Abstract:
The number of road accident related fatalities and
damages are reduced substantially by improving road infrastructure
and enacting and imposing laws. Further reduction is
possible through embedding intelligence onto the vehicles for safe
decision making. Road boundary information plays a major role
in developing such intelligent vehicles. A prominent feature of
roads in urban, semi-urban, and similar environments, is curbs
on either side defining the road’s boundary. In this brief, a novel
methodology of tracking curbs is proposed. The problem of
tracking a curb from a moving vehicle is formulated as tracking
of a maneuvering target in clutter from a mobile platform using
onboard sensors. A curb segment is presumed to be the maneuvering
target, and is modeled as a nonlinear Markov switching
process. The target’s (curb’s) orientation and location measurements
are simultaneously obtained using a two-dimensional (2-D)
scanning laser radar (LADAR) and a charge-coupled device
(CCD) monocular camera, and are modeled as traditional base
state observations. Camera images are also used to estimate the
target’s mode, which is modeled as a discrete-time point process.
An effective curb tracking algorithm, known as Curb Tracking
and Estimation (CuTE) using multiple modal sensor information
is, thus, synthesized in an image enhanced interactive multiple
model filtering framework. The use and fusion of camera vision
and LADAR within this frame provide for efficient, effective, and
robust tracking of curbs. Extensive experiments conducted in a
campus road network demonstrate the viability, effectiveness, and
robustness of the proposed method.
