SRVIO: Super Robust Visual Inertial Odometry for dynamic environments and challenging Loop-closure conditions

The visual localization or odometry problem is a well-known challenge in the field of autonomous robots and cars. Traditionally, this problem can ba tackled with the help of expensive sensors such as lidars. Nowadays, the leading research is on robust localization using economic sensors, such as cameras and IMUs. The geometric methods based on these sensors are pretty good in normal conditions withstable lighting and no dynamic objects. These methods suffer from significant loss and divergence in such challenging environments. The scientists came to use deep neural networks (DNNs) as the savior to mitigate this problem. The main idea behind using DNNs was to better understand the problem inside the data and overcome complex conditions (such as a dynamic object in front of the camera, extreme lighting conditions, keeping the track at high speeds, etc.) The prior endto-end DNN methods are able to overcome some of the mentioned challenges. However, no general and robust framework for all of these scenarios is available. In this paper, we have combined geometric and DNN based methods to have the pros of geometric SLAM frameworks and overcome the remaining challenges with the DNNs help. To do this, we have modified the Vins-Mono framework (the most robust and accurate framework till now) and we were able to achieve state-of-the-art results on TUM-Dynamic, TUM-VI, ADVIO and EuRoC datasets compared to geometric and end-to-end DNN based SLAMs. Our proposed framework was also able to achieve acceptable results on extreme simulated cases resembling the challenges mentioned earlier easy.