  Motion blur is one of the major challenges remaining for visual odometry
methods. In low-light conditions where longer exposure times are necessary,
motion blur can appear even for relatively slow camera motions. In this paper
we present a novel hybrid visual odometry pipeline with direct approach that
explicitly models and estimates the camera's local trajectory within the
exposure time. This allows us to actively compensate for any motion blur that
occurs due to the camera motion. In addition, we also contribute a novel
benchmarking dataset for motion blur aware visual odometry. In experiments we
show that by directly modeling the image formation process, we are able to
improve robustness of the visual odometry, while keeping comparable accuracy as
that for images without motion blur.
