Real-Time Robust Stereo Visual SLAM System Based on Bionic Eyes
Yanqing Liu, Dongchen Zhu, Jingquan Peng, Xianshun Wang, Lei Wang, Lili Chen, Jiamao Li, Xiaolin Zhang
Abstract
Visual Simultaneously Localization and Mapping (SLAM) systems obtain accurate ego-motion of cameras and 3D representation of the environment. Although the performance of SLAM systems is very impressive in many common scenes, tracking failure is still a very challenging issue, which always exist in low-textured environment and rapid camera motion situations. In this paper, we propose a real-time stereo visual SLAM system using bionic eyes. The new type sensor named bionic eyes are basically the stereo cameras and perform all the movements of human eyes. Our system makes a good combination of sensors and algorithms. In order to find more feature points, we proposed an autonomous searching strategy of the bionic eyes, which is mainly inspired by the peripheral and central visual of human eyes. After that the camera motion is calculated using a robust motion estimation method. Our system is tested in indoor low-textured and large-scale outdoor environment. Experimental results show that compared to fixed stereo cameras, our bionic eyes SLAM system gains more robustness and avoid tracking failure problem when facing the low-textured environment.