A new fast estimating floor region based on image segmentation for smart rovers

Abstract

In this paper, a new algorithm that hardware acceleration has enabled to estimate floor regions in a single image for unmanned ground vehicle (UGV) robots is presented. In general, the cluttered indoor surroundings patterned floors, shadows, and reflections make it extremely difficult to identify floor regions. The proposed algorithm combines extracting surface texture characteristics with specific geometric areas to determine object boundaries and then uses SVM classification to distinguish between the floor and non-floor regions. To achieve real-time performance, the implementation of the proposed algorithm on an SoC FPGA embedded platform gives a heterogeneous hardware acceleration methodology. In experimental results, a public MIT scene dataset and indoor database were used to verify detection accuracy. Compared to other research, the proposed algorithm accuracy can reach up to 94.72% on average without the assistance of any other physical sensors, such as RGB-D or laser ranger sensors.