AD-VO: Scale-Resilient Visual Odometry Using Attentive Disparity Map
This addresses scale resilience in visual odometry for SLAM systems, though it appears incremental as it builds on existing learning-based approaches.
The paper tackles the problem of monocular visual odometry requiring manual tuning for environmental changes by proposing a learning-based approach using disparity maps, achieving better and more stable performance than conventional methods.
Visual odometry is an essential key for a localization module in SLAM systems. However, previous methods require tuning the system to adapt environment changes. In this paper, we propose a learning-based approach for frame-to-frame monocular visual odometry estimation. The proposed network is only learned by disparity maps for not only covering the environment changes but also solving the scale problem. Furthermore, attention block and skip-ordering scheme are introduced to achieve robust performance in various driving environment. Our network is compared with the conventional methods which use common domain such as color or optical flow. Experimental results confirm that the proposed network shows better performance than other approaches with higher and more stable results.