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This paper proposes a novel approach for segmenting and space partitioning data of sparse 3D LiDAR point clouds for autonomous driving tasks in urban environments. Our main focus is building a compact data representation which provides enough information for an accurate segmentation algorithm. We propose the use of an extension of elevation maps for automotive driving perception tasks which is capable...
We present a novel technique for fast and accurate reconstruction of depth images from 3D point clouds acquired in urban and rural driving environments. Our approach focuses entirely on the sparse distance and reflectance measurements generated by a LiDAR sensor. The main contribution of this paper is a combined segmentation and upsampling technique that preserves the important semantical structure...
We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using recorded routes from a data collection vehicle, our proposed method generates vast quantities of labelled images containing proposed paths and obstacles without requiring manual annotation, which we then use to train a deep semantic segmentation...
For driverless driving cars, it is essential to detect drivable space. It can directly apply to plan driving paths by acquiring the occupancy grid map. In addition, it can enhance object clustering by removing the ground in advance. However, in urban, not only a large number of vehicles are driving at the same time, but also roads with diverse inclinations are complicatedly connected with each other...
An automatic algorithm for forest road identification and extraction was developed. The algorithm utilized Laplacian of Gaussian (LoG) filter and slope calculation on high resolution multispectral imagery and LiDAR data respectively to extract both primary road and secondary road segments in the forest area. Also, a hierarchical post process was designed to iteratively connect the road segments to...
To deal with the problem of urban ground object information extraction, the paper proposes an object-oriented classification method using aerial image and LiDAR data. Firstly, we select the optimal segmentation scales of different ground objects and synthesize them to get accurate object boundaries. Then, we use ReliefF algorithm to select the optimal feature combination and eliminate the Hughes phenomenon...
This paper proposes a method for automated road extraction from airborne Light Detection and Ranging (LiDAR) data. The method combines Segmentation Based Filtering (SBF) with Triangular Irregular Network-based segmentation to extract the road points. The method contains two major steps. Firstly, Segmentation Based Filtering (SBF) is applied to LiDAR data for initial segmentation of road regions. Here,...
This paper presents an approach for pixel-wise object segmentation for road scenes based on the integration of a color image and an aligned 3D point cloud. In light of the advantage of range information in object discovery, we first produce initial object hypotheses by clustering the sparse 3D point cloud. The image pixels registered to the clustered 3D points are taken as samples to learn each object's...
To understand scenes and help autonomous robots and cars, researchers' attention is directed through the problem of classifying 3D point cloud. In this paper, we present a novel approach to semantically segment 3D point cloud of residential scenes captured by a lidar sensor. Our approach is based on a dual-scale analysis: a small-scale clustering and a large-scale grouping. Features used to train...
This paper describes an unsupervised approach for efficient extraction of grid-structured urban roads from airborne LIDAR data. Technically, the approach consists of three major components: 1) terrain separation from DSM and classification of ground features, 2) road centerline extraction from generated road candidates images, and 3) completion and verification of complete road networks. A ground-height...
We address the problem of extracting the road network from large-scale range datasets. Our approach is fully automatic and does not require any inputs other than depth and intensity measurements from the range sensor. Road extraction is important because it provides contextual information for scene analysis and enables automatic content generation for geographic information systems (GIS). In addition...
The automated extraction of topographic objects has been on the research agenda in the Photogrammetry and Computer Vision communities for more than two decades. Considerable progress has been achieved, though up to now there are hardly any commercial products that have been accepted by the market. Recent developments in the field of sensor technology, along with advanced techniques for data processing,...
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