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Positioning Accuracy Optimization of DSO-SLAM and Its Use for the Localization of Unmanned Vehicle

In present work, Direct Sparse Odometry (DSO) of visual SLAM is selected as an auxiliary scheme for unmanned vehicles to solve the problem of poor positioning accuracy or inability to locate caused by multipath refraction. By extracting pixels and feature points jointly in the frontend, it can be ensured that the DSO maintained robustness in weak texture regions, while not omitting the feature corners with repeatability. To improve the positioning accuracy of DSO, the pose-graph optimization and loop closure detection with bag-of-words (BoW) are adopted. The optimized DSO has been compared with the original model in the dataset and verified by experiment in the campus scene. The results show that, the optimized algorithm can accurately detect loop closing, reduce the absolute trajectory error (ATE) of DSO by 61.26%, and greatly improve the positioning accuracy without sacrificing real-time and robustness. It verifies the validity of the DSO with loop closure detection, and provides a new method for the core positioning algorithm of driverless vehicles.