用低成本激光雷达实现机器人对人的全向感知，精度高且保护隐私
Sixth-Sense: Self-Supervised Learning of Spatial Awareness of Humans from a Planar Lidar
- 自监督学习，用RGB-D数据指导1D激光雷达的人体检测与姿态估计
- 在未见环境中实现71%精确率、80%召回率，距离误差13cm，方向误差44°
- 适合低算力、需保护隐私的社交服务机器人，部署于多个公共场景
可靠的人员定位是服务型与社交机器人与人类密切交互的基础。现有先进人体检测器多依赖RGB-D相机或昂贵的3D激光雷达，但多数商用机器人仅配备视场狭窄的摄像头，或成本低廉的1D激光雷达，其数据难以解读。为此，我们提出一种自监督方法，利用RGB-D相机检测作为监督信号，从1D激光雷达数据中检测人体并估计其2D姿态。模型在70分钟自主采集的数据上训练，可在未见过的环境中实现全向人体检测，精度达71%，召回率80%，距离平均绝对误差为13cm，方向误差为44°（基于真实标注数据）。该能力对共享公共空间中的机器人至关重要，可支持安全导航、恰当接近行为及及时人机交互启动。在两个额外公共环境中的部署进一步表明，该方法可作为低成本、隐私友好的广视角感知层，服务于社会感知服务机器人。
原文摘要 · Abstract (English)
Reliable localization of people is fundamental for service and social robots that must operate in close interaction with humans. State-of-the-art human detectors often rely on RGB-D cameras or costly 3D LiDARs. However, most commercial robots are equipped with cameras with a narrow field of view, leaving them unaware of users approaching from other directions, or inexpensive 1D LiDARs whose readings are hard to interpret. To address these limitations, we propose a self-supervised approach to detect humans and estimate their 2D pose from 1D LiDAR data, using detections from an RGB-D camera as supervision. Trained on 70 minutes of autonomously collected data, our model detects humans omnidirectionally in unseen environments with 71% precision, 80% recall, and mean absolute errors of 13cm in distance and 44° in orientation, measured against ground truth data. Beyond raw detection accuracy, this capability is relevant for robots operating in shared public spaces, where omnidirectional awareness of nearby people is crucial for safe navigation, appropriate approach behavior, and timely human-robot interaction initiation using low-cost, privacy-preserving sensing. Deployment in two additional public environments further suggests that the approach can serve as a practical wide-FOV awareness layer for socially aware service robotics.
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