  There has been extensive research on visual localization and odometry for
autonomous robots and virtual reality during the past decades. Traditionally,
this problem has been solved with the help of expensive sensors, such as
lidars. Nowadays, the focus of the leading research in this field is on robust
localization using more economic sensors, such as cameras and IMUs.
Consequently, geometric visual localization methods have become more accurate
in time. However, these methods still suffer from significant loss and
divergence in challenging environments, such as a room full of moving people.
Scientists started using deep neural networks (DNNs) to mitigate this problem.
The main idea behind using DNNs is to better understand challenging aspects of
the data and overcome complex conditions such as the movement of a dynamic
object in front of the camera that covers the full view of the camera, extreme
lighting conditions, and high speed of the camera. Prior end-to-end DNN methods
have overcome some of these challenges. However, no general and robust
framework is available to overcome all challenges together. In this paper, we
have combined geometric and DNN-based methods to have the generality and speed
of geometric SLAM frameworks and overcome most of these challenging conditions
with the help of DNNs and deliver the most robust framework so far. To do so,
we have designed a framework based on Vins-Mono, and show that it is 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 could also achieve outstanding results on extreme simulated cases
resembling the aforementioned challenges.
