Research On VSLAM Loop Closure Detection Algorithm In Complex Environment

Visual Simultaneous Localization and Mapping(vSLAM for short)is a core technology for autonomous localization and navigation of mobile robots,which has been widely used in the fields of autonomous driving,smart home and aviation.As an important module of vSLAM system,loop closure detection can effectively reduce the cumulative error and correct the constructed map by confirming whether the robot has visited the previous location.The classical loop closure detection algorithm has the problems of low accuracy,long time consumption and poor robustness when applied in complex environment,which is not conducive to the application in actual scenes.In this paper,the following improved algorithm is proposed to solve the existing problems of vSLAM loop closure detection.For the problem that traditional algorithms are mostly based on artificial design features and have poor robustness for loop closure detection of complex scenes,a past-trained HybirdNet network model was used for feature extraction in this paper.The data set was derived from a complex environment with multi-factor changes such as light,weather and perspective.By comparing the performance of feature extraction with different network layers,the best performing Conv5 layer is selected to extract image features,which can effectively improve the robustness of the algorithm.According to the characteristics of the direct use of convolution neural network to extract,similarity calculation will cause the local spatial information leakage problems,part image fusion improved multi-scale attention learning mechanism and characteristics of VLAD image description method,the characteristics of the middle tier output figure is used to identify the interest area,to extract the effective features,finally to VLAD coding of effective features,To improve the expression ability of feature to image depth information,and achieve the purpose of improving the accuracy of the algorithm.In order to solve the problem that the complexity of image feature space based on convolutional neural network is too high,which leads to the time-consuming of feature matching,PC A dimension reduction method is introduced to eliminate the redundant information and noise in features,and cosine distance is used to calculate the similarity between scene features and realize loop closure detection.By comparing with other representative algorithms,the results show that the proposed algorithm has the highest average accuracy of 93.4%on Nordland data set,and the average accuracy of 85.2%,88.3% and 86.7% on Gardens Point three sub-data sets,respectively.Moreover,the time performance of the proposed algorithm is improved by about 27.4%,which can meet the requirements of vSLAM system for the accuracy and real-time performance of loop closure detection.Meanwhile,it is proved that the proposed algorithm has certain theoretical innovation and application value.