Pointing at Moving Robots: Detecting Events from Wrist IMU Data

Abstract

We propose a practical approach for detecting the event that a human wearing an IMU-equipped bracelet points at a moving robot; the approach uses a learned classifier to verify if the robot motion (as measured by its odometry) matches the wrist motion, and does not require that the relative pose of the operator and robot is known in advance. To train the model and validate the system, we collect datasets containing hundreds of real-world pointing events. Extensive experiments quantify the performance of the classifiers and relevant metrics of the resulting detectors; the approach is implemented in a real-world demonstrator that allows users to land quadrotors by pointing at them.