A Dense Dynamic SLAM System Based on Motion Element Filtering

Abstract

The accuracy of camera pose estimation and map construction is an important factor on availability of path planning, which might be reduced due to the interference from dynamic objects. This paper proposes a dense dynamic SLAM system based on motion element filtering, which realizes the construction of background environment map and the restoration the motion trajectory of dynamic elements. Firstly, after detecting the moving objects in image frames by YOLOv5x algorithm, the system matches ORB feature points to find out the consistency in all objects. Then, based on the results of object detection and semantic segmentation, the dynamic and static feature points in the images are distinguished. After that, the system uses all the static feature points to estimate the pose of the sensor, and constructing a dense background OctoMap. Simultaneously, after estimating the relative pose of each moving object, a global dynamic visual odometry is built by restoring the motion trajectory and real-time inserting the corresponding 3D models in a point cloud space. The experimental results show that the proposed method has better localization effect than ORB-SLAM2 in the dynamic environment. And the system can densely recover background map matching around environment, and globally locate and track the dynamic elements, which retains the dynamic information in the scene.