scholarly journals Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5048
Author(s):  
Wei Chen ◽  
Qianjie Liu ◽  
Huosheng Hu ◽  
Jun Liu ◽  
Shaojie Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Autonomous Robots ◽  
Back Propagation ◽  
Point Clouds ◽  
Obstacle Detection ◽  
Gaussian Kernel ◽  
Sobel Operator ◽  
And Performance ◽  
Edge Features

Obstacle detection is one of the essential capabilities for autonomous robots operated on unstructured terrain. In this paper, a novel laser-based approach is proposed for obstacle detection by autonomous robots, in which the Sobel operator is deployed in the edge-detection process of 3D laser point clouds. The point clouds of unstructured terrain are filtered by VoxelGrid, and then processed by the Gaussian kernel function to obtain the edge features of obstacles. The Euclidean clustering algorithm is optimized by super-voxel in order to cluster the point clouds of each obstacle. The characteristics of the obstacles are recognized by the Levenberg–Marquardt back-propagation (LM-BP) neural network. The algorithm proposed in this paper is a post-processing algorithm based on the reconstructed point cloud. Experiments are conducted by using both the existing datasets and real unstructured terrain point cloud reconstructed by an all-terrain robot to demonstrate the feasibility and performance of the proposed approach.

scholarly journals A Single Point-Based Multilevel Features Fusion and Pyramid Neighborhood Optimization Method for ALS Point Cloud Classification

2019 ◽  
Vol 9 (5) ◽  
pp. 951 ◽  
Author(s):  
Yong Li ◽  
Guofeng Tong ◽  
Xiance Du ◽  
Xiang Yang ◽  
Jianjun Zhang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Single Point ◽  
Point Clouds ◽  
Optimization Method ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Angle Distribution ◽  
Features Fusion ◽  
Cloud Classification ◽  
Point Cloud Classification

3D point cloud classification has wide applications in the field of scene understanding. Point cloud classification based on points can more accurately segment the boundary region between adjacent objects. In this paper, a point cloud classification algorithm based on a single point multilevel features fusion and pyramid neighborhood optimization are proposed for a Airborne Laser Scanning (ALS) point cloud. First, the proposed algorithm determines the neighborhood region of each point, after which the features of each single point are extracted. For the characteristics of the ALS point cloud, two new feature descriptors are proposed, i.e., a normal angle distribution histogram and latitude sampling histogram. Following this, multilevel features of a single point are constructed by multi-resolution of the point cloud and multi-neighborhood spaces. Next, the features are trained by the Support Vector Machine based on a Gaussian kernel function, and the points are classified by the trained model. Finally, a classification results optimization method based on a multi-scale pyramid neighborhood constructed by a multi-resolution point cloud is used. In the experiment, the algorithm is tested by a public dataset. The experimental results show that the proposed algorithm can effectively classify large-scale ALS point clouds. Compared with the existing algorithms, the proposed algorithm has a better classification performance.

scholarly journals A PROJECTION-BASED RECONSTRUCTION ALGORITHM FOR 3D MODELING OF BRIDGE STRUCTURES FROM DRONE-BASED POINT CLOUD

2021 ◽  
Vol XLVI-4/W1-2021 ◽  
pp. 77-83
Author(s):  
M. Mehranfar ◽  
H. Arefi ◽  
F. Alidoost
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
3D Model ◽  
Reconstruction Algorithm ◽  
Point Clouds ◽  
3D Models ◽  
Reconstruction Technique ◽  
Reconstruction Process ◽  
Median Error ◽  
Bridge Structures

Abstract. This paper presents a projection-based method for 3D bridge modeling using dense point clouds generated from drone-based images. The proposed workflow consists of hierarchical steps including point cloud segmentation, modeling of individual elements, and merging of individual models to generate the final 3D model. First, a fuzzy clustering algorithm including the height values and geometrical-spectral features is employed to segment the input point cloud into the main bridge elements. In the next step, a 2D projection-based reconstruction technique is developed to generate a 2D model for each element. Next, the 3D models are reconstructed by extruding the 2D models orthogonally to the projection plane. Finally, the reconstruction process is completed by merging individual 3D models and forming an integrated 3D model of the bridge structure in a CAD format. The results demonstrate the effectiveness of the proposed method to generate 3D models automatically with a median error of about 0.025 m between the elements’ dimensions in the reference and reconstructed models for two different bridge datasets.

scholarly journals A Fast Spatial Clustering Method for Sparse LiDAR Point Clouds Using GPU Programming

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2309
Author(s):  
Yifei Tian ◽  
Wei Song ◽  
Long Chen ◽  
Yunsick Sung ◽  
Jeonghoon Kwak ◽  
...  
Keyword(s):  
Point Cloud ◽  
Spatial Clustering ◽  
Point Clouds ◽  
Obstacle Detection ◽  
Small Cells ◽  
Gpu Programming ◽  
Processing Unit ◽  
Height Distribution ◽  
Connected Component ◽  
Accurate Perception

Fast and accurate obstacle detection is essential for accurate perception of mobile vehicles’ environment. Because point clouds sensed by light detection and ranging (LiDAR) sensors are sparse and unstructured, traditional obstacle clustering on raw point clouds are inaccurate and time consuming. Thus, to achieve fast obstacle clustering in an unknown terrain, this paper proposes an elevation-reference connected component labeling (ER-CCL) algorithm using graphic processing unit (GPU) programing. LiDAR points are first projected onto a rasterized x–z plane so that sparse points are mapped into a series of regularly arranged small cells. Based on the height distribution of the LiDAR point, the ground cells are filtered out and a flag map is generated. Next, the ER-CCL algorithm is implemented on the label map generated from the flag map to mark individual clusters with unique labels. Finally, obstacle labeling results are inverse transformed from the x–z plane to 3D points to provide clustering results. For real-time 3D point cloud clustering, ER-CCL is accelerated by running it in parallel with the aid of GPU programming technology.

scholarly journals CLASSIFICATION AND INTEGRATION OF MASSIVE 3D POINTS CLOUDS IN A VIRTUAL REALITY (VR) ENVIRONMENT

2019 ◽  
Vol XLII-2/W17 ◽  
pp. 165-171 ◽  
Author(s):  
A. Kharroubi ◽  
R. Hajji ◽  
R. Billen ◽  
F. Poux
Keyword(s):  
Virtual Reality ◽  
Open Source ◽  
Point Cloud ◽  
Mixed Reality ◽  
Automatic Segmentation ◽  
Point Clouds ◽  
3D Point Clouds ◽  
3D Data ◽  
Segmentation Approach ◽  
And Performance

Abstract. With the increasing volume of 3D applications using immersive technologies such as virtual, augmented and mixed reality, it is very interesting to create better ways to integrate unstructured 3D data such as point clouds as a source of data. Indeed, this can lead to an efficient workflow from 3D capture to 3D immersive environment creation without the need to derive 3D model, and lengthy optimization pipelines. In this paper, the main focus is on the direct classification and integration of massive 3D point clouds in a virtual reality (VR) environment. The emphasis is put on leveraging open-source frameworks for an easy replication of the findings. First, we develop a semi-automatic segmentation approach to provide semantic descriptors (mainly classes) to groups of points. We then build an octree data structure leveraged through out-of-core algorithms to load in real time and continuously only the points that are in the VR user's field of view. Then, we provide an open-source solution using Unity with a user interface for VR point cloud interaction and visualisation. Finally, we provide a full semantic VR data integration enhanced through developed shaders for future spatio-semantic queries. We tested our approach on several datasets of which a point cloud composed of 2.3 billion points, representing the heritage site of the castle of Jehay (Belgium). The results underline the efficiency and performance of the solution for visualizing classifieds massive point clouds in virtual environments with more than 100 frame per second.

scholarly journals 3D RECONSTRUCTION WITH A COLLABORATIVE APPROACH BASED ON SMARTPHONES AND A CLOUD-BASED SERVER

2017 ◽  
Vol XLII-2/W8 ◽  
pp. 187-194 ◽  
Author(s):  
E. Nocerino ◽  
F. Poiesi ◽  
A. Locher ◽  
Y. T. Tefera ◽  
F. Remondino ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Image Matching ◽  
Point Cloud ◽  
Point Clouds ◽  
Bundle Adjustment ◽  
Collaborative Approach ◽  
Matching Algorithm ◽  
Dense Image ◽  
Video Feed ◽  
And Performance

The paper presents a collaborative image-based 3D reconstruction pipeline to perform image acquisition with a smartphone and geometric 3D reconstruction on a server during concurrent or disjoint acquisition sessions. Images are selected from the video feed of the smartphone’s camera based on their quality and novelty. The smartphone’s app provides on-the-fly reconstruction feedback to users co-involved in the acquisitions. The server is composed of an incremental SfM algorithm that processes the received images by seamlessly merging them into a single sparse point cloud using bundle adjustment. Dense image matching algorithm can be lunched to derive denser point clouds. The reconstruction details, experiments and performance evaluation are presented and discussed.

scholarly journals Automatic Processing Method for Deformation Monitoring of Circle Tunnels Based on 3D LiDAR Data

2018 ◽  
Author(s):  
Xiongyao Xie ◽  
Mingrui Zhao ◽  
Jiamin He ◽  
Biao Zhou
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Clustering Algorithm ◽  
Single Point ◽  
Point Clouds ◽  
Automatic Processing ◽  
Deformation Monitoring ◽  
Processing Method ◽  
Health State ◽  
3D Lidar

The application of 3D LiDAR technology has become increasingly extensive in tunnel monitoring due to the large density and high accuracy of the acquired spatial data. The proposed processing method aims at circle tunnels and provides a clear workflow to automatically process raw point data and easily interpretable results to analyze tunnel health state. The proposed automatic processing method employs a series of algorithms to extract point cloud of a single tunnel segment without obvious noise from entire raw tunnel point cloud mainly by three steps: axis acquisition, segments extraction and denoising. Tunnel axis is extracted by fitting boundaries of the tunnel point cloud rejection in plane with RANSAC algorithm. With guidance of axis, the entire preprocessed tunnel point cloud is segmented by equal division to get a section of tunnel point cloud which corresponds to a single tunnel segment. Then the noise in every single point cloud segment is removed by clustering algorithm twice, based on distance and intensity. Finally, clean point clouds of tunnel segments are processed by effective deformation extraction processor to get ovality and three-dimensional deformation nephogram.

A New Approach for 3D Edge Extraction by Fusing Point Clouds and Digital Images

2013 ◽  
Vol 457-458 ◽  
pp. 1012-1016
Author(s):  
Ying Wang ◽  
Daniel Ewert ◽  
Daniel Schilberg ◽  
Sabina Jeschke
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Microsoft Kinect ◽  
Range Image ◽  
Edge Extraction ◽  
New Approach ◽  
Cloud Data ◽  
Cloud Processing ◽  
Image Pixels ◽  
Edge Features

Edges are crucial features for object segmentation and classification in both image and point cloud processing. Though many research efforts have been made in edge extraction and enhancement in both areas, their applications are limited respectively owing to their own technical properties. This paper presents a new approach to integrating the edge pixels in the 2D image into boundary data in the 3D point cloud by establishing the mapping relationship between these two types of data to represent the 3D edge features of the object. The 3D edge extraction based on the adoption of Microsoft Kinect as a 3D sensor - involves the following three steps: first, the generation of a range image from the point cloud of the object, second the edge extraction in the range image and edge extraction in the digital image, and finally edge data integration by referring to the correspondence map between point cloud data and image pixels.

scholarly journals PAIRWISE LINKAGE FOR POINT CLOUD SEGMENTATION

2016 ◽  
Vol III-3 ◽  
pp. 201-208 ◽  
Author(s):  
Xiaohu Lu ◽  
Jian Yao ◽  
Jinge Tu ◽  
Kai Li ◽  
Li Li ◽  
...  
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Input Parameter ◽  
Point Clouds ◽  
Segmentation Algorithm ◽  
Experimental Results ◽  
3D Point Cloud ◽  
3D Point Clouds ◽  
Point Cloud Segmentation ◽  
Data Point

In this paper, we first present a novel hierarchical clustering algorithm named Pairwise Linkage (P-Linkage), which can be used for clustering any dimensional data, and then effectively apply it on 3D unstructured point cloud segmentation. The P-Linkage clustering algorithm first calculates a feature value for each data point, for example, the density for 2D data points and the flatness for 3D point clouds. Then for each data point a pairwise linkage is created between itself and its closest neighboring point with a greater feature value than its own. The initial clusters can further be discovered by searching along the linkages in a simple way. After that, a cluster merging procedure is applied to obtain the finally refined clustering result, which can be designed for specialized applications. Based on the P-Linkage clustering, we develop an efficient segmentation algorithm for 3D unstructured point clouds, in which the flatness of the estimated surface of a 3D point is used as its feature value. For each initial cluster a slice is created, then a novel and robust slicemerging method is proposed to get the final segmentation result. The proposed P-Linkage clustering and 3D point cloud segmentation algorithms require only one input parameter in advance. Experimental results on different dimensional synthetic data from 2D to 4D sufficiently demonstrate the efficiency and robustness of the proposed P-Linkage clustering algorithm and a large amount of experimental results on the Vehicle-Mounted, Aerial and Stationary Laser Scanner point clouds illustrate the robustness and efficiency of our proposed 3D point cloud segmentation algorithm.

scholarly journals PAIRWISE LINKAGE FOR POINT CLOUD SEGMENTATION

2016 ◽  
Vol III-3 ◽  
pp. 201-208 ◽  
Author(s):  
Xiaohu Lu ◽  
Jian Yao ◽  
Jinge Tu ◽  
Kai Li ◽  
Li Li ◽  
...  
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Input Parameter ◽  
Point Clouds ◽  
Segmentation Algorithm ◽  
Experimental Results ◽  
3D Point Cloud ◽  
3D Point Clouds ◽  
Point Cloud Segmentation ◽  
Data Point

In this paper, we first present a novel hierarchical clustering algorithm named Pairwise Linkage (P-Linkage), which can be used for clustering any dimensional data, and then effectively apply it on 3D unstructured point cloud segmentation. The P-Linkage clustering algorithm first calculates a feature value for each data point, for example, the density for 2D data points and the flatness for 3D point clouds. Then for each data point a pairwise linkage is created between itself and its closest neighboring point with a greater feature value than its own. The initial clusters can further be discovered by searching along the linkages in a simple way. After that, a cluster merging procedure is applied to obtain the finally refined clustering result, which can be designed for specialized applications. Based on the P-Linkage clustering, we develop an efficient segmentation algorithm for 3D unstructured point clouds, in which the flatness of the estimated surface of a 3D point is used as its feature value. For each initial cluster a slice is created, then a novel and robust slicemerging method is proposed to get the final segmentation result. The proposed P-Linkage clustering and 3D point cloud segmentation algorithms require only one input parameter in advance. Experimental results on different dimensional synthetic data from 2D to 4D sufficiently demonstrate the efficiency and robustness of the proposed P-Linkage clustering algorithm and a large amount of experimental results on the Vehicle-Mounted, Aerial and Stationary Laser Scanner point clouds illustrate the robustness and efficiency of our proposed 3D point cloud segmentation algorithm.

Adaptive Moving Least-Squares Surfaces for Multiple Point Clouds Registration

2011 ◽  
Author(s):  
Yunbao Huang ◽  
Linchi Zhang ◽  
Zhihui Tan ◽  
Qifu Wang ◽  
Liping Chen
Keyword(s):  
Least Squares ◽  
Point Cloud ◽  
Synthetic Data ◽  
Point Clouds ◽  
Integral Invariant ◽  
Gaussian Kernel ◽  
Moving Least Squares ◽  
Multiple Point ◽  
Surface Model ◽  
Core Idea

In this paper, we propose an Adaptive Moving Least-Squares (AMLS) surface based approach for multi-view or multi-sensor point cloud ICP registration. The core idea of this approach is to reconstruct a smooth and accurate surface, e. s. AMLS surface, from a point cloud, without data segmentation and surface model selection, resulting in an accurate point-to-AMLS surface ICP registration. The major difference between AMLS and traditional MLS is that the width of Gaussian kernel is adaptively scaled with the principle curvature, which is defined through local integral invariant analysis. Experimental results of both synthetic data and scanned data from a mechanical part show that the presented approach is more accurate and robust on sensor noise and sample density.

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