scholarly journals POINT CLOUD REFINEMENT WITH A TARGET-FREE INTRINSIC CALIBRATION OF A MOBILE MULTI-BEAM LIDAR SYSTEM

2016 ◽  
Vol XLI-B3 ◽  
pp. 359-366
Author(s):  
H. Nouiraa ◽  
J. E. Deschaud ◽  
F. Goulettea
Keyword(s):  
Reference Frame ◽  
Real Data ◽  
Point Clouds ◽  
The Body ◽  
Mobile Mapping ◽  
Extrinsic Calibration ◽  
Body Reference ◽  
3D Information ◽  
Calibration Parameters ◽  
Mapping Systems

LIDAR sensors are widely used in mobile mapping systems. The mobile mapping platforms allow to have fast acquisition in cities for example, which would take much longer with static mapping systems. The LIDAR sensors provide reliable and precise 3D information, which can be used in various applications: mapping of the environment; localization of objects; detection of changes. Also, with the recent developments, multi-beam LIDAR sensors have appeared, and are able to provide a high amount of data with a high level of detail. <br><br> A mono-beam LIDAR sensor mounted on a mobile platform will have an extrinsic calibration to be done, so the data acquired and registered in the sensor reference frame can be represented in the body reference frame, modeling the mobile system. For a multibeam LIDAR sensor, we can separate its calibration into two distinct parts: on one hand, we have an extrinsic calibration, in common with mono-beam LIDAR sensors, which gives the transformation between the sensor cartesian reference frame and the body reference frame. On the other hand, there is an intrinsic calibration, which gives the relations between the beams of the multi-beam sensor. This calibration depends on a model given by the constructor, but the model can be non optimal, which would bring errors and noise into the acquired point clouds. In the litterature, some optimizations of the calibration parameters are proposed, but need a specific routine or environment, which can be constraining and time-consuming. <br><br> In this article, we present an automatic method for improving the intrinsic calibration of a multi-beam LIDAR sensor, the Velodyne HDL-32E. The proposed approach does not need any calibration target, and only uses information from the acquired point clouds, which makes it simple and fast to use. Also, a corrected model for the Velodyne sensor is proposed. <br><br> An energy function which penalizes points far from local planar surfaces is used to optimize the different proposed parameters for the corrected model, and we are able to give a confidence value for the calibration parameters found. Optimization results on both synthetic and real data are presented.

scholarly journals POINT CLOUD REFINEMENT WITH A TARGET-FREE INTRINSIC CALIBRATION OF A MOBILE MULTI-BEAM LIDAR SYSTEM

2016 ◽  
Vol XLI-B3 ◽  
pp. 359-366
Author(s):  
H. Nouiraa ◽  
J. E. Deschaud ◽  
F. Goulettea
Keyword(s):  
Reference Frame ◽  
Real Data ◽  
Point Clouds ◽  
The Body ◽  
Mobile Mapping ◽  
Extrinsic Calibration ◽  
Body Reference ◽  
3D Information ◽  
Calibration Parameters ◽  
Mapping Systems

LIDAR sensors are widely used in mobile mapping systems. The mobile mapping platforms allow to have fast acquisition in cities for example, which would take much longer with static mapping systems. The LIDAR sensors provide reliable and precise 3D information, which can be used in various applications: mapping of the environment; localization of objects; detection of changes. Also, with the recent developments, multi-beam LIDAR sensors have appeared, and are able to provide a high amount of data with a high level of detail. <br><br> A mono-beam LIDAR sensor mounted on a mobile platform will have an extrinsic calibration to be done, so the data acquired and registered in the sensor reference frame can be represented in the body reference frame, modeling the mobile system. For a multibeam LIDAR sensor, we can separate its calibration into two distinct parts: on one hand, we have an extrinsic calibration, in common with mono-beam LIDAR sensors, which gives the transformation between the sensor cartesian reference frame and the body reference frame. On the other hand, there is an intrinsic calibration, which gives the relations between the beams of the multi-beam sensor. This calibration depends on a model given by the constructor, but the model can be non optimal, which would bring errors and noise into the acquired point clouds. In the litterature, some optimizations of the calibration parameters are proposed, but need a specific routine or environment, which can be constraining and time-consuming. <br><br> In this article, we present an automatic method for improving the intrinsic calibration of a multi-beam LIDAR sensor, the Velodyne HDL-32E. The proposed approach does not need any calibration target, and only uses information from the acquired point clouds, which makes it simple and fast to use. Also, a corrected model for the Velodyne sensor is proposed. <br><br> An energy function which penalizes points far from local planar surfaces is used to optimize the different proposed parameters for the corrected model, and we are able to give a confidence value for the calibration parameters found. Optimization results on both synthetic and real data are presented.

scholarly journals Extraction of Guardrails from MMS Data Using Convolutional Neural Network

2021 ◽  
Vol 15 (3) ◽  
pp. 258-267
Author(s):  
Hiroki Matsumoto ◽  
◽  
Yuma Mori ◽  
Hiroshi Masuda
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Asset Management ◽  
Extraction Method ◽  
Point Clouds ◽  
Mobile Mapping ◽  
Method Point ◽  
Mobile Mapping Systems ◽  
Capture Point ◽  
Mapping Systems

Mobile mapping systems can capture point clouds and digital images of roadside objects. Such data are useful for maintenance, asset management, and 3D map creation. In this paper, we discuss methods for extracting guardrails that separate roadways and walkways. Since there are various shape patterns for guardrails in Japan, flexible methods are required for extracting them. We propose a new extraction method based on point processing and a convolutional neural network (CNN). In our method, point clouds and images are segmented into small fragments, and their features are extracted using CNNs for images and point clouds. Then, features from images and point clouds are combined and investigated using whether they are guardrails or not. Based on our experiments, our method could extract guardrails from point clouds with a high success rate.

scholarly journals CLASSIFICATION OF RAILWAY ASSETS IN MOBILE MAPPING POINT CLOUDS

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 219-225
Author(s):  
M. Corongiu ◽  
A. Masiero ◽  
G. Tucci
Keyword(s):  
Spatial Information ◽  
Point Clouds ◽  
Railway Track ◽  
Neural Net ◽  
Mobile Mapping ◽  
Geospatial Information ◽  
Railway System ◽  
Point Neighborhood ◽  
Mobile Mapping Systems ◽  
Mapping Systems

Abstract. Nowadays, mobile mapping systems are widely used to quickly collect reliable geospatial information of relatively large areas: thanks to such characteristics, the number of applications and fields exploiting their usage is continuously increasing. Among such possible applications, mobile mapping systems have been recently considered also by railway system managers to quickly produce and update a database of the geospatial features of such system, also called assets. Despite several vehicles, devices and acquisition methods can be considered for the data collection of the railway system, the predominant one is probably that based on the use of a mobile mapping system mounted on a train, which moves all along the railway tracks, enabling the 3D reproduction of the entire railway track area.Given the large amount of data collected by such mobile mapping, automatic procedures have to be used to speed up the process of extracting the spatial information of interest, i.e. assets positions and characteristics.This paper considers the problem of extracting such information for what concerns cantilever and portal masts, by exploiting a mixed approach. First, a set of candidate areas are extracted and pre-processed by considering certain of their geometric characteristics, mainly extracted by using eigenvalues of the covariance matrix of a point neighborhood. Then, a 3D modified Fisher vector-deep learning neural net is used to classify the candidates. Tests on such approach are conducted in two areas of the Italian railway system.

scholarly journals Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features

2019 ◽  
Vol 11 (16) ◽  
pp. 1955 ◽  
Author(s):  
Markus Hillemann ◽  
Martin Weinmann ◽  
Markus S. Mueller ◽  
Boris Jutzi
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Laser Scanner ◽  
The Self ◽  
Geometric Features ◽  
Mobile Mapping ◽  
Extrinsic Calibration ◽  
Self Calibration ◽  
Mobile Mapping Systems ◽  
Mapping Systems

Mobile Mapping is an efficient technology to acquire spatial data of the environment. The spatial data is fundamental for applications in crisis management, civil engineering or autonomous driving. The extrinsic calibration of the Mobile Mapping System is a decisive factor that affects the quality of the spatial data. Many existing extrinsic calibration approaches require the use of artificial targets in a time-consuming calibration procedure. Moreover, they are usually designed for a specific combination of sensors and are, thus, not universally applicable. We introduce a novel extrinsic self-calibration algorithm, which is fully automatic and completely data-driven. The fundamental assumption of the self-calibration is that the calibration parameters are estimated the best when the derived point cloud represents the real physical circumstances the best. The cost function we use to evaluate this is based on geometric features which rely on the 3D structure tensor derived from the local neighborhood of each point. We compare different cost functions based on geometric features and a cost function based on the Rényi quadratic entropy to evaluate the suitability for the self-calibration. Furthermore, we perform tests of the self-calibration on synthetic and two different real datasets. The real datasets differ in terms of the environment, the scale and the utilized sensors. We show that the self-calibration is able to extrinsically calibrate Mobile Mapping Systems with different combinations of mapping and pose estimation sensors such as a 2D laser scanner to a Motion Capture System and a 3D laser scanner to a stereo camera and ORB-SLAM2. For the first dataset, the parameters estimated by our self-calibration lead to a more accurate point cloud than two comparative approaches. For the second dataset, which has been acquired via a vehicle-based mobile mapping, our self-calibration achieves comparable results to a manually refined reference calibration, while it is universally applicable and fully automated.

scholarly journals Equivalence of Euler equations and torque-angular momentum relation

2021 ◽  
Vol 18 (1) ◽  
pp. 136
Author(s):  
V. Tanriverdi
Keyword(s):  
Angular Momentum ◽  
Rigid Body ◽  
Reference Frame ◽  
Euler Equations ◽  
Equations Of Motion ◽  
The Body ◽  
Moments Of Inertia ◽  
Body Reference ◽  
Stationary Reference Frame ◽  
Momentum Relation

Euler derived equations for rigid body rotations in the body reference frame and in the stationary reference frame by considering an infinitesimal part of the rigid body.Another derivation is possible, and it is widely used: transforming torque-angular momentum relation to the body reference frame.However, their equivalence is not shown explicitly.In this work, for a rigid body with different moments of inertia, we calculated Euler equations explicitly in the body reference frame and in the stationary reference frame and torque-angular momentum relation.We also calculated equations of motion from Lagrangian.These calculations show that all four of them are equivalent.

scholarly journals POST-MISSION QUALITY ASSURANCE PROCEDURE FOR SURVEY-GRADE MOBILE MAPPING SYSTEMS

2016 ◽  
Vol XLI-B1 ◽  
pp. 647-652
Author(s):  
A. P. Kerstinga ◽  
P. Friess
Keyword(s):  
Quality Assurance ◽  
Asset Management ◽  
Mobile Mapping ◽  
Quality Assurance Procedure ◽  
Self Calibration ◽  
Position Errors ◽  
Position And Orientation ◽  
Mobile Mapping Systems ◽  
Calibration Parameters ◽  
Mapping Systems

Mobile Mapping Systems (MMS) consist of terrestrial-based moving platforms that integrate a set of imaging sensors (typically digital cameras and laser scanners) and a Position and Orientation System (POS), designed to collect data of the surrounding environment. MMS can be classified as “mapping-grade” or “survey-grade” depending on the system’s attainable accuracy. Mapping-grade MMS produce geospatial data suitable for GIS applications (e.g., asset management) while survey-grade systems should satisfy high-accuracy applications such as engineering/design projects. The delivered accuracy of an MMS is dependent on several factors such as the accuracy of the system measurements and calibration parameters. It is critical, especially for survey-grade systems, to implement a robust Quality Assurance (QA) procedure to ensure the achievement of the expected accuracy. In this paper, a new post-mission QA procedure is presented. The presented method consists of a fully-automated self-calibration process that allows for the estimation of corrections to the system calibration parameters (e.g., boresight angles and lever-arm offsets relating the lidar sensor(s) to the IMU body frame) as well as corrections to the system measurements (e.g., post-processed trajectory position and orientation, scan angles and ranges). As for the system measurements, the major challenge for MMS is related to the trajectory determination in the presence of multipath signals and GNSS outages caused by buildings, underpasses and high vegetation. In the proposed self-calibration method, trajectory position errors are properly modelled while utilizing an efficient/meaningful trajectory segmentation technique. The validity of the proposed method is demonstrated using a dataset collected under unfavorable GNSS conditions.

Pothole Mapping and Patching Quantity Estimates using LiDAR-Based Mobile Mapping Systems

2020 ◽  
Vol 2674 (9) ◽  
pp. 124-134
Author(s):  
Radhika Ravi ◽  
Ayman Habib ◽  
Darcy Bullock
Keyword(s):  
Point Clouds ◽  
Relative Accuracy ◽  
Mobile Mapping ◽  
Cloud Data ◽  
Pavement Distress ◽  
The Road ◽  
Mapping System ◽  
Mobile Mapping System ◽  
The Cost ◽  
Mapping Systems

Pavement distress or pothole mapping is important to public agencies responsible for maintaining roadways. The efficient capture of 3D point cloud data using mapping systems equipped with LiDAR eliminates the time-consuming and labor-intensive manual classification and quantity estimates. This paper proposes a methodology to map potholes along the road surface using ultra-high accuracy LiDAR units onboard a wheel-based mobile mapping system. LiDAR point clouds are processed to detect and report the location and severity of potholes by identifying the below-road 3D points pertaining to potholes, along with their depths. The surface area and volume of each detected pothole is also estimated along with the volume of its minimum bounding box to serve as an aide to choose the ideal method of repair as well as to estimate the cost of repair. The proposed approach was tested on a 10 mi-long segment on a U.S. Highway and it is observed to accurately detect potholes with varying severity and different causes. A sample of potholes detected in a 1 mi segment has been reported in the experimental results of this paper. The point clouds generated using the system are observed to have a single-track relative accuracy of less than ±1 cm and a multi-track relative accuracy of ±1–2 cm, which has been verified through comparing point clouds captured by different sensors from different tracks.

scholarly journals Fully Automated Profile-based Calibration Strategy for Airborne and Terrestrial Mobile LiDAR Systems with Spinning Multi-beam Laser Units

2020 ◽  
Vol 12 (3) ◽  
pp. 401 ◽  
Author(s):  
Ravi ◽  
Habib
Keyword(s):  
Point Clouds ◽  
Mobile Systems ◽  
Full Potential ◽  
Mobile Mapping ◽  
Beam Laser ◽  
3D Point Clouds ◽  
Feature Based ◽  
True Values ◽  
Mobile Mapping Systems ◽  
Mapping Systems

LiDAR-based mobile mapping systems (MMS) are rapidly gaining popularity for a multitude of applications due to their ability to provide complete and accurate 3D point clouds for any and every scene of interest. However, an accurate calibration technique for such systems is needed in order to unleash their full potential. In this paper, we propose a fully automated profile-based strategy for the calibration of LiDAR-based MMS. The proposed technique is validated by comparing its accuracy against the expected point positioning accuracy for the point cloud based on the used sensors’ specifications. The proposed strategy was seen to reduce the misalignment between different tracks from approximately 2 to 3 m before calibration down to less than 2 cm after calibration for airborne as well as terrestrial mobile LiDAR mapping systems. In other words, the proposed calibration strategy can converge to correct estimates of mounting parameters, even in cases where the initial estimates are significantly different from the true values. Furthermore, the results from the proposed strategy are also verified by comparing them to those from an existing manually-assisted feature-based calibration strategy. The major contribution of the proposed strategy is its ability to conduct the calibration of airborne and wheel-based mobile systems without any requirement for specially designed targets or features in the surrounding environment. The above claims are validated using experimental results conducted for three different MMS – two airborne and one terrestrial – with one or more LiDAR unit.

scholarly journals Scalable and Detail-Preserving Ground Surface Reconstruction from Large 3D Point Clouds Acquired by Mobile Mapping Systems

2014 ◽  
Vol XL-3 ◽  
pp. 73-80
Author(s):  
D. Craciun ◽  
A. Serna Morales ◽  
J.-E. Deschaud ◽  
B. Marcotegui ◽  
F. Goulette
Keyword(s):  
Surface Reconstruction ◽  
Large Scale ◽  
Ground Surface ◽  
Point Clouds ◽  
Reconstruction Algorithms ◽  
Mobile Mapping ◽  
3D Point Clouds ◽  
Mesh Shrinkage ◽  
Mobile Mapping Systems ◽  
Mapping Systems

The currently existing mobile mapping systems equipped with active 3D sensors allow to acquire the environment with high sampling rates at high vehicle velocities. While providing an effective solution for environment sensing over large scale distances, such acquisition provides only a discrete representation of the geometry. Thus, a continuous map of the underlying surface must be built. Mobile acquisition introduces several constraints for the state-of-the-art surface reconstruction algorithms. Smoothing becomes a difficult task for recovering sharp depth features while avoiding mesh shrinkage. In addition, interpolation-based techniques are not suitable for noisy datasets acquired by Mobile Laser Scanning (MLS) systems. Furthermore, scalability is a major concern for enabling real-time rendering over large scale distances while preserving geometric details. This paper presents a fully automatic ground surface reconstruction framework capable to deal with the aforementioned constraints. The proposed method exploits the quasi-flat geometry of the ground throughout a morphological segmentation algorithm. Then, a planar Delaunay triangulation is applied in order to reconstruct the ground surface. A smoothing procedure eliminates high frequency peaks, while preserving geometric details in order to provide a regular ground surface. Finally, a decimation step is applied in order to cope with scalability constraints over large scale distances. Experimental results on real data acquired in large urban environments are presented and a performance evaluation with respect to ground truth measurements demonstrate the effectiveness of our method.

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