Deep Learning-Based Localization and Perception Systems: Approaches for Autonomous Cargo Transportation Vehicles in Large-Scale, Semiclosed Environments,IEEE Robotics & Automation Magazine

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Deep Learning-Based Localization and Perception Systems: Approaches for Autonomous Cargo Transportation Vehicles in Large-Scale, Semiclosed Environments
IEEE Robotics & Automation Magazine ( IF 5.4 ) Pub Date : 2020-04-09 , DOI: 10.1109/mra.2020.2977290
Zhe Liu , Chuanzhe Suo , Yingtian Liu , Yueling Shen , Zhijian Qiao , Huanshu Wei , Shunbo Zhou , Haoang Li , Xinwu Liang , Hesheng Wang , Yun-Hui Liu

Vehicles capable of delivering heavy cargoes are a major means for the promotion of social productivity and are widely applied in industry. Even though self-driving technologies have been studied for a few decades and several successful applications have been demonstrated, autonomous industry vehicles are currently applied only in some specific scenarios with very low speed and fixed routes and are typically implemented in the indoor closed area of small-scale warehouses. In this article, we introduce the main perception and localization approaches of autonomous cargo transportation vehicles for industrial applications. We also demonstrate how these technologies can enable autonomous cargo transportation in the Hong Kong International Airport.

更新日期：2020-04-09

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