  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.
