SDPL-SLAM: Introducing Lines in Dynamic Visual SLAM and Multi-Object Tracking

The need for a robust visual SLAM system operating in real human environments has led to the gradual abandonment of the static world assumption and to the creation of many dynamic SLAM algorithms. Even though there have been many dynamic SLAM proposals, the vast majority of them relied on point features. However, research in static SLAM systems has demonstrated that the use of more complex geometric shapes such as lines can improve performance. Motivated by this we have created a new dynamic SLAM system that estimates the camera poses and the motion of rigid objects, by exploiting both static and dynamic points and lines. Line segments have been incorporated in a novel way in every aspect of our algorithm, by improving their correspondences through optical flow refinement, and by introducing line error terms in both camera and object motion, and in batch optimization. Our proposal has been tested extensively in indoor and outdoor datasets and has achieved significant improvement compared to other state-of-the-art dynamic SLAM systems. Our results demonstrated that line segments enhanced the robustness, thus contributing towards a fully operational SLAM system.Code is publicly available^*.