A biologically inspired solution to simultaneous localization and consistent mapping in dynamic environments

Simultaneous localization and consistent mapping in dynamic environments is a fundamental and unsolved problem in the mobile robotics community. Most of the algorithms for this problem heavily rely on discriminating dynamic objects from static objects. Because these recursive filters based discrimination algorithms always have lag before the model selection parameters converge to the steady states, they have a period of time that the filter could identify a dynamic target as static or vice versa. Mis-classifications decrease precision and consistence, and induce filter divergence.A brain interacts with dynamic environments. The biological basis of this adaptability is provided by the connectivity and the dynamic properties of neurons. Biologically inspired by the adaptability, the paper proposes a shunting STM (Short Term Memory) based method to solve the simultaneous localization and consistent mapping problem, especially in dynamic environments. The proposed method utilizes a shunting STM neural network to represent environments and to probabilistically reflect the probability of existence of an object; it adapts a scan matching scheme to localize robot based on the map representation. Dynamic properties of the neural network are used to reflect environmental changes, therefore, the proposed method does not require explicit discrimination of objects. As a result, the proposed method does not have the lag of convergence, and it has high utilization ratio of observation information. Theoretical analyses in the paper show the proposed method has Lyapunov stability and its computational complexity does not depend on the size of the environment. The paper compares the proposed method with the classification based Extend Kalman Filter on a classical outdoor dataset, in simulated environments and in real indoor environments. The results show the proposed method outperforms the classification based EKF on precision and consistence in both static environments and dynamic environments.