A Closed-loop Detection Algorithm Based on Dynamic Time Warping - ZHU Tian; PENG Zhichuan; ZHANG Zhiteng; ZHU Zemin; XIE Yongbo; WANG Wenming (Kongzhi Yu Xinxi Jishu)
In the research of closed-loop detection, algorithms based on single image matching are drawing great research interests. However, in dynamic environment, such algorithms can not meet the requirements of dynamic detection.Considering that a single image is correlated in space and time, this paper proposes a closed-loop detection algorithm based on dynamic time warping.This method solves the similarity of image sequences by dynamic time-warping algorithm, instead of solving the similarity between single images. At the same time, it uses convolutional neural network to extract image features and the results are more advanced and abstract, and this enhances the robustness of the algorithm. Considering the influence of matching sequence length on the experimental results, the quasi-call curves and ROC curves obtained under different sequence lengths are compared in detail to determine the most suitable matching sequence length. Experiments show that the loop closure detection algorithm based on dynamic time warping algorithm has higher accuracy and recall rate, and the false positive probability is lower. Compared with the mainstream methods such as FabMap and SeqSLAM, the performance of the loop closure detection algorithm is better.