用于基于平面激光雷达的人体姿态估计的分解时空卷积

中文摘要 AI 辅助

定位附近的人类并估计他们的朝向是安全导航和具备社交意识的人机交互的关键能力。许多姿态估计流程针对相机和3D激光雷达，或假定具备GPU级计算能力，而服务机器人通常仅配备全向平面激光雷达和普通板载处理器。我们使用基于时空块的轻量级网络解决从平面激光雷达序列进行全向人体检测和相对2D姿态估计的问题，该网络明确将沿扫描线的空间处理与跨扫描的时间聚合分开。我们的网络处理360°激光雷达序列以输出每条射线的人体存在、距离和相对方向。我们通过传感器重叠区域中窄RGB-D人体跟踪器的跨模态自监督进行训练，无需手动激光雷达标签。定量实验表明，我们的方法始终优于参数匹配的基线模型，减少了距离（-38%）、位置（-28%）和方向（-15%）的误差。我们还在公共FROG数据集上进行基准测试，报告服务机器人上的实时CPU推理，并通过现场演示进行验证，支持其适用于计算受限的服务机器人的空间感知。

英文摘要

Localizing nearby humans and estimating their facing direction are key capabilities for safe navigation and socially aware human-robot interaction. Many pose-estimation pipelines target cameras and 3D LiDAR or assume GPU-class compute, whereas service robots are often equipped only with omnidirectional planar LiDARs and modest onboard processors. We address omnidirectional human detection and relative 2D pose estimation from planar LiDAR sequences with a lightweight network based on Space-Time Blocks, which explicitly separate spatial processing along scan rays from temporal aggregation across scans. Our network processes 360° LiDAR sequences to output per-ray human presence, distance, and relative orientation. We train it via cross-modal self-supervision from a narrow RGB-D body tracker in the sensors' overlap region, removing the need for manual LiDAR labels. Quantitative experiments show that our approach consistently outperforms a parameter-matched baseline model, reducing errors in distance (-38%), position (-28%), and orientation (-15%). We further benchmark on the public FROG dataset, report real-time CPU inference on a service robot, and validate with in-field demonstrations, supporting its suitability for spatial perception on computationally constrained service robots.

发表机构

• Dalle Molle Institute for Artificial Intelligence (IDSIA), USI-SUPSI（瑞士意大利语区大学提契诺大学人工智能达勒莫利研究所）

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