Pole-Like Objects Recognition From Mobile Laser Scanning Data Using Smoothing and Principal Component Analysis

Yokoyama, H.; Date, Hiroaki; Kanai, Satoshi; Takeda, Hiroshi

doi:10.5194/isprsarchives-xxxviii-5-w12-115-2011

Cited by 32 publications

(27 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [23] and [24] the authors searched vertical isolated elements in a point cloud previously structured in voxels or regions within a previous segmentation procedure. [25,26] developed different methods that also depend on the geometry of vertical urban elements. In these cases, the detection is based upon the study of the three eigenvalues obtained from the covariance matrix of each segment in which the cloud was previously decomposed.…”

mentioning

confidence: 99%

Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

et al. 2015

View full text Add to dashboard Cite

Detecting and modeling urban furniture are of particular interest for urban management and the development of autonomous driving systems. This paper presents a novel method for detecting and classifying vertical urban objects and trees from unstructured three-dimensional mobile laser scanner (MLS) or terrestrial laser scanner (TLS) point cloud data. The method includes an automatic initial segmentation to remove the parts of the original cloud that are not of interest for detecting vertical objects, by means of a geometric index based on features of the point cloud. Vertical object detection is carried out through the Reed and Xiaoli (RX) anomaly detection algorithm applied to a pillar structure in which the point cloud was previously organized. A clustering algorithm is then used to classify the detected vertical elements as man-made poles or trees. The effectiveness of the proposed method was tested in two point clouds from heterogeneous street scenarios and measured by two different sensors. The results for the two test sites achieved detection rates higher than 96%; the classification accuracy was around 95%, and the completion quality of both procedures was 90%. Non-detected poles come from occlusions in the point cloud and low-height traffic signs; most misclassifications occurred in man-made poles adjacent to trees.

show abstract

mentioning

confidence: 99%

Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Detection of pole-like objects such as tree trunks, traffic signs, and light poles have been widely studied for their unique structure (Cabo et al, 2014;Landa and Ondroušek, 2016;Lehtomäki et al, 2010b;Yokoyama et al, 2011). 3D Hough transform combined with RANSAC has been shown to work well in the detection of pole-like structures by Vosselman et al (2004).…”

Section: Literature Reviewmentioning

confidence: 99%

A Two-Step Classification Approach to Distinguishing Similar Objects in Mobile Lidar Point Clouds

Khoshelham

Fraser

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

show abstract

“…We describe a sequence of values as a feature vector, and each variable as a feature variable. , 2012;Yokoyama et al, 2011;Chen et al, 2007;Cabo et al. 2014;Yang et al, 2015).…”

Section: Classes Of Pole-like Objectsmentioning

confidence: 99%

“…Identification of object classes is also required for shape reconstruction, because knowledge-based methods are typical for reconstructing 3D shapes from incomplete point-clouds (Nan et al, 2010;Nan et al, 2012;Kim et al, 2012;Lin et al, 2013). Some researchers studied shape classification for pole-like objects (Yokoyama et al, 2011;Cabo et al, 2014;Yang et al, 2015;Kamal et al, 2013;Pu et al, 2011;Li and Oude Elberink, 2013). Their methods are based on threshold values of feature values, which have to be carefully determined by experiments.…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Pole-Like Objects From Mobile Mapping Data

Fukano

Masuda

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Laser scanners on a vehicle-based mobile mapping system can capture 3D point-clouds of roads and roadside objects. Since roadside objects have to be maintained periodically, their 3D models are useful for planning maintenance tasks. In our previous work, we proposed a method for detecting cylindrical poles and planar plates in a point-cloud. However, it is often required to further classify pole-like objects into utility poles, streetlights, traffic signals and signs, which are managed by different organizations. In addition, our previous method may fail to extract low pole-like objects, which are often observed in urban residential areas. In this paper, we propose new methods for extracting and classifying pole-like objects. In our method, we robustly extract a wide variety of poles by converting point-clouds into wireframe models and calculating cross-sections between wireframe models and horizontal cutting planes. For classifying pole-like objects, we subdivide a pole-like object into five subsets by extracting poles and planes, and calculate feature values of each subset. Then we apply a supervised machine learning method using feature variables of subsets. In our experiments, our method could achieve excellent results for detection and classification of pole-like objects.

show abstract

Pole-Like Objects Recognition From Mobile Laser Scanning Data Using Smoothing and Principal Component Analysis

Cited by 32 publications

References 10 publications

Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

A Two-Step Classification Approach to Distinguishing Similar Objects in Mobile Lidar Point Clouds

Detection and Classification of Pole-Like Objects From Mobile Mapping Data

Contact Info

Product

Resources

About