A Visual Inertial SLAM Algorithm Based on Point and Line Feature Fusion
Aiming at the problem that feature point-oriented SLAM algorithms in indoor weak texture environments are difficult to extract features efficiently,and even fail to track enough features to cause motion estimation failure,a visual inertial SLAM algorithm that fuses point and line features is proposed.The algorithm improves the traditional LSD algorithm through a length suppression strategy and histogram equalization of the input image to improve the speed and quality of line feature extraction,and then uses the line features to assist the point features to provide additional constraints on the scene structure,and at the same time unites the IMU data to obtain a more accurate position estimation and reduce the impact of excessive motion speed and poor lighting conditions on the stability of the system.Experimental results comparing multiple publicly available datasets show that the method improves the positioning accuracy and robustness of the system in weak texture environments.
simultaneous localization and mappingweakly textured environmentspoint-line features
万方数据
维普
