  Human detection and tracking is an essential task for service robots, where
the combined use of multiple sensors has potential advantages that are yet to
be exploited. In this paper, we introduce a framework allowing a robot to learn
a new 3D LiDAR-based human classifier from other sensors over time, taking
advantage of a multisensor tracking system. The main innovation is the use of
different detectors for existing sensors (i.e. RGB-D camera, 2D LiDAR) to
train, online, a new 3D LiDAR-based human classifier, exploiting a so-called
trajectory probability. Our framework uses this probability to check whether
new detections belongs to a human trajectory, estimated by different sensors
and/or detectors, and to learn a human classifier in a semi-supervised fashion.
The framework has been implemented and tested on a real-world dataset collected
by a mobile robot. We present experiments illustrating that our system is able
to effectively learn from different sensors and from the environment, and that
the performance of the 3D LiDAR-based human classification improves with the
number of sensors/detectors used.
