Abstract
Most of the existed Simultaneous Localization and Mapping solutions cannot work in dynamic environments since the dynamic objects lead to wrong uncertain feature associations. In this paper, we involved a learning-based object classification front end to recognize and remove the dynamic object, and thereby ensure our ego-motion estimator’s robustness in high dynamic environments. The static backgrounds are used for static environment reconstruction, the extracted dynamic human objects are used for human object tracking and reconstruction. Experimental results show that the proposed approach can provide not only accurate environment maps but also well-reconstructed moving humans.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (Grant No. 61473027), Beijing Key Laboratory of Robot Bionics and Function Research (Grant No. BZ0337) and Beijing Advanced Innovation Center for Intelligent Robots and Systems.
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Zhang, H., Zhang, T., Zhang, L. (2021). Model-Based Dynamic Human Tracking and Reconstruction During Dynamic SLAM. In: Venture, G., Solis, J., Takeda, Y., Konno, A. (eds) ROMANSY 23 - Robot Design, Dynamics and Control. ROMANSY 2020. CISM International Centre for Mechanical Sciences, vol 601. Springer, Cham. https://doi.org/10.1007/978-3-030-58380-4_2
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DOI: https://doi.org/10.1007/978-3-030-58380-4_2
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