Ensemble of shape functions for 3D object classification

Wohlkinger, Walter; Vincze, Markus

doi:10.1109/robio.2011.6181760

Cited by 306 publications

(217 citation statements)

References 8 publications

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“…The laser scanning process generates a point cloud which is used in conjunction with 3D descriptors which summarize the object geometry to detect relevant objects. 3D descriptors can be divided into local descriptors (Gatzke et al 2005;Johnson and Hebert 1999), which describe the object geometry in the neighborhood of keypoints, and global descriptors (Wohlkinger and Vincze 2011;Rusu et al 2010), which compute features throughout an entire object. Object recognition from point clouds has been successfully applied to detect building elements (Bosche et al 2009) as well as construction equipment (Wang and Cho 2015).…”

Section: Existing Object Recognition Methodsmentioning

confidence: 99%

Robotic sensing and object recognition from thermal-mapped point clouds

Kim

Chen

Cho

2017

Int J Intell Robot Appl

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Many of the civil structures are more than half way through or nearing their intended service life; frequently assessing and maintaining structural integrity is a top maintenance priority. Robotic inspection technologies using ground and aerial robots with 3D scanning and imaging capabilities have the potential to improve safety and efficiency of infrastructure management. To provide more valuable information to inspectors and agency decision makers, automatic environment sensing and semantic information extraction are fundamental issues in this field. This paper introduces an innovative method for generating thermal-mapped point clouds of a robot's work environment and performing automatic object recognition with the aid of thermal data fused to 3D point clouds. The laser scanned point cloud and thermal data were collected using a custom-designed mobile robot. The multimodal data was combined with a data fusion process based on texture mapping. The automatic object recognition was performed by two processes: segmentation with thermal data and classification with scanned geometric features. The proposed method was validated with the scan data collected in an entire building floor. Experimental results show that the thermal integrated object recognition approach achieved better performance than a geometry only-based approach, with an average recognition accuracy of 93%, precision of 83%, and recall rate of 86% for objects in the tested environment including humans, display monitors and light fixtures.

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Section: Existing Object Recognition Methodsmentioning

confidence: 99%

Robotic sensing and object recognition from thermal-mapped point clouds

Kim

Chen

Cho

2017

Int J Intell Robot Appl

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“…Die Form ist jedoch besonders geeignet, um Objektklassifizierungen zu ermöglichen. Da das Lernen einer Klasse aus vielen Beispielen sehr zeitaufwendig ist, stellt [30] eine Methode vor, die direkt aus 3D Modellen eine Beschreibung zur Wiedererkennung des Objekts lernt. Durch das automatische Kreieren von Ansichten mit einem spezifischen Sensormodell werden sehr gute Klassifiziererfolge erzielt.…”

Section: Abb 6 Objekterkennung Mit Starken Verdeckungen: Mit Hilfe unclassified

Roboter lernen mit Gegenständen umzugehen: neue Entwicklungen und Chancen

Vincze

Zillich

Prankl

2017

Elektrotech. Inftech.

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Für den zukünftigen Einsatz von Robotern fordern Experten aus der Industrie Roboter, die sicher sind und dem Benutzer eine klare Rückmeldung geben, was der Roboter macht und warum. Eine der wichtigsten Funktionen, um dies zu erreichen, ist die zuverlässige Erkennung der Umgebung, um den Roboter mit einem besseren Verstehen der Welt auszustatten. In diesem Artikel geben wir eine Übersicht derzeitiger Entwicklungen.

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“…3.3 Object Classification 3.3.1 Features: For each segmented object we compute a vector of features composed of Geometric features mainly inspired by (Serna and Marcotegui, 2014) and of 3 histogram-based descriptors from the literature, namely CVFH (Aldoma et al, 2011), GRSD (Marton et al, 2010b) and ESF (Wohlkinger and Vincze, 2011). In summary we obtain a feature vector with 991 variables: 22 for geometric features, 308 for CVFH, 21 for GRSD and 640 for ESF.…”

Section: Object Segmentationmentioning

confidence: 99%

Fast and Robust Segmentation and Classification for Change Detection in Urban Point Clouds

Roynard¹,

Deschaud²,

Goulette³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Change detection is an important issue in city monitoring to analyse street furniture, road works, car parking, etc. For example, parking surveys are needed but are currently a laborious task involving sending operators in the streets to identify the changes in car locations. In this paper, we propose a method that performs a fast and robust segmentation and classification of urban point clouds, that can be used for change detection. We apply this method to detect the cars, as a particular object class, in order to perform parking surveys automatically. A recently proposed method already addresses the need for fast segmentation and classification of urban point clouds, using elevation images. The interest to work on images is that processing is much faster, proven and robust. However there may be a loss of information in complex 3D cases: for example when objects are one above the other, typically a car under a tree or a pedestrian under a balcony. In this paper we propose a method that retain the three-dimensional information while preserving fast computation times and improving segmentation and classification accuracy. It is based on fast region-growing using an octree, for the segmentation, and specific descriptors with Random-Forest for the classification. Experiments have been performed on large urban point clouds acquired by Mobile Laser Scanning. They show that the method is as fast as the state of the art, and that it gives more robust results in the complex 3D cases.

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Ensemble of shape functions for 3D object classification

Cited by 306 publications

References 8 publications

Robotic sensing and object recognition from thermal-mapped point clouds

Robotic sensing and object recognition from thermal-mapped point clouds

Roboter lernen mit Gegenständen umzugehen: neue Entwicklungen und Chancen

Fast and Robust Segmentation and Classification for Change Detection in Urban Point Clouds

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