CuTE: curb tracking and estimation

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