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Target Recovery for Robust Deep Learning-Based Person Following in Mobile Robots: Online Trajectory Prediction

机译：在移动机器人的强大基于深度学习的人的目标恢复：在线轨迹预测

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摘要

The ability to predict a person’s trajectory and recover a target person in the event the target moves out of the field of view of the robot’s camera is an important requirement for mobile robots designed to follow a specific person in the workspace. This paper describes an extended work of an online learning framework for trajectory prediction and recovery, integrated with a deep learning-based person-following system. The proposed framework first detects and tracks persons in real time using the single-shot multibox detector deep neural network. It then estimates the real-world positions of the persons by using a point cloud and identifies the target person to be followed by extracting the clothes color using the hue-saturation-value model. The framework allows the robot to learn online the target trajectory prediction according to the historical path of the target person. The global and local path planners create robot trajectories that follow the target while avoiding static and dynamic obstacles, all of which are elaborately designed in the state machine control. We conducted intensive experiments in a realistic environment with multiple people and sharp corners behind which the target person may quickly disappear. The experimental results demonstrated the effectiveness and practicability of the proposed framework in the given environment.
机译：在目标中预测一个人的轨迹并在目标中恢复目标人员的能力从机器人相机的视野中移动，这是移动机器人旨在遵循工作空间中特定人的移动机器人的重要要求。本文介绍了轨迹预测和恢复的在线学习框架的扩展工作，与基于深入的学习的人之后的系统集成。拟议的框架首先使用单次多射线检测器深神经网络实时检测和跟踪人员。然后，它通过使用点云估计人员的真实世界位置，并通过Hue-Sization-Value模型提取衣物颜色来识别目标人员。该框架允许机器人根据目标人的历史路径在线在线学习目标轨迹预测。全局和局部路径规划师创建跟随目标，同时避免静态和动态的障碍，所有这些都在国家机器控制的精心设计机器人轨迹。我们在具有多个人的现实环境中进行了密集的实验，并将目标人员可能会迅速消失。实验结果表明了在给定环境中提出的框架的有效性和实用性。

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