Object extraction in urban environments from large-scale dynamic point cloud datasets

Börcs, Attila; Jozsa, Oszkar; Benedek, Csaba

doi:10.1109/cbmi.2013.6576580

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…In our system, point cloud segmentation is achieved by a grid based approach [9], [10]. In the literature various robust approaches are proposed for planar ground modeling such as RANSAC.…”

Section: A Point Cloud Segmentationmentioning

confidence: 99%

On board 3D object perception in dynamic urban scenes

Börcs

Nagy

Benedek

2013

2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom)

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In urban environments, object recognition and road monitoring are key issues for driving assistance systems or autonomous vehicles. This paper presents a LIDAR-based perception system which provides reliable detection of 3D urban objects from point cloud sequences of a Velodyne HDL-64E terrestrial LIDAR scanner installed on a moving platform. As for the output of the system, we perform real-time localization and identification of typical urban objects, such as traffic signs, vehicles or crosswalks. In contrast to most existing works, the proposed algorithm does not use hand-labeled training datasets to perform object classification. Experimental results are carried out on real LIDAR measurements in the streets of Budapest, Hungary.

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“…In our system, point cloud segmentation is achieved by a grid based approach [9], [10]. In the literature various robust approaches are proposed for planar ground modeling such as RANSAC.…”

Section: A Point Cloud Segmentationmentioning

confidence: 99%

On board 3D object perception in dynamic urban scenes

Börcs

Nagy

Benedek

2013

2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom)

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“…After the classification for each voxel, a clustering process generates objects which can be also classified. Ground, short structures and tall structures may be classified from a voxel map through height thresholds, also vegetation may be detected with the multiecho in the laser rangefinder [14]. However, this method is restricted to urban scenarios where no slopes on the ground are assumed.…”

Section: Introductionmentioning

confidence: 99%

Multi-layer Perceptrons for Voxel-Based Classification of Point Clouds from Natural Environments

Plaza¹,

Gómez-Ruiz²,

Mandow³

et al. 2015

Advances in Computational Intelligence

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Abstract. This paper addresses classification of 3D point cloud data from natural environments based on voxels. The proposed model uses multi-layer perceptrons to classify voxels based on a statistic geometric analysis of the spatial distribution of inner points. Geometric features such as tubular structures or flat surfaces are identified regardless of their orientation, which is useful for unstructured or natural environments. Furthermore, the combination of voxels and neural networks pursues faster computation than alternative strategies. The model has been successfully tested with 3D laser scans from natural environments.Keywords: Multi-layer perceptron · 3D classification · Mobile robot · Voxel map IntroductionKnowledge of geometric features in three-dimensional (3D) scenes is useful for object recognition in challenging applications in natural and unstructured environments, such as robotics for search and rescue (SAR) and planetary exploration [1,2]. In these applications, scenes are usually obtained through laser scanners [3] and stereo vision [4] as large and complex point clouds. Object recognition from point clouds usually involves three main steps: segmentation, feature extraction, and classification. Three main approaches have been adopted for object recognition in point clouds. The first approach avoids point-based computations by reducing the scene to a 2D representation which can be processed with standard artificial vision algorithms. The objects may be classified based on local and global statistics features of each object from a range image [5] or from a 2D deviation map when classification is based on texture analysis [6]. The classification can be also performed with image with depth data (RGB-D) by fusing results from separate 2D and 3D segmentation and feature extraction processes [7].

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Multi-sensorial Environment Perception in Urban Environment

Benedek

2022

Communications in Computer and Information Science

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Object extraction in urban environments from large-scale dynamic point cloud datasets

Cited by 5 publications

References 6 publications

On board 3D object perception in dynamic urban scenes

On board 3D object perception in dynamic urban scenes

Multi-layer Perceptrons for Voxel-Based Classification of Point Clouds from Natural Environments

Multi-sensorial Environment Perception in Urban Environment

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