  The problem of autonomous transportation in industrial scenarios is receiving
a renewed interest due to the way it can revolutionise internal logistics,
especially in unstructured environments. This paper presents a novel
architecture allowing a robot to detect, localise, and track (possibly
multiple) pallets using machine learning techniques based on an on-board 2D
laser rangefinder only. The architecture is composed of two main components:
the first stage is a pallet detector employing a Faster Region-based
Convolutional Neural Network (Faster R-CNN) detector cascaded with a CNN-based
classifier; the second stage is a Kalman filter for localising and tracking
detected pallets, which we also use to defer commitment to a pallet detected in
the first stage until sufficient confidence has been acquired via a sequential
data acquisition process. For fine-tuning the CNNs, the architecture has been
systematically evaluated using a real-world dataset containing 340 labeled 2D
scans, which have been made freely available in an online repository. Detection
performance has been assessed on the basis of the average accuracy over k-fold
cross-validation, and it scored 99.58% in our tests. Concerning pallet
localisation and tracking, experiments have been performed in a scenario where
the robot is approaching the pallet to fork. Although data have been originally
acquired by considering only one pallet as per specification of the use case we
consider, artificial data have been generated as well to mimic the presence of
multiple pallets in the robot workspace. Our experimental results confirm that
the system is capable of identifying, localising and tracking pallets with a
high success rate while being robust to false positives.
