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

被引：12
|
作者：
Fan, Tingxiang [1 ]
Shen, Bowen [2 ]
Chen, Hua [2 ]
Zhang, Wei [2 ]
Pan, Jia [1 ]
机构：
[1] Univ Hong Kong, Hong Kong, Peoples R China
[2] Southern Univ Sci & Technol, Shenzhen, Peoples R China
来源：
2022 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, ICRA 2022 | 2022年
基金：
中国国家自然科学基金;
关键词：
D O I：
10.1109/ICRA46639.2022.9812356
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 dataset.
引用
收藏
页码：7988 / 7994
页数：7
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