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.

Autonomous navigation is a key technology for unmanned motion platforms to perform their tasks smoothly. The current approaches for daytime polarization navigation have been extensively researched. However, the polarization light intensity is the fundamental information within the polarization image, and the light intensity at night is 6–8 orders of magnitude lower than that during the day, which increase the noise and the loss of local polarization information due to occlusion, resulting in a significant decrease in the polarization orientation accuracy. Aimed at the problem, a bio-inspired model is introduced to denoise and enhance weak nighttime polarization patterns. Further, to address the issue of outlier interference in the occluded environment during practical application, a fast-fitting method of the solar meridian based on the anti-symmetric distribution of the polarization angle adjusted by Proportional and Differential (PD) control is proposed. The experimental results show that the method proposed in this paper achieves a dynamic orientation error Root Mean Square Error (RMSE) of 0.7° in the weak polarization mode at night and in the presence of local occlusion. The proposed method has strong robustness under weak polarization occlusion at night, and the orientation accuracy is improved by 97% and 80% in comparison to the least squares method, which provides a new method for polarization navigation at night. This effectively improves the robustness and environmental applicability of the bionic polarization compass for nighttime applications.