scholarly journals LiDAR-Based Glass Detection for Improved Occupancy Grid Mapping

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2263
Author(s):  
Haileleol Tibebu ◽  
Jamie Roche ◽  
Varuna De Silva ◽  
Ahmet Kondoz
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Test Results ◽  
New Approach ◽  
Occupancy Grid ◽  
Laser Scanners ◽  
Novel Method ◽  
Occupancy Grid Mapping ◽  
Cartesian Coordinate

Creating an accurate awareness of the environment using laser scanners is a major challenge in robotics and auto industries. LiDAR (light detection and ranging) is a powerful laser scanner that provides a detailed map of the environment. However, efficient and accurate mapping of the environment is yet to be obtained, as most modern environments contain glass, which is invisible to LiDAR. In this paper, a method to effectively detect and localise glass using LiDAR sensors is proposed. This new approach is based on the variation of range measurements between neighbouring point clouds, using a two-step filter. The first filter examines the change in the standard deviation of neighbouring clouds. The second filter uses a change in distance and intensity between neighbouring pules to refine the results from the first filter and estimate the glass profile width before updating the cartesian coordinate and range measurement by the instrument. Test results demonstrate the detection and localisation of glass and the elimination of errors caused by glass in occupancy grid maps. This novel method detects frameless glass from a long range and does not depend on intensity peak with an accuracy of 96.2%.

A novel method for extracting skeleton of fruit tree from 3D point clouds

2020 ◽  
pp. 2050051
Author(s):  
Shenglian lu ◽  
Guo Li ◽  
Jian Wang
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Fruit Trees ◽  
Fruit Tree ◽  
Tree Canopy ◽  
3D Point Clouds ◽  
Novel Method ◽  
Manual Interaction ◽  
Traditional Approaches

Tree skeleton could be useful to agronomy researchers because the skeleton describes the shape and topological structure of a tree. The phenomenon of organs’ mutual occlusion in fruit tree canopy is usually very serious, this should result in a large amount of data missing in directed laser scanning 3D point clouds from a fruit tree. However, traditional approaches can be ineffective and problematic in extracting the tree skeleton correctly when the tree point clouds contain occlusions and missing points. To overcome this limitation, we present a method for accurate and fast extracting the skeleton of fruit tree from laser scanner measured 3D point clouds. The proposed method selects the start point and endpoint of a branch from the point clouds by user’s manual interaction, then a backward searching is used to find a path from the 3D point cloud with a radius parameter as a restriction. The experimental results in several kinds of fruit trees demonstrate that our method can extract the skeleton of a leafy fruit tree with highly accuracy.

scholarly journals Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 599 ◽  
Author(s):  
Ravaglia ◽  
Fournier ◽  
Bac ◽  
Véga ◽  
Côté ◽  
...  
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Stem Diameter ◽  
Trunk Diameter ◽  
Diameter At Breast Height ◽  
Breast Height ◽  
Forest Inventories ◽  
Laser Scanners ◽  
Step Algorithm ◽  
Coniferous Plantations

Terrestrial laser scanners provide accurate and detailed point clouds of forest plots, which can be used as an alternative to destructive measurements during forest inventories. Various specialized algorithms have been developed to provide automatic and objective estimates of forest attributes from point clouds. The STEP (Snakes for Tuboid Extraction from Point cloud) algorithm was developed to estimate both stem diameter at breast height and stem diameters along the bole length. Here, we evaluate the accuracy of this algorithm and compare its performance with two other state-of-the-art algorithms that were designed for the same purpose (i.e., the CompuTree and SimpleTree algorithms). We tested each algorithm against point clouds that incorporated various degrees of noise and occlusion. We applied these algorithms to three contrasting test sites: (1) simulated scenes of coniferous stands in Newfoundland (Canada), (2) test sites of deciduous stands in Phalsbourg (France), and (3) coniferous plantations in Quebec, Canada. In most cases, the STEP algorithm predicted diameter at breast height with higher R2 and lower RMSE than the other two algorithms. The STEP algorithm also achieved greater accuracy when estimating stem diameter in occluded and noisy point clouds, with mean errors in the range of 1.1 cm to 2.28 cm. The CompuTree and SimpleTree algorithms respectively produced errors in the range of 2.62 cm to 6.1 cm and 1.03 cm to 3.34 cm, respectively. Unlike CompuTree or SimpleTree, the STEP algorithm was not able to estimate trunk diameter in the uppermost portions of the trees. Our results show that the STEP algorithm is more adapted to extract DBH and stem diameter automatically from occluded and noisy point clouds. Our study also highlights that SimpleTree and CompuTree require data filtering and results corrections. Conversely, none of these procedures were applied for the implementation of the STEP algorithm.

scholarly journals EXPLOITING INDOOR MOBILE LASER SCANNER TRAJECTORIES FOR SEMANTIC INTERPRETATION OF POINT CLOUDS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 355-362 ◽  
Author(s):  
S. Nikoohemat ◽  
M. Peter ◽  
S. Oude Elberink ◽  
G. Vosselman
Keyword(s):  
Data Collection ◽  
Feature Detection ◽  
Laser Scanner ◽  
Point Clouds ◽  
Semantic Interpretation ◽  
Indoor Environments ◽  
Valuable Source ◽  
Laser Scanners ◽  
Occlusion Reasoning

The use of Indoor Mobile Laser Scanners (IMLS) for data collection in indoor environments has been increasing in the recent years. These systems, unlike Terrestrial Laser Scanners (TLS), collect data along a trajectory instead of at discrete scanner positions. In this research, we propose several methods to exploit the trajectories of IMLS systems for the interpretation of point clouds. By means of occlusion reasoning and use of trajectory as a set of scanner positions, we are capable of detecting openings in cluttered indoor environments. In order to provide information about both the partitioning of the space and the navigable space, we use the voxel concept for point clouds. Furthermore, to reconstruct walls, floor and ceiling we exploit the indoor topology and plane primitives. The results show that the trajectory is a valuable source of data for feature detection and understanding of indoor MLS point clouds.

scholarly journals Assessing Low-Cost Terrestrial Laser Scanners for Deriving Forest Structure Parameters

2021 ◽  
Author(s):  
Atticus E. L. Stovall ◽  
Jeff W Atkins
Keyword(s):  
Structural Parameters ◽  
Low Cost ◽  
Laser Scanner ◽  
Point Clouds ◽  
Model Reconstruction ◽  
Tree Model ◽  
Factors Affecting ◽  
Laser Scanners ◽  
Tree Models ◽  
Profile Reconstruction

The increasingly affordable price point of terrestrial laser scanners has led to a democratization of instrument availability, but the most common low-cost instruments have yet to be compared in terms of the consistency to measure forest structural attributes. Here, we compared two low-cost terrestrial laser scanners (TLS): the Leica BLK360 and the Faro Focus 120 3D. We evaluate the instruments in terms of point cloud quality, forest inventory estimates, tree-model reconstruction, and foliage profile reconstruction. Our direct comparison of the point clouds showed reduced noise in filtered Leica data. Tree diameter and height were consistent across instruments (4.4% and 1.4% error, respectively). Volumetric tree models were less consistent across instruments, with ~29% bias, depending on model reconstruction quality. In the process of comparing foliage profiles, we conducted a sensitivity analysis of factors affecting foliage profile estimates, showing a minimal effect from instrument maximum range (for forests less than ~50 m in height) and surprisingly little impact from degraded scan resolution. Filtered unstructured TLS point clouds must be artificially re-gridded to provide accurate foliage profiles. The factors evaluated in this comparison point towards necessary considerations for future low-cost laser scanner development and application in detecting forest structural parameters.

scholarly journals Laser-Based Obstacle Detection at Railway Level Crossings

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Vítor Amaral ◽  
Francisco Marques ◽  
André Lourenço ◽  
José Barata ◽  
Pedro Santana
Keyword(s):  
Open Problem ◽  
Laser Scanner ◽  
Point Clouds ◽  
Obstacle Detection ◽  
Background Model ◽  
Training Phase ◽  
Level Crossing ◽  
Level Crossings ◽  
3D Point Clouds ◽  
Laser Scanners

This paper presents a system for obstacle detection in railway level crossings from 3D point clouds acquired with tilting 2D laser scanners. Although large obstacles in railway level crossings are detectable with current solutions, the detection of small obstacles remains an open problem. By relying on a tilting laser scanner, the proposed system is able to acquire highly dense and accurate point clouds, enabling the detection of small obstacles, like rocks laying near the rail. During an offline training phase, the system learns a background model of the level crossing from a set of point clouds. Then, online, obstacles are detected as occupied space contrasting with the background model. To reduce the need for manual on-site calibration, the system automatically estimates the pose of the level crossing and railway with respect to the laser scanner. Experimental results show the ability of the system to successfully perform on a set of 41 point clouds acquired in an operational one-lane level crossing.

scholarly journals 3D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATA

2015 ◽  
Vol II-3/W5 ◽  
pp. 151-157 ◽  
Author(s):  
S. Hosseinyalamdary ◽  
A. Yilmaz
Keyword(s):  
Anisotropic Diffusion ◽  
Low Cost ◽  
Laser Scanner ◽  
Super Resolution ◽  
Point Clouds ◽  
Superior Performance ◽  
Laser Scanners ◽  
Anisotropic Diffusion Equation ◽  
Tracking Object ◽  
High Level

Laser scanner point cloud has been emerging in Photogrammetry and computer vision to achieve high level tasks such as object tracking, object recognition and scene understanding. However, low cost laser scanners are noisy, sparse and prone to systematic errors. This paper proposes a novel 3D super resolution approach to reconstruct surface of the objects in the scene. This method works on sparse, unorganized point clouds and has superior performance over other surface recovery approaches. Since the proposed approach uses anisotropic diffusion equation, it does not deteriorate the object boundaries and it preserves topology of the object.

scholarly journals COMPARISON WITH ACCURACY OF TERRESTRIAL LASER SCANNER BY USING POINT CLOUD ALIGNED WITH SHAPE MATCHING AND BEST FITTING METHODS

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 535-541 ◽  
Author(s):  
T. Ogawa ◽  
Y. Hori
Keyword(s):  
Laser Scanning ◽  
Shape Matching ◽  
Laser Scanner ◽  
Point Clouds ◽  
Random Errors ◽  
Laser Scanners ◽  
Error Distributions ◽  
Best Fitting ◽  
Scan Data ◽  
Fitting In

<p><strong>Abstract.</strong> Recently operation systems of laser scanning have been obviously improved; for instance shape matching has been equipped with software on a post processing stage so measurement without any targets is a prerequisite condition of field surveying with laser scanners. Moreover a shape matching method enables us to easily register a pair of point clouds with some errors even if those data are scanned by several type scanners. Those slightly errors can influence accuracy of alignments if the object is large to require a lot of scans. Laser scanning data has random errors and accuracy of alignments can be improved by matching error distributions of pairs of point clouds to natural distributions. This method is called “best fitting” in contrast “shape matching” in a software, PolyWorks |Inspector. In this paper, accuracy of alignments between shape matching and best fitting is discussed. The scan data of three phaseshift laser scanners (FARO Focus 3D MS120, FARO Focus 3D X330 and Z+F Imager 5016) and two time-of-flight scanners (Leica BLK 360 and Leica Scan station C5) are used for analyses. Accuracy of alignments by using shape matching and best fitting methods is demonstrated by showing points of scan data with histograms of error distributions.</p>

scholarly journals NEW APPROACH FOR SEGMENTATION AND EXTRACTION OF SINGLE TREE FROM POINT CLOUDS DATA AND AERIAL IMAGES

2016 ◽  
Vol XLI-B8 ◽  
pp. 1287-1292
Author(s):  
A. S. Homainejad
Keyword(s):  
Urban Planning ◽  
3D Model ◽  
Point Clouds ◽  
Aerial Images ◽  
Science And Engineering ◽  
New Approach ◽  
Single Tree ◽  
Airborne Laser ◽  
Laser Scanners ◽  
Boundary Lines

This paper addresses a new approach for reconstructing a 3D model from single trees via Airborne Laser Scanners (ALS) data and aerial images. The approach detects and extracts single tree from ALS data and aerial images. The existing approaches are able to provide bulk segmentation from a group of trees; however, some methods focused on detection and extraction of a particular tree from ALS and images. Segmentation of a single tree within a group of trees is mostly a mission impossible since the detection of boundary lines between the trees is a tedious job and basically it is not feasible. In this approach an experimental formula based on the height of the trees was developed and applied in order to define the boundary lines between the trees. As a result, each single tree was segmented and extracted and later a 3D model was created. Extracted trees from this approach have a unique identification and attribute. The output has application in various fields of science and engineering such as forestry, urban planning, and agriculture. For example in forestry, the result can be used for study in ecologically diverse, biodiversity and ecosystem.

scholarly journals A Fast Point Clouds Registration Algorithm for Laser Scanners

2021 ◽  
Vol 11 (8) ◽  
pp. 3426
Author(s):  
Guangxuan Xu ◽  
Yajun Pang ◽  
Zhenxu Bai ◽  
Yulei Wang ◽  
Zhiwei Lu
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Shape Context ◽  
Registration Accuracy ◽  
Icp Algorithm ◽  
Laser Scanners ◽  
Intrinsic Shape ◽  
Random Sample Consensus ◽  
Voxel Grid ◽  
Ransac Algorithm

Point clouds registration is an important step for laser scanner data processing, and there have been numerous methods. However, the existing methods often suffer from low accuracy and low speed when registering large point clouds. To meet this challenge, an improved iterative closest point (ICP) algorithm combining random sample consensus (RANSAC) algorithm, intrinsic shape signatures (ISS), and 3D shape context (3DSC) is proposed. The proposed method firstly uses voxel grid filter for down-sampling. Next, the feature points are extracted by the ISS algorithm and described by the 3DSC. Afterwards, the ISS-3DSC features are used for rough registration with the RANSAC algorithm. Finally, the ICP algorithm is used for accurate registration. The experimental results show that the proposed algorithm has faster registration speed than the compared algorithms, while maintaining high registration accuracy.

scholarly journals Novel Aerial 3D Mapping System Based on UAV Platforms and 2D Laser Scanners

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
David Roca ◽  
Joaquín Martínez-Sánchez ◽  
Susana Lagüela ◽  
Pedro Arias
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
3D Mapping ◽  
The Real ◽  
3D Point Clouds ◽  
Accuracy And Precision ◽  
Mapping System ◽  
Laser Scanners ◽  
3D Mapping System ◽  
Aerial View

The acquisition of 3D geometric data from an aerial view implies a high number of advantages with respect to terrestrial acquisition, the greatest being that aerial view allows the acquisition of information from areas with no or difficult accessibility, such as roofs and tops of trees. If the aerial platform is copter-type, other advantages are present, such as the capability of displacement at very low-speed, allowing for a more detailed acquisition. This paper presents a novel Aerial 3D Mapping System based on a copter-type platform, where a 2D laser scanner is integrated with a GNSS sensor and an IMU for the generation of georeferenced 3D point clouds. The accuracy and precision of the system are evaluated through the measurement of geometries in the point clouds generated by the system, as well as through the geolocation of target points for which the real global coordinates are known.

Sign in / Sign up

Export Citation Format

Share Document