  Recent deep learning based visual simultaneous localization and mapping
(SLAM) methods have made significant progress. However, how to make full use of
visual information as well as better integrate with inertial measurement unit
(IMU) in visual SLAM has potential research value. This paper proposes a novel
deep SLAM network with dual visual factors. The basic idea is to integrate both
photometric factor and re-projection factor into the end-to-end differentiable
structure through multi-factor data association module. We show that the
proposed network dynamically learns and adjusts the confidence maps of both
visual factors and it can be further extended to include the IMU factors as
well. Extensive experiments validate that our proposed method significantly
outperforms the state-of-the-art methods on several public datasets, including
TartanAir, EuRoC and ETH3D-SLAM. Specifically, when dynamically fusing the
three factors together, the absolute trajectory error for both monocular and
stereo configurations on EuRoC dataset has reduced by 45.3% and 36.2%
respectively.
