  Robust and fast motion estimation and mapping is a key prerequisite for
autonomous operation of mobile robots. The goal of performing this task solely
on a stereo pair of video cameras is highly demanding and bears conflicting
objectives: on one hand, the motion has to be tracked fast and reliably, on the
other hand, high-level functions like navigation and obstacle avoidance depend
crucially on a complete and accurate environment representation. In this work,
we propose a two-layer approach for visual odometry and SLAM with stereo
cameras that runs in real-time and combines feature-based matching with
semi-dense direct image alignment. Our method initializes semi-dense depth
estimation, which is computationally expensive, from motion that is tracked by
a fast but robust keypoint-based method. Experiments on public benchmark and
proprietary datasets show that our approach is faster than state-of-the-art
methods without losing accuracy and yields comparable map building
capabilities. Moreover, our approach is shown to handle large inter-frame
motion and illumination changes much more robustly than its direct
counterparts.
