A critique on previous work in vision aided navigation

This paper presents a critique on previous work in the field of vision aided navigation, particularly in the fusion of visual and inertial sensors for navigation. Several improvements and updates are proposed for the existent systems. GPS receivers have allowed for accurate navigation for many vehicles and robotic platforms. GPS based navigation can, however, prove to be impractical in applications where there is no GPS reception such as underground, indoors or in some urban areas. This pertains, in particular, to many robotic applications where position must be known in global coordinates or relative to a reference point. An inertial navigation system (INS) can be used to calculate one's relative navigation state via dead-reckoning calculations. The downfall of a low-cost INS is the errors associated with the system. While these errors are initially small, integration causes large drift errors over time. To combat this problem, cameras can be used to estimate the errors present in the INS readings. These results can then be used to correct the navigation state output from the INS. While the motion estimations from the cameras are not error-free, this method is made highly effective because of the complementary nature of the errors from the cameras and INS. Several improvements are proposed for this method; algorithmically, in updates to its hardware, and with the introduction of graphics processors to improve computational performance. The overall system performance, individual steps, algorithms, and results are compared to results from similar works to those of the proposed improvements. It is shown that the accuracy, responsiveness and overall performance of the system can potentially be greatly improved.