RoboSense: Large-scale Dataset and Benchmark for Multi-sensor Low-speed Autonomous Driving
<|reference_start|>RoboSense: Large-scale Dataset and Benchmark for Multi-sensor Low-speed Autonomous Driving: Robust object detection and tracking under arbitrary sight of view is challenging yet essential for the development of Autonomous Vehicle technology. With the growing demand of unmanned function vehicles, near-field scene understanding becomes an important research topic in the areas of low-speed autonomous driving. Due to the complexity of driving conditions and diversity of near obstacles such as blind spots and high occlusion, the perception capability of near-field environment is still inferior than its farther counterpart. To further enhance the intelligent ability of unmanned vehicles, in this paper, we construct a multimodal data collection platform based on 3 main types of sensors (Camera, LiDAR and Fisheye), which supports flexible sensor configurations to enable dynamic sight of view for ego vehicle, either global view or local view. Meanwhile, a large-scale multi-sensor dataset is built, named RoboSense, to facilitate near-field scene understanding. RoboSense contains more than 133K synchronized data with 1.4M 3D bounding box and IDs annotated in the full $360^{\circ}$ view, forming 216K trajectories across 7.6K temporal sequences. It has $270\times$ and $18\times$ as many annotations of near-field obstacles within 5$m$ as the previous single-vehicle datasets such as KITTI and nuScenes. Moreover, we define a novel matching criterion for near-field 3D perception and prediction metrics. Based on RoboSense, we formulate 6 popular tasks to facilitate the future development of related research, where the detailed data analysis as well as benchmarks are also provided accordingly. Code and dataset will be available at https://github.com/suhaisheng/RoboSense.<|reference_end|>
@article{su2024robosense:,
    title={RoboSense: Large-scale Dataset and Benchmark for Multi-sensor Low-speed
  Autonomous Driving},
    author={Haisheng Su, Feixiang Song, Cong Ma, Wei Wu, Junchi Yan},
    journal={arXiv preprint arXiv:2408.15503},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15503},
    primaryClass={cs.CV cs.AI}
}