Inspired by rodents’ ability to navigate freely in a given space, bionavigation systems provide alternatives to traditional probabilistic solutions. This paper proposed a bionic path planning method based on RatSLAM to provide a novel viewpoint for robots to make a more flexible and intelligent navigation scheme. A neural network fusing historic episodic memory was proposed to improve the connectivity of the episodic cognitive map. It is biomimetically important to generate an episodic cognitive map and establish a one-to-one correspondence between the events generated by episodic memory and the visual template of RatSLAM. The episodic cognitive map can be improved by imitating the rodents’ behavior of memory fusion to produce better path planning results. The experimental results of different scenarios illustrate that the proposed method identified the connectivity between way points, optimized the result of path planning, and improved the flexibility of the system.

Event cameras produce asynchronous discrete outputs due to the independent response of camera pixels to changes in brightness. The asynchronous and discrete nature of event data facilitate the tracking of prolonged feature trajectories. Nonetheless, this necessitates the adaptation of feature tracking techniques to efficiently process this type of data. In addressing this challenge, we proposed a hybrid data-driven feature tracking method that utilizes data from both event cameras and frame-based cameras to track features asynchronously. It mainly includes patch initialization, patch optimization, and patch association modules. In the patch initialization module, FAST corners are detected in frame images, providing points responsive to local brightness changes. The patch association module introduces a nearest-neighbor (NN) algorithm to filter new feature points effectively. The patch optimization module assesses optimization quality for tracking quality monitoring. We evaluate the tracking accuracy and robustness of our method using public and self-collected datasets, focusing on average tracking error and feature age. In contrast to the event-based Kanade–Lucas–Tomasi tracker method, our method decreases the average tracking error ranging from 1.3 to 29.2% and boosts the feature age ranging from 9.6 to 32.1%, while ensuring the computational efficiency improvement of 1.2–7.6%. Thus, our proposed feature tracking method utilizes the unique characteristics of event cameras and traditional cameras to deliver a robust and efficient tracking system.

In recent years, various computational models have been proposed in neuroscience, but only a few have been utilized in robot visual navigation through engineering approaches. Besides, these engineering methods lack environmental adaptability, especially in the aspect of visual place recognition in weak or uncontrollable light fluctuations. To address this issue and enhance the performance of visual template matching and map loop closure detection in challenging lighting environments, this paper proposed a bionic visual navigation model that combines two neural network architectures, the Pulse Coupled Neural Network (PCNN) and Continuous Attractor Neural Network (CANN). In our navigation model, the visual features of the environments are encoded as temporal information by the spiking model and input into the local view cells for visual template matching. Additionally, we utilize the pose cells network, which incorporates the similarity between current and previous templates, along with odometry data, to encode spatial information and store it as an experience map. To validate the effectiveness of the proposed model, we conducted evaluations on datasets collected within our library, campus, and an open-source office dataset. The experimental results reveal that our algorithm increases the F1-score of template matching by approximately 10.5 $$ \% $$% , 35.8 $$ \% $$% , 61.7 $$ \% $$% , and 1.9 $$ \% $$% in each dataset, compared to the conventional RatSLAM method. Furthermore, our algorithm generates a more accurate map that closely correlates with the real-world trajectory without compromising on computation time. The results suggest that our bionic visual navigation model is reliable for both standard and extreme lighting conditions.