Dynamic SLAM: A Visual SLAM in Outdoor Dynamic Scenes

Simultaneous localization and mapping (SLAM) has been widely used in augmented reality (AR), virtual reality (VR), robotics, and autonomous vehicles as the theoretical basis for robots to perceive their environment. Most popular SLAM algorithms assume that objects in the scene are static. Solving dynamic problems in SLAM is now attracting increasing attention. In this article, we propose a method that combines semantic segmentation information and spatial motion information of associated pixels to cope with dynamic objects based on ORB-SLAM2. We add a deep segmentation network SegNet to segment input image and obtain the semantic information for each feature point. Next, the spatial velocity of feature points between adjacent frames is calculated assuming uniform motion. Finally, the two parts are fused for the final judgment, and the dynamic feature points are removed to improve positioning accuracy. We evaluate our SLAM algorithms using the public KITTI dataset. The proposed algorithm has a similar overall accuracy level to ORB-SLAM2, but it is more accurate in sequences with many dynamic objects. On KITTI's raw data sequence containing multiple dynamic objects, our pipeline achieves the best performance, improving 39.5% compared with the original ORB-SLAM2 system. We compare our algorithm with other state-of-the-art SLAM systems used to cope with dynamic environments. The results show that the proposed algorithm has a better performance.