  We propose a neurobiologically inspired visual simultaneous localization and
mapping (SLAM) system based on direction sparse method to real-time build
cognitive maps of large-scale environments from a moving stereo camera. The
core SLAM system mainly comprises a Bayesian attractor network, which utilizes
neural responses of head direction (HD) cells in the hippocampus and grid cells
in the medial entorhinal cortex (MEC) to represent the head direction and the
position of the robot in the environment, respectively. Direct sparse method is
employed to accurately and robustly estimate velocity information from a stereo
camera. Input rotational and translational velocities are integrated by the HD
cell and grid cell networks, respectively. We demonstrated our
neurobiologically inspired stereo visual SLAM system on the KITTI odometry
benchmark datasets. Our proposed SLAM system is robust to real-time build a
coherent semi-metric topological map from a stereo camera. Qualitative
evaluation on cognitive maps shows that our proposed neurobiologically inspired
stereo visual SLAM system outperforms our previous brain-inspired algorithms
and the neurobiologically inspired monocular visual SLAM system both in terms
of tracking accuracy and robustness, which is closer to the traditional
state-of-the-art one.
