用手机装在扫地机器人上，实现无需改装的智能监控。
Piggyback Camera: Easy-to-Deploy Visual Surveillance by Mobile Sensing on Commercial Robot Vacuums
- 用手机+惯性传感器搭在机器人上，通过神经导航估算位置。
- 定位误差0.83米，100多个物品地图定位误差仅0.97米。
- 适合想低成本部署视觉监控的零售或家庭场景。
本文提出Piggyback Camera，一种基于商用扫地机器人的简易视觉监控系统。无需访问机器人内部系统，仅将带摄像头和惯性测量单元（IMU）的智能手机安装于机器人即可部署。系统通过神经惯性导航估计机器人位姿，并在清洁任务中以固定空间间隔高效采集图像。针对神经惯性导航中的领域差异问题，提出一种新的测试时数据增强方法——旋转增强集成（RAE）。结合利用机器人清洁模式的回环检测方法，进一步优化位姿估计。我们展示了该系统在物体地图构建中的应用，通过分析采集图像实现环境中超过100个物体的地理定位。在零售环境的实验表明，系统实现0.83米的相对位姿误差和0.97米的物体定位误差。
原文摘要 · Abstract (English)
This paper presents Piggyback Camera, an easy-to-deploy system for visual surveillance using commercial robot vacuums. Rather than requiring access to internal robot systems, our approach mounts a smartphone equipped with a camera and Inertial Measurement Unit (IMU) on the robot, making it applicable to any commercial robot without hardware modifications. The system estimates robot poses through neural inertial navigation and efficiently captures images at regular spatial intervals throughout the cleaning task. We develop a novel test-time data augmentation method called Rotation-Augmented Ensemble (RAE) to mitigate domain gaps in neural inertial navigation. A loop closure method that exploits robot cleaning patterns further refines these estimated poses. We demonstrate the system with an object mapping application that analyzes captured images to geo-localize objects in the environment. Experimental evaluation in retail environments shows that our approach achieves 0.83 m relative pose error for robot localization and 0.97 m positional error for object mapping of over 100 items.
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