  In the absence of reliable and accurate GPS, visual odometry (VO) has emerged
as an effective means of estimating the egomotion of robotic vehicles. Like any
dead-reckoning technique, VO suffers from unbounded accumulation of drift error
over time, but this accumulation can be limited by incorporating absolute
orientation information from, for example, a sun sensor. In this paper, we
leverage recent work on visual outdoor illumination estimation to show that
estimation error in a stereo VO pipeline can be reduced by inferring the sun
position from the same image stream used to compute VO, thereby gaining the
benefits of sun sensing without requiring a dedicated sun sensor or the sun to
be visible to the camera. We compare sun estimation methods based on
hand-crafted visual cues and Convolutional Neural Networks (CNNs) and
demonstrate our approach on a combined 7.8 km of urban driving from the popular
KITTI dataset, achieving up to a 43% reduction in translational average root
mean squared error (ARMSE) and a 59% reduction in final translational drift
error compared to pure VO alone.
