outliers. By using an FPGA together with two cameras we can remove the need for descriptors by performing what we call spurious matching and the use of 3D landmarks. The approach bypasses the problem of outliers and reduces the time consuming task of data association, which slows many matching algorithms down.