Visual SLAM Based on Dynamic Object Removal 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 in the real dynamic environment. In this paper, a dynamic object 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.

Robust Realtime RGBD Visual Odometry in Dynamic Environments via Rigid Motion Model In the paper, we propose a robust realtime visual odometry in dynamic environments via rigidmotion model updated by scene flow. The proposed algorithm consists of spatial motion segmentation and temporal motion tracking. The spatial segmentation first generates several motion hypotheses by using a gridbased scene flow and clusters the extracted motion hypotheses, separating objects that move independently of one another. Further, we use a dualmode motion model to consistently distinguish between the static and dynamic parts in the temporal motion tracking stage. Finally, the proposed algorithm estimates the pose of a camera by taking advantage of the region classified as static parts. In order to evaluate the performance of visual odometry under the existence of dynamic rigid objects, we use selfcollected dataset containing RGBD images and motion capture data for groundtruth. We compare our algorithm with stateoftheart visual odometry algorithms. The validation results suggest that the proposed algorithm can estimate the pose of a camera robustly and accurately in dynamic environments.