Extrinsic Calibration of 2D Laser Rangefinders Using an Existing Cuboid-Shaped Corridor as the Reference

Laser rangefinders (LRFs) are widely used in autonomous systems for indoor positioning and mobile mapping through the simultaneous localization and mapping (SLAM) approach. The extrinsic parameters of multiple LRFs need to be determined, and they are one of the key factors impacting system performance. This study presents an extrinsic calibration method of multiple LRFs that requires neither extra calibration sensors nor special artificial reference landmarks. Instead, it uses a naturally existing cuboid-shaped corridor as the calibration reference, and it hence needs no additional cost. The present method takes advantage of two types of geometric constraints for the calibration, which can be found in a common cuboid-shaped corridor. First, the corresponding point cloud is scanned by the set of LRFs. Second, the lines that are scanned on the corridor surfaces are extracted from the point cloud. Then, the lines within the same surface and the lines within two adjacent surfaces satisfy the coplanarity constraint and the orthogonality constraint, respectively. As such, the calibration problem is converted into a nonlinear optimization problem with the constraints. Simulation experiments and experiments based on real data verified the feasibility and stability of the proposed method.

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Extrinsic LiDAR/Ground Calibration Method Using 3D Geometrical Plane-Based Estimation

Sensors ◽

10.3390/s20102841 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2841

Author(s):

Mohammad Ali Zaiter ◽

Régis Lherbier ◽

Ghaleb Faour ◽

Oussama Bazzi ◽

Jean-Charles Noyer

Keyword(s):

Ground Plane ◽

Ground Surface ◽

Real Data ◽

Calibration Method ◽

Parameters Estimation ◽

Parameters Optimization ◽

Method Performance ◽

Extrinsic Calibration ◽

Extrinsic Parameters ◽

The Stability

This paper details a new extrinsic calibration method for scanning laser rangefinder that is precisely focused on the geometrical ground plane-based estimation. This method is also efficient in the challenging experimental configuration of a high angle of inclination of the LiDAR. In this configuration, the calibration of the LiDAR sensor is a key problem that can be be found in various domains and in particular to guarantee the efficiency of ground surface object detection. The proposed extrinsic calibration method can be summarized by the following procedure steps: fitting ground plane, extrinsic parameters estimation (3D orientation angles and altitude), and extrinsic parameters optimization. Finally, the results are presented in terms of precision and robustness against the variation of LiDAR’s orientation and range accuracy, respectively, showing the stability and the accuracy of the proposed extrinsic calibration method, which was validated through numerical simulation and real data to prove the method performance.

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Simulation of simultaneous localization and mapping using 3D point cloud data

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v16.i2.pp941-949 ◽

2019 ◽

Vol 16 (2) ◽

pp. 941

Author(s):

Shuzlina Abdul-Rahman ◽

Mohamad Soffi Abd Razak ◽

Aliya Hasanah Binti Mohd Mushin ◽

Raseeda Hamzah ◽

Nordin Abu Bakar ◽

...

Keyword(s):

Point Cloud ◽

Simultaneous Localization And Mapping ◽

Real Data ◽

Light Detection And Ranging ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Localization And Mapping ◽

Graph Slam ◽

Robotic Application

<span>Abstract—This paper presents a simulation study of Simultaneous Localization and Mapping (SLAM) using 3D point cloud data from Light Detection and Ranging (LiDAR) technology. Methods like simulation is useful to simplify the process of learning algorithms particularly when collecting and annotating large volumes of real data is impractical and expensive. In this study, a map of a given environment was constructed in Robotic Operating System platform with Gazebo Simulator. The paper begins by presenting the most currently popular algorithm that are widely used in SLAM namely Extended Kalman Filter, Graph SLAM and Fast SLAM. The study performed the simulations by using standard SLAM with Turtlebot and Husky robots. Husky robot was further compared with ACML algorithm. The results showed that Hector SLAM could reach the goal faster than ACML algorithm in a pre-defined map. Further studies in this field with other SLAM algorithms would certainly beneficial to many parties due to the demands of robotic application.</span>

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Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target

Applied Sciences ◽

10.3390/app11020582 ◽

2021 ◽

Vol 11 (2) ◽

pp. 582

Author(s):

Zean Bu ◽

Changku Sun ◽

Peng Wang ◽

Hang Dong

Keyword(s):

Simulated Data ◽

Real Data ◽

Calibration Method ◽

Multiple Sensors ◽

Triangular Pyramid ◽

World Coordinate System ◽

Flash Lidar ◽

Novel Method ◽

3D Information ◽

Incremental Validation

Calibration between multiple sensors is a fundamental procedure for data fusion. To address the problems of large errors and tedious operation, we present a novel method to conduct the calibration between light detection and ranging (LiDAR) and camera. We invent a calibration target, which is an arbitrary triangular pyramid with three chessboard patterns on its three planes. The target contains both 3D information and 2D information, which can be utilized to obtain intrinsic parameters of the camera and extrinsic parameters of the system. In the proposed method, the world coordinate system is established through the triangular pyramid. We extract the equations of triangular pyramid planes to find the relative transformation between two sensors. One capture of camera and LiDAR is sufficient for calibration, and errors are reduced by minimizing the distance between points and planes. Furthermore, the accuracy can be increased by more captures. We carried out experiments on simulated data with varying degrees of noise and numbers of frames. Finally, the calibration results were verified by real data through incremental validation and analyzing the root mean square error (RMSE), demonstrating that our calibration method is robust and provides state-of-the-art performance.

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Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual–Real Integration

Remote Sensing ◽

10.3390/rs13152868 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2868

Author(s):

Yonglin Tian ◽

Xiao Wang ◽

Yu Shen ◽

Zhongzheng Guo ◽

Zilei Wang ◽

...

Keyword(s):

Point Cloud ◽

Closed Loop ◽

Real Data ◽

Point Clouds ◽

Autonomous Driving ◽

Training Model ◽

Labor Cost ◽

Open Loop ◽

Training Dataset ◽

Data Annotation

Three-dimensional information perception from point clouds is of vital importance for improving the ability of machines to understand the world, especially for autonomous driving and unmanned aerial vehicles. Data annotation for point clouds is one of the most challenging and costly tasks. In this paper, we propose a closed-loop and virtual–real interactive point cloud generation and model-upgrading framework called Parallel Point Clouds (PPCs). To our best knowledge, this is the first time that the training model has been changed from an open-loop to a closed-loop mechanism. The feedback from the evaluation results is used to update the training dataset, benefiting from the flexibility of artificial scenes. Under the framework, a point-based LiDAR simulation model is proposed, which greatly simplifies the scanning operation. Besides, a group-based placing method is put forward to integrate hybrid point clouds, via locating candidate positions for virtual objects in real scenes. Taking advantage of the CAD models and mobile LiDAR devices, two hybrid point cloud datasets, i.e., ShapeKITTI and MobilePointClouds, are built for 3D detection tasks. With almost zero labor cost on data annotation for newly added objects, the models (PointPillars) trained with ShapeKITTI and MobilePointClouds achieved 78.6% and 60.0% of the average precision of the model trained with real data on 3D detection, respectively.

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3D Mesh Pre-processing Method Based on Feature Point Classification and Anisotropic Vertex Denoising Considering Scene Structure Characteristics

Remote Sensing ◽

10.3390/rs13112145 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2145

Author(s):

Yawen Liu ◽

Bingxuan Guo ◽

Xiongwu Xiao ◽

Wei Qiu

Keyword(s):

Real Data ◽

Geometric Constraints ◽

Feature Point ◽

Feature Points ◽

Denoising Method ◽

3D Mesh ◽

Mesh Denoising ◽

Scene Structure ◽

Sharp Edges ◽

Update Strategy

3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this paper, we propose a novel feature-preserving mesh denoising method, which was based on robust guidance normal estimation, accurate feature point extraction and an anisotropic vertex denoising strategy. The methodology of the proposed approach is as follows: (1) The dual weight function that takes into account the angle characteristics is used to estimate the guidance normals of the surface, which improved the reliability of the joint bilateral filtering algorithm and avoids losing the corner structures; (2) The filtered facet normal is used to classify the feature points based on the normal voting tensor (NVT) method, which raised the accuracy and integrity of feature classification for the noisy model; (3) The anisotropic vertex update strategy is used in triangular mesh denoising: updating the non-feature points with isotropic neighborhood normals, which effectively suppressed the sharp edges from being smoothed; updating the feature points based on local geometric constraints, which preserved and restored the features while avoided sharp pseudo features. The detailed quantitative and qualitative analyses conducted on synthetic and real data show that our method can remove the noise of various mesh models and retain or restore the edge and corner features of the model without generating pseudo features.

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A novel extrinsic calibration method for robotic systems with structured light sensors

2015 IEEE International Conference on Robotics and Biomimetics (ROBIO) ◽

10.1109/robio.2015.7419109 ◽

2015 ◽

Author(s):

Zhichao Cui ◽

Yuehu Liu ◽

Xinzhao Li

Keyword(s):

Structured Light ◽

Calibration Method ◽

Robotic Systems ◽

Extrinsic Calibration

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Investigating the Impact of Competition and Incentive Design on Performance of Crowdfunding Projects: A Case of Independent Movies

Journal of theoretical and applied electronic commerce research ◽

10.3390/jtaer16040045 ◽

2021 ◽

Vol 16 (4) ◽

pp. 791-810

Author(s):

Li Chen

Keyword(s):

Real Data ◽

Theoretical Explanation ◽

Research Model ◽

Key Factors ◽

Incentive Design ◽

Competition Intensity ◽

Academic Researchers ◽

Competition Pressure ◽

The Impact ◽

Funding Level

Recently, crowdfunding has become a popular e-commerce model based on web 2.0 platforms for fundraisers to collect funding from a large group of supporters using the Internet. However, many projects failed to reach their funding targets. Despite the growing interest of academic researchers and e-commerce professionals in identifying drivers of crowdfunding success, important factors like competition and incentive design have not received much attention in prior research. In this study we aim to fill this gap by investigating the impact of competition and incentive design on the performance of crowdfunding projects. Drawing upon literature of entrepreneurship, we develop a research model involving key factors such as competition intensity and the number of reward levels. Using real data of 209 independent movie projects of an online crowdfunding platform, we test the proposed hypotheses of the impact of competition and incentive design on crowdfunding success. Our results show that competition plays a significant role in crowdfunding performance. The higher competition pressure is, the lower performance of crowdfunding projects will be. We also find that factors such as the number of reward levels and the plan of attending movie festivals are essential to the success of crowdfunding projects, but the funding level of getting the top reward does not exert a significant impact. Our study contributes to the e-commerce literature by further exploring the mechanism of crowdfunding success with theoretical explanation and empirical evidence. Researchers and professionals can apply our theoretical findings regarding competition and incentive design in other e-commerce platforms. Furthermore, our results provide useful managerial insights and operational policies for project founders and managers of crowdfunding platforms.

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User-Driven Computer-Assisted Reverse Engineering of Editable CAD Assembly Models

Journal of Computing and Information Science in Engineering ◽

10.1115/1.4053150 ◽

2021 ◽

pp. 1-16

Author(s):

Ghazanfar Ali Shah ◽

Jean-Philippe Pernot ◽

Arnaud Polette ◽

Franca Giannini ◽

Marina Monti

Keyword(s):

Reverse Engineering ◽

Point Cloud ◽

Point Clouds ◽

Geometric Constraints ◽

Computer Assisted ◽

Reconstruction Process ◽

Mechanical Parts ◽

Starting Point ◽

Cad Models ◽

Incomplete Datasets

Abstract This paper introduces a novel reverse engineering technique for the reconstruction of editable CAD models of mechanical parts' assemblies. The input is a point cloud of a mechanical parts' assembly that has been acquired as a whole, i.e. without disassembling it prior to its digitization. The proposed framework allows for the reconstruction of the parametric CAD assembly model through a multi-step reconstruction and fitting approach. It is modular and it supports various exploitation scenarios depending on the available data and starting point. It also handles incomplete datasets. The reconstruction process starts from roughly sketched and parameterized geometries (i.e 2D sketches, 3D parts or assemblies) that are then used as input of a simulated annealing-based fitting algorithm, which minimizes the deviation between the point cloud and the reconstructed geometries. The coherence of the CAD models is maintained by a CAD modeler that performs the updates and satisfies the geometric constraints as the fitting process goes on. The optimization process leverages a two-level filtering technique able to capture and manage the boundaries of the geometries inside the overall point cloud in order to allow for local fitting and interfaces detection. It is a user-driven approach where the user decides what are the most suitable steps and sequence to operate. It has been tested and validated on both real scanned point clouds and as-scanned virtually generated point clouds incorporating several artifacts that would appear with real acquisition devices.

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