3D Reconstruction using a sparse laser scanner and a single camera for outdoor autonomous vehicle

2016 ◽  
Author(s):  
Honggu Lee ◽  
Soohwan Song ◽  
Sungho Jo
Keyword(s):  
3D Reconstruction ◽  
Autonomous Vehicle ◽  
Laser Scanner ◽  
Single Camera

scholarly journals A Normalization scheme for Terrestrial LiDAR Intensity Data by Range and Incidence Angle

2018 ◽  
Author(s):  
Sandeep Sasidharan
Keyword(s):  
Incidence Angle ◽  
Autonomous Vehicle ◽  
Laser Scanner ◽  
Spectral Information ◽  
Intensity Data ◽  
Automatic Registration ◽  
Terrestrial Lidar ◽  
Intensity Information ◽  
Laser Scanners ◽  
Normalization Scheme

Automatic registration, classification and segmentation of Terrestrial Laser Scanner (TLS) data are of great interest in Geoinformatics & Autonomous vehicle research. Along with dense and accurate 3D geometric data, laser scanners also collect return intensity information. Inclusion of this spectral information has potential to improve the working of the above mentioned processes. However, these intensity values need to be normalized, prior to their use, as they are subject to a large number of errors. This paper presents a technique to carry out normalization of intensity values using the range and incidence angle corrections. The developed approach has been tested on a large number of data and results are found satisfactory.

Terrestrial laser scanner based 3D reconstruction of trees and retrieval of leaf area index in a forest environment

2019 ◽  
Vol 53 ◽  
pp. 100986 ◽  
Author(s):  
Indu Indirabai ◽  
M.V. Harindranathan Nair ◽  
R. Nair Jaishanker ◽  
Rama Rao Nidamanuri
Keyword(s):  
3D Reconstruction ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Laser Scanner ◽  
Terrestrial Laser Scanner ◽  
Forest Environment ◽  
Area Index

scholarly journals A comparison of multi-view 3D reconstruction of a rock wall using several cameras and a laser scanner

2014 ◽  
Vol XL-5 ◽  
pp. 573-580 ◽  
Author(s):  
K. Thoeni ◽  
A. Giacomini ◽  
R. Murtagh ◽  
E. Kniest
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Laser Scanner ◽  
Ground Truth ◽  
Point Clouds ◽  
Digital Cameras ◽  
Rock Wall ◽  
Geological Features ◽  
Dense Point ◽  
Sharp Edges

This work presents a comparative study between multi-view 3D reconstruction using various digital cameras and a terrestrial laser scanner (TLS). Five different digital cameras were used in order to estimate the limits related to the camera type and to establish the minimum camera requirements to obtain comparable results to the ones of the TLS. The cameras used for this study range from commercial grade to professional grade and included a GoPro Hero 1080 (5 Mp), iPhone 4S (8 Mp), Panasonic Lumix LX5 (9.5 Mp), Panasonic Lumix ZS20 (14.1 Mp) and Canon EOS 7D (18 Mp). The TLS used for this work was a FARO Focus 3D laser scanner with a range accuracy of ±2 mm. The study area is a small rock wall of about 6 m height and 20 m length. The wall is partly smooth with some evident geological features, such as non-persistent joints and sharp edges. Eight control points were placed on the wall and their coordinates were measured by using a total station. These coordinates were then used to georeference all models. A similar number of images was acquired from a distance of between approximately 5 to 10 m, depending on field of view of each camera. The commercial software package PhotoScan was used to process the images, georeference and scale the models, and to generate the dense point clouds. Finally, the open-source package CloudCompare was used to assess the accuracy of the multi-view results. Each point cloud obtained from a specific camera was compared to the point cloud obtained with the TLS. The latter is taken as ground truth. The result is a coloured point cloud for each camera showing the deviation in relation to the TLS data. The main goal of this study is to quantify the quality of the multi-view 3D reconstruction results obtained with various cameras as objectively as possible and to evaluate its applicability to geotechnical problems.

scholarly journals THREE DIMENTIONAL RECONSTRUCTION OF LARGE CULTURAL HERITAGE OBJECTS BASED ON UAV VIDEO AND TLS DATA

2016 ◽  
Vol XLI-B5 ◽  
pp. 985-988 ◽  
Author(s):  
Z. Xu ◽  
T. H. Wu ◽  
Y. Shen ◽  
L. Wu
Keyword(s):  
3D Reconstruction ◽  
Cultural Heritage ◽  
Laser Scanner ◽  
Digital Camera ◽  
Point Clouds ◽  
Endurance Capacity ◽  
Still Images ◽  
Culture Heritage ◽  
Invariant Regions ◽  
Cultural Heritage Objects

This paper investigates the synergetic use of unmanned aerial vehicle (UAV) and terrestrial laser scanner (TLS) in 3D reconstruction of cultural heritage objects. Rather than capturing still images, the UAV that equips a consumer digital camera is used to collect dynamic videos to overcome its limited endurance capacity. Then, a set of 3D point-cloud is generated from video image sequences using the automated structure-from-motion (SfM) and patch-based multi-view stereo (PMVS) methods. The TLS is used to collect the information that beyond the reachability of UAV imaging e.g., partial building facades. A coarse to fine method is introduced to integrate the two sets of point clouds UAV image-reconstruction and TLS scanning for completed 3D reconstruction. For increased reliability, a variant of ICP algorithm is introduced using local terrain invariant regions in the combined designation. The experimental study is conducted in the Tulou culture heritage building in Fujian province, China, which is focused on one of the TuLou clusters built several hundred years ago. Results show a digital 3D model of the Tulou cluster with complete coverage and textural information. This paper demonstrates the usability of the proposed method for efficient 3D reconstruction of heritage object based on UAV video and TLS data.

scholarly journals Birds Eye View Look-Up Table Estimation with Semantic Segmentation

2021 ◽  
Vol 11 (17) ◽  
pp. 8047
Author(s):  
Dongkyu Lee ◽  
Wee Peng Tay ◽  
Seok-Cheol Kee
Keyword(s):  
Pose Estimation ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Synthetic Data ◽  
Ground Truth ◽  
Image Plane ◽  
Semantic Segmentation ◽  
Single Camera ◽  
Look Up Table ◽  
Camera Pose

In this work, a study was carried out to estimate a look-up table (LUT) that converts a camera image plane to a birds eye view (BEV) plane using a single camera. The traditional camera pose estimation fields require high costs in researching and manufacturing autonomous vehicles for the future and may require pre-configured infra. This paper proposes an autonomous vehicle driving camera calibration system that is low cost and utilizes low infra. A network that outputs an image in the form of an LUT that converts the image into a BEV by estimating the camera pose under urban road driving conditions using a single camera was studied. We propose a network that predicts human-like poses from a single image. We collected synthetic data using a simulator, made BEV and LUT as ground truth, and utilized the proposed network and ground truth to train pose estimation function. In the progress, it predicts the pose by deciphering the semantic segmentation feature and increases its performance by attaching a layer that handles the overall direction of the network. The network outputs camera angle (roll/pitch/yaw) on the 3D coordinate system so that the user can monitor learning. Since the network's output is a LUT, there is no need for additional calculation, and real-time performance is improved.

Three-Dimensional Reconstruction of Maize Plants and Extraction of Phenotypic Parameters Based On Laser Point Cloud Data

2021 ◽  
Author(s):  
Chengxin Ju ◽  
Yuanyuan Zhao ◽  
Fengfeng Wu ◽  
Rui Li ◽  
Tianle Yang ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Plant Height ◽  
Point Cloud ◽  
Agricultural Research ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Cloud Data ◽  
Scanning Method ◽  
Cloud Data ◽  
Maize Plants

Abstract Background: Three-dimensional (3D) laser scanning technology could rapidly extract the surface geometric features of maize plants to achieve non-destructive monitoring of maize phenotypes. However, extracting the phenotypic parameters of maize plants based on laser point cloud data is challenging.Methods: In this paper, a rotational scanning method was used to collect the data of potted maize point cloud from different perspectives by using a laser scanner. Maize point cloud data were grid-reconstructed and aligned based on greedy projection triangulation algorithm and iterative closest point (ICP) algorithm, and the random sampling consistency algorithm was used to segment the stem and leaf point clouds of single maize plant to obtain the plant height and leaf parameters.Results: The results showed that the R2 between the predicted plant height and the measured plant height was above 0.95, and the R2 of the predicted leaf length, leaf width and leaf area were 0.938, 0878 and 0.956 respectively when compared with the measured values.Conclusions: The 3D reconstruction of maize plants using the laser scanner showed a good performance, and the phenotypic parameters obtained based on the reconstructed 3D model had high accuracy. The results were helpful to the practical application of plant 3D reconstruction and provided guidance for plant parameter acquisition and theoretical methods for intelligent agricultural research.

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