Accurate and robust localization is critical for autonomous driving. Even the state-of-the-art multi-sensor fusion platform (GNSS/INS/LiDAR/HD Map) could be severely challenged in the highly urbanized areas with tall buildings and dense traffic. Major challenges come from two parts: 1) excessive sensor outliers, such as GNSS outliers in dense urban, and 2) varying sensor error models as errors are not always subject to Gaussian distributions. The existing datasets for autonomous driving don’t necessarily demonstrate these challenges for localization. Therefore, we present a challenging multi-sensor integrated localization dataset. We traversed various scenarios with different degrees of urbanization in both Hong Kong (by Hong Kong Polytechnic University, Intelligent Positioning and Navigation lab) and California (University of California, Berkeley, the MSC lab). The measurements came from multiple sensors including a global navigation satellite system (GNSS), an inertial navigation system (INS), and light detection and ranging (LiDAR). The dataset is available online (please see the downloads). Furthermore, GNSS positioning performance in typical urban canyons and scenario urbanization definitions are also presented in the paper associated with this paper. The major contribution of this dataset is its diversity in providing challenging localization data from different scenes.
UrbanLoco Dataset
Major challenges for autonomous driving vehicle localization
GNSS positioning performance in typical urban canyons of Hong Kong
Sample use of the dataset
Citation:
To see more details please find the conference paper here. This work is finished by a team from Hong Kong Polytechnic University and the University of California, Berkeley
Weisong Wen, Yiyang Zhou, Guohao Zhang, Saman Fahandezh-Saadi, Xiwei Bai, Wei Zhan, Masayoshi Tomizuka, and Li-Ta Hsu, UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes (submitted), ICRA 2020, Paris, France.

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and is provided for non-commercial but academic use. If you are interested in using this dataset for commercial purposes, please contact us.

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