RGB-D SLAM based on semantic information and geometric constraints in indoor dynamic scenes

In order to solve the shortcomings of traditional simultaneous localization and mapping in dynamic environment, which is interfered by moving objects, resulting in low accuracy and poor robustness, a visual simultaneous localization and mapping algorithm combining semantic information for motion detection was proposed. First, the SegNet deep neural network is used to extract the semantic information of the environment, and the prior knowledge is used to determine the static attribute objects and dynamic attribute objects. In the motion detection module, the feature points on the dynamic attribute objects are used to perform motion detection using geometric constraint relationships. Then the building module uses semantic information to build a semantic octo-Tree map. In order to analyse the effect of motion detection, a control experiment with a motion detection module removed was set up. Finally, experiments were conducted using TUM datasets, and the experimental results of the two schemes were compared and analysed.