As autonomous vehicle systems become more prevalent, their navigation capabilities become increasingly critical. Currently most systems rely on a combined GPS/INS solution for vehicle pose computation, while some systems use a video-based approach. One problem with a GPS/INS approach is the possible loss of GPS data, especially in urban environments. Using only INS in this case causes significant drift in the computed pose. The video-based approach is not always reliable due to its heavy dependence on image texture. Our approach to autonomous vehicle navigation exploits the best of both of these by coupling an outlier-robust video-based solution with INS when GPS is unavailable. This allows accurate computation of the system's current pose in these situations. In this paper we describe our system design and provide an analysis of its performance, using simulated data with a range of different noise levels.
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