Research On SLAM Loop Closure Detection Algorithm In Coal Mine Based On Deep Learning

In recent years,the coal mining industry has gradually become more automated and intelligent.As an important support in the field of robotics,visual SLAM has been widely used to carry out various work in coal mines.As a key component of visual SLAM,loop closure detection uses cameras to collect information about the surrounding environment to correct its own posture and help robots build a globally consistent environment map in coal mines.However,the existing loop closure detection research methods have problems such as poor robustness,low accuracy and long time.In order to meet the requirements of accuracy and real-time of downhole loop closure detection,the following research is carried out in this paper.(1)Aiming at the problems of low accuracy and poor robustness of traditional loop closure detection algorithms,a loop closure detection algorithm based on convolutional neural network is proposed.That is,the Faster RCNN network is used to replace the traditional hand-designed features to extract the image features of the coal mine data set.By comparing the performance of each convolutional layer to extract image features,the conv3 layer with stronger representation ability on each data set is selected as the image feature extraction.This improves the accuracy and robustness of loop closure detection.(2)Aiming at the problem of the loss of local feature information in the image features extracted by the network,a loop closure detection algorithm based on Faster RCNN-ROIs is proposed.That is,using the RPN network combined with the enhanced attention mechanism to cluster and fuse the image features extracted by the network to generate the local area of interest of the feature map.By extracting important information in the image,the accuracy of loop closure detection is further improved,but it cannot meet the requirements of real-time performance.(3)Aiming at the problem that the existing loop closure detection algorithms take too long in the process of image feature extraction and matching,a loop closure detection algorithm based on Faster RCNN-ROIs-LSH is proposed.That is,a hash function is constructed for the image features of the region of interest,and the locality-sensitive hashing algorithm is used to reduce the dimension of high-dimensional image features and build a hash table,which realizes the dimension reduction of high-dimensional features while ensuring high accuracy.Experiments show that after dimensionality reduction,the real-time performance of the algorithm in this paper is improved by 29.27%.Finally,the algorithm in this paper is compared with other algorithms on three sets of self-built coal mine underground data sets,which further proves that the algorithm in this paper is superior to other algorithms in terms of accuracy and real-time performance.To sum up,the loop closure detection algorithm based on Faster RCNN-ROIs-LSH proposed in this paper has excellent performance on the self-built coal mine underground data set,and to a certain extent improves the accuracy and real-time performance of the loop closure detection algorithm of SLAM underground coal mines.