Camera-aided inertial navigation using epipolar points

Due to the rapid error growth of navigation systems using low-cost inertial measurement units there is a need to fuse the information with complementary sensors. In this paper a monocular camera is used to aid the system. Unlike SLAM-like approaches the problem of estimating the location of each feature point viewed in a scene is avoided, instead estimated epipolar points on the image plane are used. By maintaining a buffer of past views, the method mimics a short-term visual memory which imposes multiple constraints on the estimation problem. The result is a Sigma-Point Kalman filter in square-root form with a linear and efficient time-update. A simulation study is presented indicating the filter´s capacity to constrain the rate of error growth of an inertial navigation system. The filter may also find useful applications when fusing with additional sensors.