An Analytical Review of Stereovision Techniques to Reconstruct 3D Coordinates

Abstract

Stereovision based on 3D environment reconstruction provides a true picture of real world situations for detection of objects' locations. This approach has specific use in the scenarios like identifying traffic jams on the roads, locating curves and bends on the roads, finding obstacles in the construction sites, etc. This paper describes different methods used in stereovision to detect images like use of trinocular stereovision, calculating correlation between left and right contours for achieving accuracy, use of prior information with intrinsic and extrinsic parameters, detection of side lane and 3D points of guardrails and fences, use of dense stereovision information, especially in urban environment. The paper also discusses Forward Collision Detection method that uses Elevation Map with Dense Stereovision, tracking of multiple objects using two- level approach and building an enhanced grid that involves obstacle cells. Hybrid dense stereo engine, which is used in urban detection scenarios is also discussed in the paper along with a solution of lane estimation in different situations using particle filtering method. Pattern matching using 3D image for pedestrian detection and lane estimation based on the particle filtering with greyscale images are also explored. The use of the rectangular digital elevation map for transforming stereo based information and the methodology used to enhance the sub pixel accuracy are also part of the paper.