Robust Feature Correspondences for Vision-Based Navigation with Slow Frame-Rate Cameras

We propose a vision-based inertial system that overcomes the problems associated with slow update rates in navigation systems based on high-resolution cameras. Due to bandwidth limitations in current camera interfaces, like Firewire or USB, an increase in the camera resolution results in a drop of the effective frame-rate from the sensor. This increases the correspondence problem for point features in consecutive images due to significant motion of the features. We solve the correspondence problem for very significant motion of point features between consecutive images by analyzing the motion blur to estimate the current motion parameters. The proposed algorithm can be used to track the position of point features in video images originating from slow frame-rate cameras. The system is validated on real images from a camera moved by a mobile system, but it can be used for any type of motion ranging from flying systems to mobile robots operating in outdoor or indoor environments