1 research outputs found
Robust and Real-Time Detection and Tracking of Moving Objects with Minimum 2D LiDAR Information to Advance Autonomous Cargo Handling in Ports
Detecting and tracking moving objects (DATMO) is an essential component for autonomous
driving and transportation. In this paper, we present a computationally low-cost and robust DATMO
system which uses as input only 2D laser rangefinder (LRF) information. Due to its low requirements
both in sensor needs and computation, our DATMO algorithm is meant to be used in current
Autonomous Guided Vehicles (AGVs) to improve their reliability for the cargo transportation tasks at
port terminals, advancing towards the next generation of fully autonomous transportation vehicles.
Our method follows a Detection plus Tracking paradigm. In the detection step we exploit the
minimum information of 2D-LRFs by segmenting the elements of the scene in a model-free way and
performing a fast object matching to pair segmented elements from two different scans. In this way,
we easily recognize dynamic objects and thus reduce consistently by between two and five times the
computational burden of the adjacent tracking method. We track the final dynamic objects with an
improved Multiple-Hypothesis Tracking (MHT), to which special functions for filtering, confirming,
holding, and deleting targets have been included. The full system is evaluated in simulated and real
scenarios producing solid results. Specifically, a simulated port environment has been developed
to gather realistic data of common autonomous transportation situations such as observing an
intersection, joining vehicle platoons, and perceiving overtaking maneuvers. We use different sensor
configurations to demonstrate the robustness and adaptability of our approach. We additionally
evaluate our system with real data collected in a port terminal the Netherlands. We show that it is
able to accomplish the vehicle following task successfully, obtaining a total system recall of more than
98% while running faster than 30 HzThis work has been partly funded by the EU project CargoANTs FP7-SST-2013-605598, the Spanish
MINECO projects DPI2016-78957-R (AEI/FEDER EU) and Unidad de Excelencia Maria de Maeztu 2016
(MDM-2016-0656).Peer reviewe