| Simultaneous Localization and Mapping(SLAM)technology has broad application in military defense,aerospace and other fields.Among them,the technical solution based on visual sensor has the advantages of rich information and simple equipment,and stands out among many SLAM methods.Visual Odometry(VO)is the front-end part of the visual SLAM algorithm.Its theoretical basis is an incremental pose estimation scheme based on inter-frame matching.Therefore,there are inevitably defects such as accumulation of drift errors,often through loopback detection,etc.Back-end optimization means are corrected.However,many typical application scenarios do not have the conditions for reciprocating motion and cannot effectively perform loop closure detection and optimization.Therefore,exploring the optimization mechanism and scheme of visual odometry has become a hot research problem to be solved urgently in the field of visual SLAM.In recent years,the optimization solutions for visual odometry have been mainly focusing on the improvement of feature extraction and multi-source data fusion.Although these solutions can significantly improve the accuracy of visual positioning,they also suffer from many limitations,such as adding a positioning system.The applicability of the algorithm is significantly restricted by the complexity of the algorithm,adding auxiliary equipment and so on.Nevertheless,basic static assumptions required by traditional visual odometry are often breached by the time-varying outdoor environment background,which interferes with the inter-frame matching process.With the technology enhancement of the performance of graphics and image processing equipment,real-time simulation imaging rendering on mobile processors is now able to be achieved.Starting from making full use of the 3D point cloud data in the process of visual positioning,this paper conducts research from two aspects:(1)the imaging simulation method based on physical model and visual positioning optimization scheme fused with simulated images.The main research contents and contributions are as follows:1.An imaging simulation algorithm based on physical model is designed.Built on the 3D point cloud map constructed during the operation of the visual positioning algorithm,the surface modelling and material attribute matching of the target in the scene are performed.According to the ray tracing theory,the imaging simulation rendering of the target appearance is carried out,and the imaging simulation results are evaluated,which proves the consistency between the generated image quality and the actual captured image quality.2.A visual odometry method for generating images by fusing target features is proposed.The drift error correction is carried out according to the simulated image of the target characteristics generated in real time,which overcomes the interference caused by the background time-varying characteristics and effectively solves the problem of drift error accumulation.A filter based on the extended Kalman method is constructed to realize the optimal estimation of pose state quantities without loop closure detection.3.Developing a prototype system,building simulation scenarios for experimental verification,and gathering data sets under various scenarios for comparative experiments.The analysis shows that the method proposed in this paper effectively solves the drift error accumulation problem in the front-end part of the traditional algorithm,and the front-end positioning accuracy of the ORB-SLAM2 algorithm is significantly improved as well.The above research work provides a new solution for the application of the visual SLAM algorithm to the landing of carrier-based drones,the interactive docking of spacecraft on-orbit and Mars exploration missions without reciprocating motion or loop detection difficulties.It provides a new strategy for the research of visual odometry optimization scheme. |
