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Localization of an unmanned ground vehicle (UGV) is a very important task for autonomous vehicle navigation. In this paper, we propose a computer vision technique to identify the location of an outdoor UGV. The proposed technique is based on 3D registration of 360 degree laser range data to a digital surface model (DSM). A long sequence of range frames is obtained from a rotating range sensor which is mounted on the top of the vehicle. Two novel approaches are proposed for accurate 3D registration of range data and the DSM. First, registration is done between range frames in a pair-wise manner followed by a refinement with the DSM. Second, we divide the DSM to several layers and find correspondences near the current vehicle elevation. This reduces the number of outliers and facilitates fast localization. Experimental results show that the proposed approaches yield better performance in 3D localization compared to conventional 3D registration techniques. Error analysis on four outdoor paths is presented with respect to ground truth.

For an autonomous mobile robot operating in an unknown environment, distinguishing obstacles from the traversable ground region is an essential step in determining whether the robot can traverse the area. Ground segmentation thus plays a critical role in autonomous mobile robot navigation in challenging environments, especially in real time. In this article, a ground segmentation method is proposed that combines three techniques: gradient threshold, adaptive break point detection, and mean height evaluation. Based on three-dimensional (3D) point clouds obtained from a Velodyne HDL-32E sensor, and by exploiting the structure of a two-dimensional reference image, the 3D data are represented as a graph data structures. This process serves as both a preprocessing step and a visualization of very large data sets, mobilegenerated data for segmentation, and building maps of the area. Various types of 3D data-such as ground regions near the sensor center, uneven regions, and sparse regions-need to be represented and segmented. For the ground regions, we apply the gradient threshold technique for segmentation. We address the uneven regions using adaptive break points. Finally, for the sparse region, we segment the ground by using a mean height evaluation.

In this paper, we propose a robust corner detection method to improve both detection rate and localization accuracy by modifying the structure tensor-based corner detection method in two ways. First, we introduce a connected component analysis (CCA) method for constructing a CCA structure tensor in order to make the structure tensor adaptive to the structure of the image. Second, the normalized cross-correlation (NCC) method is applied for false corner rejection with the observation that the patch of a true corner has a distinctive characteristic compared with connected neighboring patches. The proposed method is compared with several corner detection methods over a number of images. Experimental results show that the proposed method shows better performance in terms of both detection rate and localization accuracy.