  Autonomous vehicles and mobile robotic systems are typically equipped with
multiple sensors to provide redundancy. By integrating the observations from
different sensors, these mobile agents are able to perceive the environment and
estimate system states, e.g. locations and orientations. Although deep learning
approaches for multimodal odometry estimation and localization have gained
traction, they rarely focus on the issue of robust sensor fusion - a necessary
consideration to deal with noisy or incomplete sensor observations in the real
world. Moreover, current deep odometry models suffer from a lack of
interpretability. To this extent, we propose SelectFusion, an end-to-end
selective sensor fusion module which can be applied to useful pairs of sensor
modalities such as monocular images and inertial measurements, depth images and
LIDAR point clouds. Our model is a uniform framework that is not restricted to
specific modality or task. During prediction, the network is able to assess the
reliability of the latent features from different sensor modalities and
estimate trajectory both at scale and global pose. In particular, we propose
two fusion modules - a deterministic soft fusion and a stochastic hard fusion,
and offer a comprehensive study of the new strategies compared to trivial
direct fusion. We extensively evaluate all fusion strategies in both public
datasets and on progressively degraded datasets that present synthetic
occlusions, noisy and missing data and time misalignment between sensors, and
we investigate the effectiveness of the different fusion strategies in
attending the most reliable features, which in itself, provides insights into
the operation of the various models.
