Research

21 pages, 8049 KiB

Open AccessArticle

Cross-Section Deformation Analysis and Visualization of Shield Tunnel Based on Mobile Tunnel Monitoring System

by Haili Sun, Shuang Liu, Ruofei Zhong and Liming Du

Sensors 2020, 20(4), 1006; https://doi.org/10.3390/s20041006 - 13 Feb 2020

Cited by 29 | Viewed by 3740

With the ongoing developments in laser scanning technology, applications for describing tunnel deformation using rich point cloud data have become a significant topic of investigation. This study describes the independently developed a mobile tunnel monitoring system called the second version of Tunnel Scan [...] Read more.

With the ongoing developments in laser scanning technology, applications for describing tunnel deformation using rich point cloud data have become a significant topic of investigation. This study describes the independently developed a mobile tunnel monitoring system called the second version of Tunnel Scan developed by Capital Normal University (CNU-TS-2) for data acquisition, which has an electric system to control its forward speed and is compatible with various laser scanners such as the Faro and Leica models. A comparison with corresponding data acquired by total station data demonstrates that the data collected by CNU-TS-2 is accurate. Following data acquisition, the overall and local deformation of the tunnel is determined by denoising and 360° deformation analysis of the point cloud data. To enhance the expression of the analysis results, this study proposes an expansion of the tunnel point cloud data into a two-dimensional image via cylindrical projection, followed by an expression of the tunnel deformation through color difference to visualize the deformation. Compared with the three-dimensional modeling method of visualization, this method is easier to implement and facilitates storage. In addition, it is conducive to the performance of comprehensive analysis of problems such as water leakage in the tunnel, thereby achieving the effect of multiple uses for a single image. Full article

(This article belongs to the Special Issue 3D Point Clouds for Intelligent Road Transportation Systems)

► Show Figures

Figure 1

18 pages, 21183 KiB

Open AccessArticle

Voxel-FPN: Multi-Scale Voxel Feature Aggregation for 3D Object Detection from LIDAR Point Clouds

by Hongwu Kuang, Bei Wang, Jianping An, Ming Zhang and Zehan Zhang

Sensors 2020, 20(3), 704; https://doi.org/10.3390/s20030704 - 28 Jan 2020

Cited by 125 | Viewed by 7408

Abstract

Object detection in point cloud data is one of the key components in computer vision systems, especially for autonomous driving applications. In this work, we present Voxel-Feature Pyramid Network, a novel one-stage 3D object detector that utilizes raw data from LIDAR sensors only. [...] Read more.

Object detection in point cloud data is one of the key components in computer vision systems, especially for autonomous driving applications. In this work, we present Voxel-Feature Pyramid Network, a novel one-stage 3D object detector that utilizes raw data from LIDAR sensors only. The core framework consists of an encoder network and a corresponding decoder followed by a region proposal network. Encoder extracts and fuses multi-scale voxel information in a bottom-up manner, whereas decoder fuses multiple feature maps from various scales by Feature Pyramid Network in a top-down way. Extensive experiments show that the proposed method has better performance on extracting features from point data and demonstrates its superiority over some baselines on the challenging KITTI-3D benchmark, obtaining good performance on both speed and accuracy in real-world scenarios. Full article

(This article belongs to the Special Issue 3D Point Clouds for Intelligent Road Transportation Systems)

► Show Figures

Figure 1

17 pages, 1358 KiB

Open AccessArticle

Rapid Motion Segmentation of LiDAR Point Cloud Based on a Combination of Probabilistic and Evidential Approaches for Intelligent Vehicles

by Kichun Jo, Sumyeong Lee, Chansoo Kim and Myoungho Sunwoo

Sensors 2019, 19(19), 4116; https://doi.org/10.3390/s19194116 - 23 Sep 2019

Cited by 5 | Viewed by 3106

Abstract

Point clouds from light detecting and ranging (LiDAR) sensors represent increasingly important information for environmental object detection and classification of automated and intelligent vehicles. Objects in the driving environment can be classified as either

d y n a m i c

or [...] Read more.

Point clouds from light detecting and ranging (LiDAR) sensors represent increasingly important information for environmental object detection and classification of automated and intelligent vehicles. Objects in the driving environment can be classified as either

d y n a m i c

or

s t a t i c

depending on their movement characteristics. A LiDAR point cloud is also segmented into

d y n a m i c

and

s t a t i c

points based on the motion properties of the measured objects. The segmented motion information of a point cloud can be useful for various functions in automated and intelligent vehicles. This paper presents a fast motion segmentation algorithm that segments a LiDAR point cloud into

d y n a m i c

and

s t a t i c

points in real-time. The segmentation algorithm classifies the motion of the latest point cloud based on the LiDAR’s laser beam characteristics and the geometrical relationship between consecutive LiDAR point clouds. To accurately and reliably estimate the motion state of each LiDAR point considering the measurement uncertainty, both probability theory and evidence theory are employed in the segmentation algorithm. The probabilistic and evidential algorithm segments the point cloud into three classes:

d y n a m i c

,

s t a t i c

, and

u n k n o w n

. Points are placed in the

u n k n o w n

class when LiDAR point cloud is not sufficient for motion segmentation. The point motion segmentation algorithm was evaluated quantitatively and qualitatively through experimental comparisons with previous motion segmentation methods. Full article

(This article belongs to the Special Issue 3D Point Clouds for Intelligent Road Transportation Systems)

► Show Figures

Figure 1

Journal Menu

Journal Browser

3D Point Clouds for Intelligent Road Transportation Systems

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (3 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI