Abstract
With advances in computing power, stereo vision has become an essential part of navigation applications. However, there may be instances wherein insufficient image data precludes the estimation of navigation parameters. Earlier, a novel vision-based velocity estimation method was developed by the authors, which suffered from the aforementioned drawback. In this paper, the vision-based navigation method has been integrated with a unique low-cost reduced inertial sensor system to bridge the navigation gap using one gyroscope and two accelerometers along with the inputs from wheel speed sensors. The integrated system is based on the extended Kalman filter and was tested on three trajectories with the introduction of vision data gaps. The system showed promising results for autonomous land vehicle applications.
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