Fast Closed Loop Detection Method Based on Simplification Convolutional Neural Network
摘要
Loop closure detection methods based on deep learning can achieve good detection performance in complicate illumination environment.But the dimension of extracted scene feature is too high to achieve the real-time detection requirement for closed loop.For that,propose a loop closure detection method based on fast and lightweight convolutional neural network.Concatenated rectified linear unit,batch normalization and deep residual module are combined to design the fast and lightweight network model which is trained with the Places365-Standard data set.The scene feature is extracted by trained neural network model and the regions of closed loop are obtained by measuring the similarities of them.Results from the test show that compared with loop closure detection algorithms based on PlaceCNN and Autoencoder,the proposed method not only achieves the high correct rate but also increases the implementation speed for detection.