Dynamic SLAM: The Need For Speed

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Abstract. The static world assumption is standard in most simultaneous localisation and mapping (SLAM) algorithms. Increased deployment of autonomous systems to unstructured dynamic environments is driving a need to identify moving objects and estimate their velocity in real-time. Most existing SLAM based approaches rely on a database of 3D models of objects or impose significant motion constraints. In this paper, we propose a new feature-based, model-free, object-aware dynamic SLAM algorithm that exploits semantic segmentation to allow estimation of motion of rigid objects in a scene without the need to estimate the object poses or have any prior knowledge of their 3D models. The algorithm generates a map of dynamic and static structure and has the ability to extract velocities of rigid moving objects in the scene. Its performance is demonstrated on simulated, synthetic and real-world datasets.

DOI: 10.1109/ICRA40945.2020.9196895

Reference:

• M. Henein, J. Zhang, R. Mahony and V. Ila, “Dynamic SLAM: The Need For Speed,” 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020, pp. 2123-2129, doi: 10.1109/ICRA40945.2020.9196895.