  Monocular vision-based Simultaneous Localization and Mapping (SLAM) is used
for various purposes due to its advantages in cost, simple setup, as well as
availability in the environments where navigation with satellites is not
effective. However, camera motion and map points can be estimated only up to a
global scale factor with monocular vision. Moreover, estimation error
accumulates over time without bound, if the camera cannot detect the previously
observed map points for closing a loop. We propose an innovative approach to
estimate a global scale factor and reduce drifts in monocular vision-based
localization with an additional single ranging link. Our method can be easily
integrated with the back-end of monocular visual SLAM methods. We demonstrate
our algorithm with real datasets collected on a rover, and show the evaluation
results.
