Feature extraction method based on 2.5-dimensions lidar platform for indoor mobile robots localization

Abstract

In this paper we proposed a lidar feature extraction method based on range measurement points from a novel designed 2.5-Dimensions lidar platform. For a conventional 2D laser scanner, only the planar information can be detected in once scan. In order to get more spatial information about the indoor(rectilinear) environment, a novel 2.5D lidar platform is designed by driving a 2D laser scanner up-and-down vibrating in a short distance using a voice coil motor. Then an effective feature extraction method is formulated based on the 2.5D laser point data. Compared with traditional method, these laser points are actually located in 3D environment that resulting the extracted data actually are strip-shaped which include more informations than 2D data. In our experiment, the geometrical features of space edges and surfaces can be extracted accurately, these are useful for the indoor mobile robot localization at the indoor environment. The effectiveness of this approach is validated through experiments on ROS(Robot Operating System).