An efficient RANSAC hypothesis evaluation using sufficient statistics for RGB-D pose estimation

Abstract

Achieving autonomous flight in GPS-denied environments begins with pose estimation in threedimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets.

Research on Visual Positioning of a Roadheader and Construction of an Environment Map

“…The process is as follows: (1) According to the matched 3D point set, the initial model is generated by randomly sampling some point pairs; (2) the model is evaluated according to the given threshold by calculating the point-pair error; and (3) through calculating the number of interior point pairs and the average value of the error, the model is updated and iterated based on the given threshold. After several iterations, a relatively optimal motion transformation can be obtained [26,27].…”