scholarly journals MOVING OBJECTS AWARE SENSOR MESH FUSION FOR INDOOR RECONSTRUCTION FROM A COUPLE OF 2D LIDAR SCANS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 507-514
Author(s):  
T. Wu ◽  
B. Vallet ◽  
C. Demonceaux ◽  
J. Liu
Keyword(s):  
Point Cloud ◽  
Moving Objects ◽  
Lidar Data ◽  
Natural Topology ◽  
Reconstruction Methods ◽  
3D Camera ◽  
Static Part ◽  
Boolean Optimization ◽  
2D And 3D ◽  
Very High

Abstract. Indoor mapping attracts more attention with the development of 2D and 3D camera and Lidar sensor. Lidar systems can provide a very high resolution and accurate point cloud. When aiming to reconstruct the static part of the scene, moving objects should be detected and removed which can prove challenging. This paper proposes a generic method to merge meshes produced from Lidar data that allows to tackle the issues of moving objects removal and static scene reconstruction at once. The method is adapted to a platform collecting point cloud from two Lidar sensors with different scan direction, which will result in different quality. Firstly, a mesh is efficiently produced from each sensor by exploiting its natural topology. Secondly, a visibility analysis is performed to handle occlusions (due to varying viewpoints) and remove moving objects. Then, a boolean optimization allows to select which triangles should be removed from each mesh. Finally, a stitching method is used to connect the selected mesh pieces. Our method is demonstrated on a Navvis M3 (2D laser ranger system) dataset and compared with Poisson and Delaunay based reconstruction methods.

scholarly journals CLASSIFICATION OF INFORMAL SETTLEMENTS THROUGH THE INTEGRATION OF 2D AND 3D FEATURES EXTRACTED FROM UAV DATA

2016 ◽  
Vol III-3 ◽  
pp. 317-324
Author(s):  
C. M. Gevaert ◽  
C. Persello ◽  
R. Sliuzas ◽  
G. Vosselman
Keyword(s):  
Point Cloud ◽  
Informal Settlement ◽  
Informal Settlements ◽  
Classification Problems ◽  
Roof Material ◽  
Topographic Features ◽  
Urban Scene ◽  
2D And 3D ◽  
Very High

Unmanned Aerial Vehicles (UAVs) are capable of providing very high resolution and up-to-date information to support informal settlement upgrading projects. In order to provide accurate basemaps, urban scene understanding through the identification and classification of buildings and terrain is imperative. However, common characteristics of informal settlements such as small, irregular buildings with heterogeneous roof material and large presence of clutter challenge state-of-the-art algorithms. Especially the dense buildings and steeply sloped terrain cause difficulties in identifying elevated objects. This work investigates how 2D radiometric and textural features, 2.5D topographic features, and 3D geometric features obtained from UAV imagery can be integrated to obtain a high classification accuracy in challenging classification problems for the analysis of informal settlements. It compares the utility of pixel-based and segment-based features obtained from an orthomosaic and DSM with point-based and segment-based features extracted from the point cloud to classify an unplanned settlement in Kigali, Rwanda. Findings show that the integration of 2D and 3D features leads to higher classification accuracies.

scholarly journals LiDAR-Based System and Optical VHR Data for Building Detection and Mapping

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1285 ◽  
Author(s):  
Silvia Liberata Ullo ◽  
Chiara Zarro ◽  
Konrad Wojtowicz ◽  
Giuseppe Meoli ◽  
Mariano Focareta
Keyword(s):  
Environmental Monitoring ◽  
Case Studies ◽  
Urban Areas ◽  
Lidar Data ◽  
Building Detection ◽  
The Earth ◽  
Areas Of Interest ◽  
2D And 3D ◽  
3D Lidar ◽  
Very High

The aim of this paper is to highlight how the employment of Light Detection and Ranging (LiDAR) technique can enhance greatly the performance and reliability of many monitoring systems applied to the Earth Observation (EO) and Environmental Monitoring. A short presentation of LiDAR systems, underlying their peculiarities, is first given. References to some review papers are highlighted, as they can be regarded as useful guidelines for researchers interested in using LiDARs. Two case studies are then presented and discussed, based on the use of 2D and 3D LiDAR data. Some considerations are done on the performance achieved through the use of LiDAR data combined with data from other sources. The case studies show how the LiDAR-based systems, combined with optical Very High Resolution (VHR) data, succeed in improving the analysis and monitoring of specific areas of interest, specifically how LiDAR data help in exploring external environment and extracting building features from urban areas. Moreover the discussed Case Studies demonstrate that the use of the LiDAR data, even with a low density of points, allows the development of an automatic procedure for accurate building features extraction, through object-oriented classification techniques, therefore by underlying the importance that even simple LiDAR-based systems play in EO and Environmental Monitoring.

scholarly journals LiDAR Data Enrichment by Fusing Spatial and Temporal Adjacent Frames

2021 ◽  
Vol 13 (18) ◽  
pp. 3640
Author(s):  
Hao Fu ◽  
Hanzhang Xue ◽  
Xiaochang Hu ◽  
Bokai Liu
Keyword(s):  
Sparse Representation ◽  
Real Time ◽  
Point Cloud ◽  
Moving Objects ◽  
Autonomous Driving ◽  
Experimental Results ◽  
Lidar Data ◽  
Identification Algorithm ◽  
Data Enrichment ◽  
A New Technique

In autonomous driving scenarios, the point cloud generated by LiDAR is usually considered as an accurate but sparse representation. In order to enrich the LiDAR point cloud, this paper proposes a new technique that combines spatial adjacent frames and temporal adjacent frames. To eliminate the “ghost” artifacts caused by moving objects, a moving point identification algorithm is introduced that employs the comparison between range images. Experiments are performed on the publicly available Semantic KITTI dataset. Experimental results show that the proposed method outperforms most of the previous approaches. Compared with these previous works, the proposed method is the only method that can run in real-time for online usage.

scholarly journals CLASSIFICATION OF INFORMAL SETTLEMENTS THROUGH THE INTEGRATION OF 2D AND 3D FEATURES EXTRACTED FROM UAV DATA

2016 ◽  
Vol III-3 ◽  
pp. 317-324 ◽  
Author(s):  
C. M. Gevaert ◽  
C. Persello ◽  
R. Sliuzas ◽  
G. Vosselman
Keyword(s):  
Point Cloud ◽  
Informal Settlement ◽  
Informal Settlements ◽  
Classification Problems ◽  
Roof Material ◽  
Topographic Features ◽  
Urban Scene ◽  
2D And 3D ◽  
Very High

Unmanned Aerial Vehicles (UAVs) are capable of providing very high resolution and up-to-date information to support informal settlement upgrading projects. In order to provide accurate basemaps, urban scene understanding through the identification and classification of buildings and terrain is imperative. However, common characteristics of informal settlements such as small, irregular buildings with heterogeneous roof material and large presence of clutter challenge state-of-the-art algorithms. Especially the dense buildings and steeply sloped terrain cause difficulties in identifying elevated objects. This work investigates how 2D radiometric and textural features, 2.5D topographic features, and 3D geometric features obtained from UAV imagery can be integrated to obtain a high classification accuracy in challenging classification problems for the analysis of informal settlements. It compares the utility of pixel-based and segment-based features obtained from an orthomosaic and DSM with point-based and segment-based features extracted from the point cloud to classify an unplanned settlement in Kigali, Rwanda. Findings show that the integration of 2D and 3D features leads to higher classification accuracies.

scholarly journals Slice-Based Instance and Semantic Segmentation for Low-Channel Roadside LiDAR Data

2020 ◽  
Vol 12 (22) ◽  
pp. 3830
Author(s):  
Hui Liu ◽  
Ciyun Lin ◽  
Dayong Wu ◽  
Bowen Gong
Keyword(s):  
Point Cloud ◽  
Moving Objects ◽  
Regional Growth ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Lidar Data ◽  
Seed Point ◽  
Growth Method ◽  
The Impact ◽  
Instance Segmentation

More and more scholars are committed to light detection and ranging (LiDAR) as a roadside sensor to obtain traffic flow data. Filtering and clustering are common methods to extract pedestrians and vehicles from point clouds. This kind of method ignores the impact of environmental information on traffic. The segmentation process is a crucial part of detailed scene understanding, which could be especially helpful for locating, recognizing, and classifying objects in certain scenarios. However, there are few studies on the segmentation of low-channel (16 channels in this paper) roadside 3D LiDAR. This paper presents a novel segmentation (slice-based) method for point clouds of roadside LiDAR. The proposed method can be divided into two parts: the instance segmentation part and semantic segmentation part. The part of the instance segmentation of point cloud is based on the regional growth method, and we proposed a seed point generation method for low-channel LiDAR data. Furthermore, we optimized the instance segmentation effect under occlusion. The part of semantic segmentation of a point cloud is realized by classifying and labeling the objects obtained by instance segmentation. For labeling static objects, we represented and classified a certain object through the related features derived from its slices. For labeling moving objects, we proposed a recurrent neural network (RNN)-based model, of which the accuracy could be up to 98.7%. The result implies that the slice-based method can obtain a good segmentation effect and the slice has good potential for point cloud segmentation.

scholarly journals Semantic 3D Mapping from Deep Image Segmentation

2021 ◽  
Vol 11 (4) ◽  
pp. 1953
Author(s):  
Francisco Martín ◽  
Fernando González ◽  
José Miguel Guerrero ◽  
Manuel Fernández ◽  
Jonatan Ginés
Keyword(s):  
Point Cloud ◽  
Autonomous Mobile Robots ◽  
Semantic Mapping ◽  
Indoor Environments ◽  
3D Mapping ◽  
Direct Mapping ◽  
Learning Techniques ◽  
3D Camera ◽  
Deep Image ◽  
Boundary Pixel

The perception and identification of visual stimuli from the environment is a fundamental capacity of autonomous mobile robots. Current deep learning techniques make it possible to identify and segment objects of interest in an image. This paper presents a novel algorithm to segment the object’s space from a deep segmentation of an image taken by a 3D camera. The proposed approach solves the boundary pixel problem that appears when a direct mapping from segmented pixels to their correspondence in the point cloud is used. We validate our approach by comparing baseline approaches using real images taken by a 3D camera, showing that our method outperforms their results in terms of accuracy and reliability. As an application of the proposed algorithm, we present a semantic mapping approach for a mobile robot’s indoor environments.

scholarly journals Computational Geometry-Based Surface Reconstruction for Volume Estimation: A Case Study on Magnitude-Frequency Relations for a LiDAR-Derived Rockfall Inventory

2021 ◽  
Vol 10 (3) ◽  
pp. 157
Author(s):  
Paul-Mark DiFrancesco ◽  
David A. Bonneau ◽  
D. Jean Hutchinson
Keyword(s):  
Computational Geometry ◽  
Surface Reconstruction ◽  
Power Law ◽  
Point Cloud ◽  
Point Clouds ◽  
Volume Estimation ◽  
Estimation Methods ◽  
Small Scale ◽  
Reconstruction Methods ◽  
3D Volume

Key to the quantification of rockfall hazard is an understanding of its magnitude-frequency behaviour. Remote sensing has allowed for the accurate observation of rockfall activity, with methods being developed for digitally assembling the monitored occurrences into a rockfall database. A prevalent challenge is the quantification of rockfall volume, whilst fully considering the 3D information stored in each of the extracted rockfall point clouds. Surface reconstruction is utilized to construct a 3D digital surface representation, allowing for an estimation of the volume of space that a point cloud occupies. Given various point cloud imperfections, it is difficult for methods to generate digital surface representations of rockfall with detailed geometry and correct topology. In this study, we tested four different computational geometry-based surface reconstruction methods on a database comprised of 3668 rockfalls. The database was derived from a 5-year LiDAR monitoring campaign of an active rock slope in interior British Columbia, Canada. Each method resulted in a different magnitude-frequency distribution of rockfall. The implications of 3D volume estimation were demonstrated utilizing surface mesh visualization, cumulative magnitude-frequency plots, power-law fitting, and projected annual frequencies of rockfall occurrence. The 3D volume estimation methods caused a notable shift in the magnitude-frequency relations, while the power-law scaling parameters remained relatively similar. We determined that the optimal 3D volume calculation approach is a hybrid methodology comprised of the Power Crust reconstruction and the Alpha Solid reconstruction. The Alpha Solid approach is to be used on small-scale point clouds, characterized with high curvatures relative to their sampling density, which challenge the Power Crust sampling assumptions.

scholarly journals EXTRACTING MOBILE OBJECTS IN IMAGES USING A VELODYNE LIDAR POINT CLOUD

2015 ◽  
Vol II-3/W4 ◽  
pp. 247-253 ◽  
Author(s):  
B. Vallet ◽  
W. Xiao ◽  
M. Brédif
Keyword(s):  
Point Cloud ◽  
Mobile Object ◽  
Object A ◽  
Mobile Objects ◽  
Dempster Shafer Theory ◽  
Static Part ◽  
Shafer Theory

This paper presents a full pipeline to extract mobile objects in images based on a simultaneous laser acquisition with a Velodyne scanner. The point cloud is first analysed to extract mobile objects in 3D. This is done using Dempster-Shafer theory and it results in weights telling for each points if it corresponds to a mobile object, a fixed object or if no decision can be made based on the data (unknown). These weights are projected in an image acquired simultaneously and used to segment the image between the mobile and the static part of the scene.

scholarly journals Fast Geometric Surface Based Segmentation of Point Cloud from Lidar Data

2019 ◽  
pp. 415-423 ◽  
Author(s):  
Aritra Mukherjee ◽  
Sourya Dipta Das ◽  
Jasorsi Ghosh ◽  
Ananda S. Chowdhury ◽  
Sanjoy Kumar Saha
Keyword(s):  
Point Cloud ◽  
Lidar Data ◽  
Geometric Surface
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