A brain-inspired SLAM navigation based on speeded up robust features matching algorithm

Brain-inspired navigation algorithm controlled by simultaneous localization and mapping (SLAM) is prone to errors, primarily caused by complex environmental factors such as changes in light direction. To address this limitation, a brain-inspired SLAM approach is proposed where feature matching is supported by the speeded up robust features (SURF) algorithm. This model gathers environmental information via a set of mobile vision systems: the local view cell operators, designed to retrieve information about carrier direction/location via the SURF algorithm, and the head-direction cell and pose cell operators, which simultaneously represents current carrier position via continuous attractor neural networks. The position and time information retrieved by these cell operators are used to compute the position of the current carrier through path integration. In the final step, experience maps of the topology are constructed based on cognitive points. In addition, while the local view cell acquire environmental information, closed-loop detections are executed by the SURF algorithm to correct (if necessary) the current position. The optimized brain-inspired SLAM model successfully addresses the problem of scene matching errors faced by previous models in the presence of changing light direction. The effectiveness of the proposed method is validated through experimental verification.