基于神经网络的激光雷达实时遥操作姿态识别
Neural Network Based Lidar Gesture Recognition for Realtime Robot Teleoperation
摘要：
我们提出了一种新的低复杂度的激光雷达手势识别系统的移动机器人控制鲁棒的手势变化。我们的系统使用模块化的方法，包括姿势估计模块和手势分类器。使用卷积神经网络从激光雷达扫描预测姿态估计，该卷积神经网络使用现有的基于立体的姿态估计系统进行训练。手势分类是使用长短期记忆网络完成的，并使用一系列估计的身体姿势作为输入来预测手势。将流水线分解为两个模块降低了输入的维度，输入可以是激光雷达扫描，立体图像或任何其他可以提取身体关键点的模态，使我们的系统重量轻，适用于计算能力有限的移动机器人控制。激光雷达的使用有助于提高系统的鲁棒性，使其能够在大多数户外条件下运行，不受照明条件的影响，并且可以在机器人周围360度检测输入。基于激光雷达的姿态估计器和手势分类器使用数据增强和自动标记技术，需要最少量的数据收集并避免手动标记的需要。我们报告我们的系统的每个模块的实验结果，并证明其有效性，通过测试它在现实世界中的机器人遥操作设置。
英文摘要：
We propose a novel low-complexity lidar gesture recognition system for mobile robot control robust to gesture variation. Our system uses a modular approach, consisting of a pose estimation module and a gesture classifier. Pose estimates are predicted from lidar scans using a Convolutional Neural Network trained using an existing stereo-based pose estimation system. Gesture classification is accomplished using a Long Short-Term Memory network and uses a sequence of estimated body poses as input to predict a gesture. Breaking down the pipeline into two modules reduces the dimensionality of the input, which could be lidar scans, stereo imagery, or any other modality from which body keypoints can be extracted, making our system lightweight and suitable for mobile robot control with limited computing power. The use of lidar contributes to the robustness of the system, allowing it to operate in most outdoor conditions, to be independent of lighting conditions, and for input to be detected 360 degrees around the robot. The lidar-based pose estimator and gesture classifier use data augmentation and automated labeling techniques, requiring a minimal amount of data collection and avoiding the need for manual labeling. We report experimental results for each module of our system and demonstrate its effectiveness by testing it in a real-world robot teleoperation setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
