  Deep learning is the essential building block of state-of-the-art person
detectors in 2D range data. However, only a few annotated datasets are
available for training and testing these deep networks, potentially limiting
their performance when deployed in new environments or with different LiDAR
models. We propose a method, which uses bounding boxes from an image-based
detector (e.g. Faster R-CNN) on a calibrated camera to automatically generate
training labels (called pseudo-labels) for 2D LiDAR-based person detectors.
Through experiments on the JackRabbot dataset with two detector models, DROW3
and DR-SPAAM, we show that self-supervised detectors, trained or fine-tuned
with pseudo-labels, outperform detectors trained using manual annotations from
a different dataset. Combined with robust training techniques, the
self-supervised detectors reach a performance close to the ones trained using
manual annotations. Our method is an effective way to improve person detectors
during deployment without any additional labeling effort, and we release our
source code to support relevant robotic applications.
