An Adaptive Visual Dynamic-SLAM Method Based on Fusing the Semantic Information

The SLAM problem in dynamic scenes is regarded as a challenge. This article proposes a novel SLAM framework for dynamic environments, which combines neural network and motion information of dynamic objects, making the system more adaptable to dynamic scenes. Specifically, we adopt a fast object detection network, tightly couple the results of the object detection with the geometric information in the SLAM system. Then the feature point extracted from the image is associated with a dynamic probability. By utilizing the feature points which tend to be static, the localization result can be greatly improved in dynamic environments. We perform the experiments both on the public data set and the real environment. The result can demonstrate that the proposed method greatly improves the localization accuracy in a dynamic environment. An open-source version of the source code is available.

Automated summary

This article proposes a novel SLAM framework for dynamic environments, which combines neural network and motion information of dynamic objects to make the system more adaptable to dynamic scenes. By tightly coupling the results of object detection with geometric information in the SLAM system, and associating feature points in frames with dynamic probabilities, the proposed method greatly improves the localization accuracy in dynamic environments.