Scene Understanding and Semantic Mapping for Unmanned Ground Vehicles Using 3D Point Clouds

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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Fast segmentation of 3D point clouds for ground vehicles

2010 IEEE Intelligent Vehicles Symposium ◽

10.1109/ivs.2010.5548059 ◽

2010 ◽

Cited By ~ 129

Author(s):

M. Himmelsbach ◽

Felix v. Hundelshausen ◽

H.-J. Wuensche

Keyword(s):

Point Clouds ◽

Ground Vehicles ◽

3D Point Clouds

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APPLICATION FOR 3D SCENE UNDERSTANDING IN DETECTING DISCHARGE OF DOMESTICWASTE ALONG COMPLEX URBAN RIVERS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b3-663-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 663-667

Author(s):

Y. Ninsalam ◽

R. Qin ◽

J. Rekittke

Keyword(s):

Image Classification ◽

Solid Waste ◽

Low Cost ◽

Scene Understanding ◽

Point Clouds ◽

Household Waste ◽

Step Method ◽

River Bank ◽

3D Point Clouds ◽

3D Scene

In our study we use 3D scene understanding to detect the discharge of domestic solid waste along an urban river. Solid waste found along the Ciliwung River in the neighbourhoods of Bukit Duri and Kampung Melayu may be attributed to households. This is in part due to inadequate municipal waste infrastructure and services which has caused those living along the river to rely upon it for waste disposal. However, there has been little research to understand the prevalence of household waste along the river. Our aim is to develop a methodology that deploys a low cost sensor to identify point source discharge of solid waste using image classification methods. To demonstrate this we describe the following five-step method: 1) a strip of GoPro images are captured photogrammetrically and processed for dense point cloud generation; 2) depth for each image is generated through a backward projection of the point clouds; 3) a supervised image classification method based on Random Forest classifier is applied on the view dependent red, green, blue and depth (RGB-D) data; 4) point discharge locations of solid waste can then be mapped by projecting the classified images to the 3D point clouds; 5) then the landscape elements are classified into five types, such as vegetation, human settlement, soil, water and solid waste. While this work is still ongoing, the initial results have demonstrated that it is possible to perform quantitative studies that may help reveal and estimate the amount of waste present along the river bank.

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APPLICATION FOR 3D SCENE UNDERSTANDING IN DETECTING DISCHARGE OF DOMESTICWASTE ALONG COMPLEX URBAN RIVERS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b3-663-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 663-667

Author(s):

Y. Ninsalam ◽

R. Qin ◽

J. Rekittke

Keyword(s):

Image Classification ◽

Solid Waste ◽

Low Cost ◽

Scene Understanding ◽

Point Clouds ◽

Household Waste ◽

Step Method ◽

River Bank ◽

3D Point Clouds ◽

3D Scene

In our study we use 3D scene understanding to detect the discharge of domestic solid waste along an urban river. Solid waste found along the Ciliwung River in the neighbourhoods of Bukit Duri and Kampung Melayu may be attributed to households. This is in part due to inadequate municipal waste infrastructure and services which has caused those living along the river to rely upon it for waste disposal. However, there has been little research to understand the prevalence of household waste along the river. Our aim is to develop a methodology that deploys a low cost sensor to identify point source discharge of solid waste using image classification methods. To demonstrate this we describe the following five-step method: 1) a strip of GoPro images are captured photogrammetrically and processed for dense point cloud generation; 2) depth for each image is generated through a backward projection of the point clouds; 3) a supervised image classification method based on Random Forest classifier is applied on the view dependent red, green, blue and depth (RGB-D) data; 4) point discharge locations of solid waste can then be mapped by projecting the classified images to the 3D point clouds; 5) then the landscape elements are classified into five types, such as vegetation, human settlement, soil, water and solid waste. While this work is still ongoing, the initial results have demonstrated that it is possible to perform quantitative studies that may help reveal and estimate the amount of waste present along the river bank.

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Outdoor Scene Understanding Based on Multi-Scale PBA Image Features and Point Cloud Features

Sensors ◽

10.3390/s19204546 ◽

2019 ◽

Vol 19 (20) ◽

pp. 4546 ◽

Cited By ~ 1

Author(s):

Yisha Liu ◽

Yufeng Gu ◽

Fei Yan ◽

Yan Zhuang

Keyword(s):

Point Cloud ◽

Spherical Surface ◽

Contextual Information ◽

Scene Understanding ◽

Point Clouds ◽

Multiple Scale ◽

Image Features ◽

3D Point Clouds ◽

Pyramid Model ◽

Initial Classification

Outdoor scene understanding based on the results of point cloud classification plays an important role in mobile robots and autonomous vehicles equipped with a light detection and ranging (LiDAR) system. In this paper, a novel model named Panoramic Bearing Angle (PBA) images is proposed which is generated from 3D point clouds. In a PBA model, laser point clouds are projected onto the spherical surface to establish the correspondence relationship between the laser ranging point and the image pixels, and then we use the relative location relationship of the laser point in the 3D space to calculate the gray value of the corresponding pixel. To extract robust features from 3D laser point clouds, both image pyramid model and point cloud pyramid model are utilized to extract multiple-scale features from PBA images and original point clouds, respectively. A Random Forest classifier is used to accomplish feature screening on extracted high-dimensional features to obtain the initial classification results. Moreover, reclassification is carried out to correct the misclassification points by remapping the classification results into the PBA images and using superpixel segmentation, which makes full use of the contextual information between laser points. Within each superpixel block, the reclassification is carried out again based on the results of the initial classification results, so as to correct some misclassification points and improve the classification accuracy. Two datasets published by ETH Zurich and MINES ParisTech are used to test the classification performance, and the results show the precision and recall rate of the proposed algorithms.

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