  In the proposed study, we describe an approach to improving the computational
efficiency and robustness of visual SLAM algorithms on mobile robots with
multiple cameras and limited computational power by implementing an
intermediate layer between the cameras and the SLAM pipeline. In this layer,
the images are classified using a ResNet18-based neural network regarding their
applicability to the robot localization. The network is trained on a six-camera
dataset collected in the campus of the Skolkovo Institute of Science and
Technology (Skoltech). For training, we use the images and ORB features that
were successfully matched with subsequent frame of the same camera ("good"
keypoints or features). The results have shown that the network is able to
accurately determine the optimal images for ORB-SLAM2, and implementing the
proposed approach in the SLAM pipeline can help significantly increase the
number of images the SLAM algorithm can localize on, and improve the overall
robustness of visual SLAM. The experiments on operation time state that the
proposed approach is at least 6 times faster compared to using ORB extractor
and feature matcher when operated on CPU, and more than 30 times faster when
run on GPU. The network evaluation has shown at least 90% accuracy in
recognizing images with a big number of "good" ORB keypoints. The use of the
proposed approach allowed to maintain a high number of features throughout the
dataset by robustly switching from cameras with feature-poor streams.
