AutoMine: An Unmanned Mine Dataset

Yuchen Li, Zixuan Li, Siyu Teng, Yu Zhang, Yuhang Zhou, Yuchang Zhu, Dongpu Cao, Bin Tian, Yunfeng Ai, Zhe Xuanyuan, Long Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 21308-21317

Autonomous driving datasets have played an important role in validating the advancement of intelligent vehicle algorithms including localization, perception and prediction in academic areas. However, current existing datasets pay more attention to the structured urban road, which hampers the exploration on unstructured special scenarios. Moreover, the open-pit mine is one of the typical representatives for them. Therefore, we introduce the Autonomous driving dataset on the Mining scene (AutoMine) for positioning and perception tasks in this paper. The AutoMine is collected by multiple acquisition platforms including an SUV, a wide-body mining truck and an ordinary mining truck, depending on the actual mine operation scenarios. The dataset consists of 18+ driving hours, 18K annotated lidar and image frames for 3D perception with various mines, time-of-the-day and weather conditions. The main contributions of the AutoMine dataset are as follows: 1.The first autonomous driving dataset for perception and localization in mine scenarios. 2.There are abundant dynamic obstacles of 9 degrees of freedom with large dimension difference (mining trucks and pedestrians) and extreme climatic conditions (the dust and snow) in the mining area. 3.Multi-platform acquisition strategies could capture mining data from multiple perspectives that fit the actual operation. More details can be found in our website(https://automine.cc).