scholarly journals MEMS Mirrors for LiDAR: A Review

Micromachines ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 456 ◽  
Author(s):  
Dingkang Wang ◽  
Connor Watkins ◽  
Huikai Xie
Keyword(s):  
Autonomous Vehicles ◽  
Figure Of Merit ◽  
Three Dimensional ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Field Of View ◽  
Preliminary Assessment ◽  
Wide Range ◽  
Laser Scanners ◽  
Increasing Demand

In recent years, Light Detection and Ranging (LiDAR) has been drawing extensive attention both in academia and industry because of the increasing demand for autonomous vehicles. LiDAR is believed to be the crucial sensor for autonomous driving and flying, as it can provide high-density point clouds with accurate three-dimensional information. This review presents an extensive overview of Microelectronechanical Systems (MEMS) scanning mirrors specifically for applications in LiDAR systems. MEMS mirror-based laser scanners have unrivalled advantages in terms of size, speed and cost over other types of laser scanners, making them ideal for LiDAR in a wide range of applications. A figure of merit (FoM) is defined for MEMS mirrors in LiDAR scanners in terms of aperture size, field of view (FoV) and resonant frequency. Various MEMS mirrors based on different actuation mechanisms are compared using the FoM. Finally, a preliminary assessment of off-the-shelf MEMS scanned LiDAR systems is given.

The Impact of Mental States on Semi-autonomous Driving Takeover Performance: A Systematic Review

2020 ◽  
Vol 64 (1) ◽  
pp. 1372-1376
Author(s):  
Gaojian Huang ◽  
Christine Petersen ◽  
Brandon J. Pitts
Keyword(s):  
Systematic Review ◽  
Autonomous Vehicles ◽  
Response Times ◽  
Autonomous Driving ◽  
Mental States ◽  
Manual Control ◽  
Monitoring Systems ◽  
Wide Range ◽  
Different Types ◽  
The Impact

Semi-autonomous vehicles still require drivers to occasionally resume manual control. However, drivers of these vehicles may have different mental states. For example, drivers may be engaged in non-driving related tasks or may exhibit mind wandering behavior. Also, monitoring monotonous driving environments can result in passive fatigue. Given the potential for different types of mental states to negatively affect takeover performance, it will be critical to highlight how mental states affect semi-autonomous takeover. A systematic review was conducted to synthesize the literature on mental states (such as distraction, fatigue, emotion) and takeover performance. This review focuses specifically on five fatigue studies. Overall, studies were too few to observe consistent findings, but some suggest that response times to takeover alerts and post-takeover performance may be affected by fatigue. Ultimately, this review may help researchers improve and develop real-time mental states monitoring systems for a wide range of application domains.

scholarly journals Three-Dimensional Stitching of Binocular Endoscopic Images Based on Feature Points

Photonics ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 330
Author(s):  
Changjiang Zhou ◽  
Hao Yu ◽  
Bo Yuan ◽  
Liqiang Wang ◽  
Qing Yang
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Field Of View ◽  
Small Field ◽  
Feature Points ◽  
Endoscopic Image ◽  
Left And Right ◽  
Conventional Algorithm ◽  
Small Field Of View

There are shortcomings of binocular endoscope three-dimensional (3D) reconstruction in the conventional algorithm, such as low accuracy, small field of view, and loss of scale information. To address these problems, aiming at the specific scenes of stomach organs, a method of 3D endoscopic image stitching based on feature points is proposed. The left and right images are acquired by moving the endoscope and converting them into point clouds by binocular matching. They are then preprocessed to compensate for the errors caused by the scene characteristics such as uneven illumination and weak texture. The camera pose changes are estimated by detecting and matching the feature points of adjacent left images. Finally, based on the calculated transformation matrix, point cloud registration is carried out by the iterative closest point (ICP) algorithm, and the 3D dense reconstruction of the whole gastric organ is realized. The results show that the root mean square error is 2.07 mm, and the endoscopic field of view is expanded by 2.20 times, increasing the observation range. Compared with the conventional methods, it does not only preserve the organ scale information but also makes the scene much denser, which is convenient for doctors to measure the target areas, such as lesions, in 3D. These improvements will help improve the accuracy and efficiency of diagnosis.

scholarly journals Divergent excitation two photon microscopy for 3D random access mesoscale imaging at single cell resolution

2019 ◽  
Author(s):  
FK Janiak ◽  
P Bartel ◽  
MR Bale ◽  
T Yoshimatsu ◽  
E Komulainen ◽  
...  
Keyword(s):  
Single Cell ◽  
Neuronal Activity ◽  
Low Cost ◽  
Three Dimensional ◽  
Random Access ◽  
Field Of View ◽  
Three Dimensions ◽  
Two Photon ◽  
Wide Range ◽  
Effective Excitation

ABSTACTIn neuroscience, diffraction limited two-photon (2P) microscopy is a cornerstone technique that permits minimally invasive optical monitoring of neuronal activity. However, most conventional 2P microscopes impose significant constraints on the size of the imaging field-of-view and the specific shape of the effective excitation volume, thus limiting the scope of biological questions that can be addressed and the information obtainable. Here, employing ‘divergent beam optics’ (DBO), we present an ultra-low-cost, easily implemented and flexible solution to address these limitations, offering a several-fold expanded three-dimensional field of view that also maintains single-cell resolution. We show that this implementation increases both the space-bandwidth product and effective excitation power, and allows for straight-forward tailoring of the point-spread-function. Moreover, rapid laser-focus control via an electrically tunable lens now allows near-simultaneous imaging of remote regions separated in three dimensions and permits the bending of imaging planes to follow natural curvatures in biological structures. Crucially, our core design is readily implemented (and reversed) within a matter of hours, and fully compatible with a wide range of existing 2P customizations, making it highly suitable as a base platform for further development. We demonstrate the application of our system for imaging neuronal activity in a variety of examples in mice, zebrafish and fruit flies.

scholarly journals ASSESSING THE ACCURACY AND PRECISION OF IMPERFECT POINT CLOUDS FOR 3D INDOOR MAPPING AND MODELING

2018 ◽  
Vol IV-4/W6 ◽  
pp. 3-10
Author(s):  
J. Chen ◽  
O. E. Mora ◽  
K. C. Clarke
Keyword(s):  
Low Cost ◽  
Random Noise ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Accuracy And Precision ◽  
Wide Range ◽  
Sharp Edges ◽  
The Cost ◽  
Indoor Spaces

<p><strong>Abstract.</strong> In recent years, growing public interest in three-dimensional technology has led to the emergence of affordable platforms that can capture 3D scenes for use in a wide range of consumer applications. These platforms are often widely available, inexpensive, and can potentially find dual use in taking measurements of indoor spaces for creating indoor maps. Their affordability, however, usually comes at the cost of reduced accuracy and precision, which becomes more apparent when these instruments are pushed to their limits to scan an entire room. The point cloud measurements they produce often exhibit systematic drift and random noise that can make performing comparisons with accurate data difficult, akin to trying to compare a fuzzy trapezoid to a perfect square with sharp edges. This paper outlines a process for assessing the accuracy and precision of these imperfect point clouds in the context of indoor mapping by integrating techniques such as the extended Gaussian image, iterative closest point registration, and histogram thresholding. A case study is provided at the end to demonstrate use of this process for evaluating the performance of the Scanse Sweep 3D, an ultra-low cost panoramic laser scanner.</p>

scholarly journals HCNET: A Point Cloud Object Detection Network Based on Height and Channel Attention

2021 ◽  
Vol 13 (24) ◽  
pp. 5071
Author(s):  
Jing Zhang ◽  
Jiajun Wang ◽  
Da Xu ◽  
Yunsong Li
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Feature Fusion ◽  
Three Dimensional ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Attention Mechanism ◽  
Uneven Distribution ◽  
Adaptive Adjustment ◽  
High Level

The use of LiDAR point clouds for accurate three-dimensional perception is crucial for realizing high-level autonomous driving systems. Upon considering the drawbacks of the current point cloud object-detection algorithms, this paper proposes HCNet, an algorithm that combines an attention mechanism with adaptive adjustment, starting from feature fusion and overcoming the sparse and uneven distribution of point clouds. Inspired by the basic idea of an attention mechanism, a feature-fusion structure HC module with height attention and channel attention, weighted in parallel, is proposed to perform feature-fusion on multiple pseudo images. The use of several weighting mechanisms enhances the ability of feature-information expression. Additionally, we designed an adaptively adjusted detection head that also overcomes the sparsity of the point cloud from the perspective of original information fusion. It reduces the interference caused by the uneven distribution of the point cloud from the perspective of adaptive adjustment. The results show that our HCNet has better accuracy than other one-stage-network or even two-stage-network RCNNs under some evaluation detection metrics. Additionally, it has a detection rate of 30FPS. Especially for hard samples, the algorithm in this paper has better detection performance than many existing algorithms.

The Advantages of Using Laser Scanners in Surveying in Protected Sites

2015 ◽  
pp. 382-402
Author(s):  
Gülhan Benli
Keyword(s):  
Protected Areas ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Laser Scanners ◽  
Point Data

Since the 2000s, terrestrial laser scanning, as one of the methods used to document historical edifices in protected areas, has taken on greater importance because it mitigates the difficulties associated with working on large areas and saves time while also making it possible to better understand all the particularities of the area. Through this technology, comprehensive point data (point clouds) about the surface of an object can be generated in a highly accurate three-dimensional manner. Furthermore, with the proper software this three-dimensional point cloud data can be transformed into three-dimensional rendering/mapping/modeling and quantitative orthophotographs. In this chapter, the study will present the results of terrestrial laser scanning and surveying which was used to obtain three-dimensional point clouds through three-dimensional survey measurements and scans of silhouettes of streets in Fatih in Historic Peninsula in Istanbul, which were then transposed into survey images and drawings. The study will also cite examples of the facade mapping using terrestrial laser scanning data in Istanbul Historic Peninsula Project.

scholarly journals SEMANTIC SEGMENTATION OF POINT CLOUDS WITH POINTNET AND KPCONV ARCHITECTURES APPLIED TO RAILWAY TUNNELS

2020 ◽  
Vol V-2-2020 ◽  
pp. 281-288
Author(s):  
M. Soilán ◽  
A. Nóvoa ◽  
A. Sánchez-Rodríguez ◽  
B. Riveiro ◽  
P. Arias
Keyword(s):  
Three Dimensional ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Research Area ◽  
Transport Infrastructure ◽  
Infrastructure Monitoring ◽  
Current State ◽  
Model Training ◽  
Multi Class Classification

Abstract. Transport infrastructure monitoring has lately attracted increasing attention due to the rise in extreme natural hazards posed by climate change. Mobile Mapping Systems gather information regarding the state of the assets, which allows for more efficient decision-making. These systems provide information in the form of three-dimensional point clouds. Point cloud analysis through deep learning has emerged as a focal research area due to its wide application in areas such as autonomous driving. This paper aims to apply the pioneering PointNet, and the current state-of-the-art KPConv architectures to perform scene segmentation of railway tunnels, in order to validate their employability over heuristic classification methods. The approach is to perform a multi-class classification that classifies the most relevant components of tunnels: ground, lining, wiring and rails. Both architectures are trained from scratch with heuristically classified point clouds of two different railway tunnels. Results show that, while both architectures are suitable for the proposed classification task, KPConv outperforms PointNet with F1-scores over 97% for ground, lining and wiring classes, and over 90% for rails. In addition, KPConv is tested using transfer learning, which gives F1-scores slightly lower than for the model training from scratch but shows better generalization capabilities.

scholarly journals GACM: A Graph Attention Capsule Model for the Registration of TLS Point Clouds in the Urban Scene

2021 ◽  
Vol 13 (22) ◽  
pp. 4497
Author(s):  
Jianjun Zou ◽  
Zhenxin Zhang ◽  
Dong Chen ◽  
Qinghua Li ◽  
Lan Sun ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Feature Descriptors ◽  
Local Point ◽  
Urban Scene ◽  
Pairwise Registration ◽  
Structure Complexity

Point cloud registration is the foundation and key step for many vital applications, such as digital city, autonomous driving, passive positioning, and navigation. The difference of spatial objects and the structure complexity of object surfaces are the main challenges for the registration problem. In this paper, we propose a graph attention capsule model (named as GACM) for the efficient registration of terrestrial laser scanning (TLS) point cloud in the urban scene, which fuses graph attention convolution and a three-dimensional (3D) capsule network to extract local point cloud features and obtain 3D feature descriptors. These descriptors can take into account the differences of spatial structure and point density in objects and make the spatial features of ground objects more prominent. During the training progress, we used both matched points and non-matched points to train the model. In the test process of the registration, the points in the neighborhood of each keypoint were sent to the trained network, in order to obtain feature descriptors and calculate the rotation and translation matrix after constructing a K-dimensional (KD) tree and random sample consensus (RANSAC) algorithm. Experiments show that the proposed method achieves more efficient registration results and higher robustness than other frontier registration methods in the pairwise registration of point clouds.

scholarly journals Point Cloud Registration Based on Multiparameter Functional

Mathematics ◽  
2021 ◽  
Vol 9 (20) ◽  
pp. 2589
Author(s):  
Artyom Makovetskii ◽  
Sergei Voronin ◽  
Vitaly Kober ◽  
Aleksei Voronin
Keyword(s):  
Dimensional Space ◽  
Three Dimensional ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Geometric Transformation ◽  
Icp Algorithm ◽  
Registration Algorithm ◽  
Closed Point ◽  
Partial Overlap ◽  
Point To Point

The registration of point clouds in a three-dimensional space is an important task in many areas of computer vision, including robotics and autonomous driving. The purpose of registration is to find a rigid geometric transformation to align two point clouds. The registration problem can be affected by noise and partiality (two point clouds only have a partial overlap). The Iterative Closed Point (ICP) algorithm is a common method for solving the registration problem. Recently, artificial neural networks have begun to be used in the registration of point clouds. The drawback of ICP and other registration algorithms is the possible convergence to a local minimum. Thus, an important characteristic of a registration algorithm is the ability to avoid local minima. In this paper, we propose an ICP-type registration algorithm (λ-ICP) that uses a multiparameter functional (λ-functional). The proposed λ-ICP algorithm generalizes the NICP algorithm (normal ICP). The application of the λ-functional requires a consistent choice of the eigenvectors of the covariance matrix of two point clouds. The paper also proposes an algorithm for choosing the directions of eigenvectors. The performance of the proposed λ-ICP algorithm is compared with that of a standard point-to-point ICP and neural network Deep Closest Points (DCP).

Decoupled Iterative Deep Sensor Fusion for 3D Semantic Segmentation

2021 ◽  
Vol 15 (03) ◽  
pp. 293-312
Author(s):  
Fabian Duerr ◽  
Hendrik Weigel ◽  
Jürgen Beyerer
Keyword(s):  
Autonomous Vehicles ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Sensor Data ◽  
Range Image ◽  
Fusion Strategy ◽  
3D Point Clouds ◽  
3D Environment ◽  
Wide Range

One of the key tasks for autonomous vehicles or robots is a robust perception of their 3D environment, which is why autonomous vehicles or robots are equipped with a wide range of different sensors. Building upon a robust sensor setup, understanding and interpreting their 3D environment is the next important step. Semantic segmentation of 3D sensor data, e.g. point clouds, provides valuable information for this task and is often seen as key enabler for 3D scene understanding. This work presents an iterative deep fusion architecture for semantic segmentation of 3D point clouds, which builds upon a range image representation of the point clouds and additionally exploits camera features to increase accuracy and robustness. In contrast to other approaches, which fuse lidar and camera features once, the proposed fusion strategy iteratively combines and refines lidar and camera features at different scales inside the network architecture. Additionally, the proposed approach can deal with camera failure as well as jointly predict lidar and camera segmentation. We demonstrate the benefits of the presented iterative deep fusion approach on two challenging datasets, outperforming all range image-based lidar and fusion approaches. An in-depth evaluation underlines the effectiveness of the proposed fusion strategy and the potential of camera features for 3D semantic segmentation.

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