基于thLSH的快速闭环检测方法

Fast closed loop detection method based on thLSH

同步定位与地图构建(SLAM)问题是实现移动机器人在未知环境中自我定位和导航的关键技术,具有重大的理论意义和研究价值。闭环检测是SLAM中的一个关键模块,对机器人实时更新地图和避免引入错误的地图节点起着关键作用。本文将视觉闭环检测问题看作是图像检索问题,基于深度学习的思想,将Rand Net神经网络应用到闭环检测,并利用基于阈值的局部敏感哈希算法对其提取的图像特征匹配过程进行加速。实验结果表明:本文所提出的基于th LSH的快速闭环检测方法,在保证高准确率的前提下,特征匹配速度提升了10倍,能够更好满足闭环检测的实时性需求。

The issue of Simultaneous Localization and Mapping（ SLAM） is a crucial technology for autonomous positioning and navigation of mobile robots in an unknown environment,and has theoretical significance and research value. Loop closure detection is a vital module in SLAM and plays an important role in updating the map in real time and avoiding the introduction of wrong map nodes. The loop detection vision problem in this thesis is regarded as an image retrieval issue,and based on the idea of deep learning,the RandNet neural network is applied to the loop detection problem,and a threshold-based locality-sensitive hashing algorithm is also used to accelerate the image feature matching process. The experimental results show that the fast loop detection method based on thLSH proposed in this paper can improve the feature extraction speed by more than 3 times on the premise of high accuracy,the speed of feature matching is 10 times higher,and it can meet the real-time requirement of loop detection.