Improved VSLAM Algorithm Based on Fast Visual Odometry and Large Loop Local Optimization Model

An improved visual simultaneous localization and mapping (VSLAM) algorithm based on fast visual odometry and large loop local optimization model is proposed in terms of the accuracy and real-time performance of autonomous localization in mobile robot VSLAM.First of all, the error function of color GICP (color supported generalized iterative closest point) is improved based on the uncertainty analysis on the feature points.Frame-to-model approach is utilized to achieve fast registration between data sets and model sets.And the model sets are updated through Kalman filtering and the weighting method.The accuracy of pose estimation is improved through the above steps.Secondly, a large local loop optimization model based on model-to-model registration is proposed and g²o is combined to optimize the accumulated error of the pose estimation quickly, which improves the accuracy and efficiency of autonomous localization further.The offline contrast experiments based on the datasets and the online experiments based on actual scenes show that, with the proposed algorithm, not only the accuracy of autonomous localization and map in mobile robot VSLAM are improved effectively, but also the real-time performance is guaranteed.