Performance Analysis of ORB-SLAM in Foggy Environments

Vision based localization approaches, be Simultaneous Localization and Mapping (SLAM) or Visual Odometry (VO), rely heavily on distinct features detectable and trackable across different frames. Therefore, state of the art approaches utilize features that are scale-invariant, visible from different points of view, and are also tolerant to changes in light. However, visibility of the feature points are affected also by haze, mist or fog, which are atmospheric phenomena that vary across the day, effectively hindering the performance of vision based SLAM/VO approaches. In this work, we study the effect of fog on SLAM, particularly ORB-SLAM. We analyze the changes in the quality and quantity of the features with varying fog levels, as well as the quality of the eventual path generated by SLAM. We also show that performance of SLAM in foggy conditions can be improved by defogging the images, though only to a limited extent depending on the amount of fog in the environment.