Ground Extraction from 3D Lidar Point Clouds with the Classification Learner App

Pomares, Antonio; Martínez, Jorge L.; Mandow, Anthony; Martínez, M.a Dolores Barrios; Morán, Mariano; Morales, José

doi:10.1109/med.2018.8442569

Cited by 16 publications

(14 citation statements)

References 19 publications

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“…The following combination of simple spatial features, which has been already used for reliable ground extraction [13], is employed for every 3D laser point.…”

Section: Feature Computationmentioning

confidence: 99%

“…Taking into account that it is not necessary to employ complex classification methods to extract ground accurately from 3D Lidar scans [13], seven relevant supervised learning techniques have been selected for training: Decision Trees (DT), Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Linear Support Vector Machine (LSVM), Bagged Decision Trees (BDT), Random Forest (RF) and Gradient Boosted Trees (GBT). The last three are ensemble methods that combine various base estimators.…”

Section: Supervised Learningmentioning

confidence: 99%

“…Supervised learning usually employs hand-labelled points to obtain predictive models that can be applied to new data [12]. In this way, the Classification Learner App of Matlab has been employed to extract ground from 3D point clouds of an urban dataset [13]. Accordingly, a support vector machine was used to detect urban and rural roads with stereo vision [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans

et al. 2020

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Autonomous navigation of ground vehicles on natural environments requires looking for traversable terrain continuously. This paper develops traversability classifiers for the three-dimensional (3D) point clouds acquired by the mobile robot Andabata on non-slippery solid ground. To this end, different supervised learning techniques from the Python library Scikit-learn are employed. Training and validation are performed with synthetic 3D laser scans that were labelled point by point automatically with the robotic simulator Gazebo. Good prediction results are obtained for most of the developed classifiers, which have also been tested successfully on real 3D laser scans acquired by Andabata in motion.

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“…The following combination of simple spatial features, which has been already used for reliable ground extraction [13], is employed for every 3D laser point.…”

Section: Feature Computationmentioning

confidence: 99%

Section: Supervised Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans

et al. 2020

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“…Point-cloud segmentation based on machine learning is also a mature research area. Pomares et al [21] compared 23 state-of-the-art machine learning-based ground point extraction methods (e.g., SVM and KNN) through the MATLAB Classification Learner App which shows a promising ground extraction accuracy. Hackel et al [22] developed a supervised learning framework for point-wise semantic classification.…”

Section: Related Workmentioning

confidence: 99%

Integrate Point-Cloud Segmentation with 3D LiDAR Scan-Matching for Mobile Robot Localization and Mapping

et al. 2019

Sensors

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Localization and mapping are key requirements for autonomous mobile systems to perform navigation and interaction tasks. Iterative Closest Point (ICP) is widely applied for LiDAR scan-matching in the robotic community. In addition, the standard ICP algorithm only considers geometric information when iteratively searching for the nearest point. However, ICP individually cannot achieve accurate point-cloud registration performance in challenging environments such as dynamic environments and highways. Moreover, the computation of searching for the closest points is an expensive step in the ICP algorithm, which is limited to meet real-time requirements, especially when dealing with large-scale point-cloud data. In this paper, we propose a segment-based scan-matching framework for six degree-of-freedom pose estimation and mapping. The LiDAR generates a large number of ground points when scanning, but many of these points are useless and increase the burden of subsequent processing. To address this problem, we first apply an image-based ground-point extraction method to filter out noise and ground points. The point cloud after removing the ground points is then segmented into disjoint sets. After this step, a standard point-to-point ICP is applied into to calculate the six degree-of-freedom transformation between consecutive scans. Furthermore, once closed loops are detected in the environment, a 6D graph-optimization algorithm for global relaxation (6D simultaneous localization and mapping (SLAM)) is employed. Experiments based on publicly available KITTI datasets show that our method requires less runtime while at the same time achieves higher pose estimation accuracy compared with the standard ICP method and its variants.

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“…Subsequently, the trained classifiers can be exported to the Matlab workspace, where they can be used to compute predictions of new input data using the predictFcn function of the Matlab software. The Matlab software contains 22 popular classifier types and 5 different major classification algorithms in the Classification Learner app toolbox [33]. In following some analyze method has explained:…”

Section: The Classification Learner Applicationmentioning

confidence: 99%

A Comparative Study of Object Classification Methods Using 3D Zernike Moment on 3D Point Clouds

Özbay¹,

Çınar²

2019

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The point clouds provide responsive geometric representation on many applications. The classification of objects through point clouds is one of the popular subjects of recent years. In this study, we introduce the potential of the 3D Zernike Moment approach for the object classification on the 3D point cloud. Zernike Moment (ZM) has utilized as a feature extractor of point clouds. This paper presents a comparative study of the state-of-the-art classification methods with respect to machine learning algorithms and PointNet which have been developed for classification by Stanford University. Object classification has been applied to a dataset with labeled 3D Zernike Moment features inferences obtained from the 3D point cloud. The performance of the developed method is verified by comparing the experimental results on the Washington RGB-D Object Dataset which consists of forty-five different household objects as point cloud data. Fine Gaussian SVM gives the best results in accuracy (96.0%) according to the results obtained with built-in cross-validation results. The results of the proposed classification of 3D Point Clouds on the 3D Zernike Moment features have significantly higher accuracy. The classification of 3D Zernike Moments on point cloud compared to directly point cloud classification is efficient and effective and lower complexity computation is obtained. It is emphasized that the 3D Zernike Moment features can be optimized for classification. In general, the comparative validation results have been reached a high accuracy in the proposed method. In the future, 3D Zernike Moment feature extractions are emphasized for the usability of classification operations on 3D data.

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Ground Extraction from 3D Lidar Point Clouds with the Classification Learner App

Cited by 16 publications

References 19 publications

Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans

Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans

Integrate Point-Cloud Segmentation with 3D LiDAR Scan-Matching for Mobile Robot Localization and Mapping

A Comparative Study of Object Classification Methods Using 3D Zernike Moment on 3D Point Clouds

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