RoboSense：面向拥挤和非结构化环境中自我中心机器人感知与导航的大规模数据集与基准
RoboSense: Large-scale Dataset and Benchmark for Egocentric Robot Perception and Navigation in Crowded and Unstructured Environments
AI总结：
本文针对拥挤和非结构化环境下的自我中心机器人感知问题，搭建了多传感器平台并构建了大规模多模态数据集RoboSense，提出近场3D感知新匹配标准并规划6项任务提供基准，显著提升了近场障碍物标注规模。
摘要：
从自我中心视角进行可靠的具身感知具有挑战性，但对于智能移动智能体的自主导航技术至关重要。随着社会对社交机器人需求的不断增长，近场场景理解已成为与拥挤和非结构化环境导航相关的自我中心感知任务领域的重要研究课题。由于环境条件的复杂性以及因截断和遮挡导致的周围障碍物识别困难，在此情况下的感知能力仍然不足。为了进一步提升移动机器人的智能性，在本文中，我们搭建了一个基于3种主要传感器（Camera、LiDAR和Fisheye）的自我中心多传感器数据采集平台，该平台支持灵活的传感器配置，能够实现自我视角的动态视野，捕捉近处或更远的区域。同时，我们构建了一个名为RoboSense的大规模多模态数据集，以促进自我中心机器人感知。具体而言，RoboSense包含超过133K条同步数据，在完整的360度视图中标注了1.4M个3D边界框和ID，形成了跨越7.6K个时间序列的216K条轨迹。与之前为自动驾驶场景收集的数据集（如KITTI和nuScenes）相比，它在近场范围内对周围障碍物的标注量分别是它们的270倍和18倍。此外，我们为近场3D感知和预测指标定义了一种新颖的匹配标准。基于RoboSense，我们规划了6项流行任务以促进未来的研究发展，并相应地提供了详细的分析和基准。为保护隐私，已采取了数据脱敏措施。
英文摘要：
Reliable embodied perception from an egocentric perspective is challenging yet essential for autonomous navigation technology of intelligent mobile agents. With the growing demand of social robotics, near-field scene understanding becomes an important research topic in the areas of egocentric perceptual tasks related to navigation in both crowded and unstructured environments. Due to the complexity of environmental conditions and difficulty of surrounding obstacles owing to truncation and occlusion, the perception capability under this circumstance is still inferior. To further enhance the intelligence of mobile robots, in this paper, we setup an egocentric multi-sensor 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 from ego-perspective, capturing either near or farther areas. Meanwhile, a large-scale multimodal dataset is constructed, named RoboSense, to facilitate egocentric robot perception. Specifically, 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 surrounding obstacles within near ranges as the previous datasets collected for autonomous driving scenarios 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 research development, where the detailed analysis as well as benchmarks are also provided accordingly. Data desensitization measures have been conducted for privacy protection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
