DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation

• URL: http://arxiv.org/abs/2003.04569v1
• Date: Tue, 10 Mar 2020 08:25:21 GMT
• Title: DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation
• Authors: Chenjie Wang and Bin Luo and Yun Zhang and Qing Zhao and Lu Yin and Wei Wang and Xin Su and Yajun Wang and Chengyuan Li
• Abstract summary: DymSLAM is a dynamic stereo visual SLAM system capable of reconstructing a 4D (3D + time) dynamic scene with rigid moving objects. The proposed system allows the robot to be employed for high-level tasks, such as obstacle avoidance for dynamic objects.
• Score: 22.444657614883084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most SLAM algorithms are based on the assumption that the scene is static. However, in practice, most scenes are dynamic which usually contains moving objects, these methods are not suitable. In this paper, we introduce DymSLAM, a dynamic stereo visual SLAM system being capable of reconstructing a 4D (3D + time) dynamic scene with rigid moving objects. The only input of DymSLAM is stereo video, and its output includes a dense map of the static environment, 3D model of the moving objects and the trajectories of the camera and the moving objects. We at first detect and match the interesting points between successive frames by using traditional SLAM methods. Then the interesting points belonging to different motion models (including ego-motion and motion models of rigid moving objects) are segmented by a multi-model fitting approach. Based on the interesting points belonging to the ego-motion, we are able to estimate the trajectory of the camera and reconstruct the static background. The interesting points belonging to the motion models of rigid moving objects are then used to estimate their relative motion models to the camera and reconstruct the 3D models of the objects. We then transform the relative motion to the trajectories of the moving objects in the global reference frame. Finally, we then fuse the 3D models of the moving objects into the 3D map of the environment by considering their motion trajectories to obtain a 4D (3D+time) sequence. DymSLAM obtains information about the dynamic objects instead of ignoring them and is suitable for unknown rigid objects. Hence, the proposed system allows the robot to be employed for high-level tasks, such as obstacle avoidance for dynamic objects. We conducted experiments in a real-world environment where both the camera and the objects were moving in a wide range.

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