Compared to roadways with structured characteristics, sidewalk scenarios in urban environments are often more complex and variable, and are also easily encountered with special and unknown challenges. The Continental-NTU Delivery Robotic Platform dataset was constructed to provide a class of multimodal sensor information sets that can be used for sidewalk navigation in unstructured environments, and to provide validation support for navigation and decision-making algorithms of last-mile delivery robots. The sensor information in the released dataset not only covers 3D LiDAR, camera, and inertial measurement unit data for sidewalk navigation, but also provides ground truth odometry and mesh data for verification and computer simulation, respectively. This dataset was collected in real-time by the prototype food delivery robot platform developed by Continental and organized in JSON file format. In addition, eight routes that can be used for food delivery tasks were selected for recording in the campus environment of Nanyang Technological University. In this paper, the composition of this dataset is described in detail. Furthermore, a variety of existing SLAM algorithms are used on the basis of this dataset for validating reliability and providing benchmarks. The website for access: https://ntu-conti-a2.github.io/Continental-NTU-Dataset.


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    Title :

    Continental-NTU Delivery Robot Dataset for Perception and Navigation on Footpath


    Contributors:
    Kee, Da Wei (author) / Zhang, Jun (author) / Li, Heshan (author) / Peng, Guohao (author) / Zheng, Lunan (author) / Chia, Timothy (author) / Pan, Shu Ting (author) / Wen, Mingxing (author) / Ma, Yingchong (author) / Wang, Danwei (author)


    Publication date :

    2023-09-24


    Size :

    7612612 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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