A Hybrid Loop Closure Detection Method Based on Brain-Inspired Models

Various hippocampal-entorhinal-based models are used to construct brain-inspired simultaneous localization and mapping (SLAM) systems. Loop closure detection (LCD) is a critical process of SLAM systems for robots to relocalize themselves and correct accumulative errors. The existing LCD methods of brain-inspired SLAM systems cannot solve well with challenging or large-scale environments by hand-crafted features and brute force search strategy. In this article, we propose a hybrid LCD method, which is based on the convolutional neural network (CNN) features and the fly’s locality-sensitive hashing algorithm (Fly-LSH). CNN features can improve the reliability of image matching results, and the Fly-LSH, a nearest neighbor search method derived from the fruit fly olfactory circuit, is used to accelerate the image processing. We use multiple hash tables to make a balance between the image matching time and the accuracy of loop closures. The proposed method provides a general approach for SLAM systems to process visual cues, and has application in a hippocampal-entorhinal-based SLAM system. The experimental results verify that the proposed method enables the system to build cognitive maps with better robustness and efficiency.