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.

翻译：可靠的人员定位是需要在人类附近紧密交互的服务机器人与社交机器人的基础。现有最先进的人员检测器通常依赖RGB-D摄像头或昂贵的3D激光雷达。然而，大多数商用机器人配备具有狭窄视场的摄像头（使其无法感知从其他方向接近的用户）或难以解读读数的低成本1D激光雷达。为解决这些局限，我们提出一种自监督方法，通过RGB-D摄像头的检测结果作为监督信号，从1D激光雷达数据中检测人类并估计其二维姿态。该模型在70分钟自主采集数据上训练后，能在未知环境中以71%精确率、80%召回率实现全向人员检测，与真值数据相比，距离平均绝对误差为13厘米，方向平均绝对误差为44°。除原始检测精度外，该能力对在共享公共空间运行的机器人尤为关键——低成本隐私保护感知下的全向人员感知对安全导航、恰当接近行为及及时的人机交互启动至关重要。在两个额外公共环境的部署实验进一步表明，该方法可作为社会感知服务机器人实用的宽视场感知层。