Feature-based visual odometry prior for real-time semi-dense stereo SLAM
This work addresses the problem of robust and fast motion estimation and mapping for autonomous mobile robots, offering an incremental improvement over existing methods.
The paper tackles the challenge of real-time stereo SLAM by proposing a two-layer approach that combines feature-based matching with semi-dense direct image alignment, resulting in faster performance than state-of-the-art methods without losing accuracy and improved robustness to large inter-frame motion and illumination changes.
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.