FusionPortable: A Multi-Sensor Campus-Scene Dataset for Evaluation of Localization and Mapping Accuracy on Diverse Platforms

Jianhao Jiao1; Hexiang Wei2; Tianshuai Hu2; Xiangcheng Hu2; Yilong Zhu2; Zhijian He2; Jin Wu2; Jingwen Yu3; Xupeng Xie2; Huaiyang Huang2; Ruoyu Geng2; Lujia Wang1; Ming Liu4

10.1109/IROS47612.2022.9982119

2022

2153-0858

978-1-6654-7928-8

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

3851-3856

23-27 Oct. 2022

Kyoto, Japan

Combining multiple sensors enables a robot to maximize its perceptual awareness of environments and enhance its robustness to external disturbance, crucial to robotic navigation. This paper proposes the FusionPortable benchmark, a complete multi-sensor dataset with a diverse set of sequences for mobile robots. This paper presents three contributions. We first advance a portable and versatile multi-sensor suite that offers rich sensory measurements: 10Hz LiDAR point clouds, 20Hz stereo frame images, high-rate and asynchronous events from stereo event cameras, 200Hz inertial readings from an IMU, and 10Hz GPS signal. Sensors are already temporally synchronized in hardware. This device is lightweight, self-contained, and has plug-and-play support for mobile robots. Second, we construct a dataset by collecting 17 sequences that cover a variety of environments on the campus by exploiting multiple robot platforms for data collection. Some sequences are challenging to existing SLAM algorithms. Third, we provide ground truth for the decouple localization and mapping performance evaluation. We additionally evaluate state-of-the-art SLAM approaches and identify their limitations. The dataset, consisting of raw sensor measurements, ground truth, calibration data, and evaluated algorithms, will be released.

Location awareness Point cloud compression Performance evaluation Simultaneous localization and mapping Robot vision systems Benchmark testing Robustness

其他

IEEE

1.Clear Water Bay Institute of Autonomous Driving, Hong Kong, China
2.The Hong Kong University of Science and Technology, Hong Kong, China
3.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
4.HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen, China

Jianhao Jiao,Hexiang Wei,Tianshuai Hu,et al. FusionPortable: A Multi-Sensor Campus-Scene Dataset for Evaluation of Localization and Mapping Accuracy on Diverse Platforms[C],2022:3851-3856.