IEEE Robotics & Automation Magazine - March 2014 - 40

A Robust Motion
Detec tion Technique for
Dynamic Environment
Monitoring
A Framework for Grid-Based
Monitoring of the Dynamic
Environment

By Qadeer Baig, Mathias Perrollaz,
and Christian Laugier

Moving Objects Detection
for Risk Assessment
In the field of advanced driver assistance systems (ADAS), many current approaches rely on
the perception of the road scene. Particularly,

Image lIcensed by Ingram PublIshIng

Digital Object Identifier 10.1109/MRA.2013.2297812
Date of publication: 10 March 2014

40

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IEEE ROBOTICS & AUTOMATION MAGAZINE

T

he Bayesian occupancy filter (BOF) provides a
framework for grid-based monitoring of the dynamic
environment. It allows us to estimate dynamic grids,
containing both information of occupancy and velocity.
Clustering such grids then provides detection of the
objects in the observed scene.
In this article, we present recent improvements in this framework.
First, multiple layers from a laser scanner are fused using opinion
pool, to deal with conflicting information.
Then, a fast motion detection
(MD) technique based on laser
data and an odometer/inertial
measurement unit (IMU) information is used to separate the
dynamic environment from the
static one. This technique is based
on transferring occupancy information between consecutive data grids,
instead of performing a complete
simultaneous localization and mapping (SLAM) solution. The objective of
the MD technique is to avoid false positives (static objects) like other detection
and tracking of moving objects (DATMOs) approaches. Finally, we show the
integration with BOF and with the subsequent tracking module called the fast clustering tracking algorithm (FCTA). We show
the improvements achieved in tracking
results after this integration, for an intelligent vehicle application.

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MARCH 2014

1070-9932/14/$31.00©2014IEEE



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