STMVO: biologically inspired monocular visual odometry

Visual odometry (VO) is a fundamental and challenging problem in both the computer vision community and the robotics community. VO refers the process of recovering the relative movements of a camera by analyzing the associated image sequence. While VO is generally formulated as descriptors-based feature tracking with outliers rejection and global optimization, these algorithms are not only computationally expensive but also lack robustness. In the paper, a biologically inspired solution to the monocular visual odometry problem was presented, which was named as shunting short-term memory monocular visual odometry. The proposed method is simple and concise in both concept and implementation. To be more specific, it utilizes the shunting short-term memory to represent the key frames and the latest observations and also to adapt to uncertainties and ambiguities. And scan matching scheme is adopted to search the movement that best explained the difference between the latest observation and the key frame. Because of the dynamic properties of the neural network, the proposed method requires neither explicit extraction of features and descriptors, nor outliers detection and bundle optimization. Theoretical analysis in the paper showed that the proposed method has Lyapunov stability and constant computational complexity. The proposed method was also compared with the classical monocular VO algorithm in real indoor environments, and the experimental results proved that the proposed method outperforms the classical method on both effectiveness and robustness.