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arXiv · 2206.15102

DynamicFilter: an Online Dynamic Objects Removal Framework for Highly Dynamic Environments

Tingxiang Fan·Bowen Shen·Hua Chen·Wei Zhang·Jia Pan
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Abstract

Emergence of massive dynamic objects will diversify spatial structures when robots navigate in urban environments. Therefore, the online removal of dynamic objects is critical. In this paper, we introduce a novel online removal framework for highly dynamic urban environments. The framework consists of the scan-to-map front-end and the map-to-map back-end modules. Both the front- and back-ends deeply integrate the visibility-based approach and map-based approach. The experiments validate the framework in highly dynamic simulation scenarios and real-world datasets.

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Tingxiang Fan, Bowen Shen, Hua Chen, Wei Zhang, Jia Pan. 2022-06-30. DynamicFilter: an Online Dynamic Objects Removal Framework for Highly Dynamic Environments. https://arxiv.org/abs/2206.15102

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