Abstract
Visual simultaneous localization and mapping (SLAM) is the core of
intelligent robot navigation system. Many traditional SLAM algorithms
assume that the scene is static. When a dynamic object appears in the
environment, the accuracy of visual SLAM can degrade due to the
interference of dynamic features of moving objects. This strong
hypothesis limits the SLAM applications for service robot or driverless
car intherealdynamicenvironment.Inthispaper,adynamicobject removal
algorithm that combines object recognition and optical flow techniques is
proposed in the visual SLAM framework for dynamic scenes. The
experimental results show that our new method can detect moving object
effectively and improve the SLAM performance compared to the state of
the art methods.