The Mesh Tools Package – Introducing Annotated 3D Triangle Maps in ROS

Pütz, Sebastian; Wiemann, Thomas; Hertzberg, Joachim

doi:10.1016/j.robot.2020.103688

Cited by 10 publications

(3 citation statements)

References 20 publications

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“…For each device in this model (smart device, user device, or server) we assigned it with a universally unique ID (UUID) with 128-bit length compliant with RFC 4122 [22,23]. This UUID is generated base on SHA-1 of a namespace (ISO object ID) and SHA-1 of network MAC address.…”

Section: Installation Phasementioning

confidence: 99%

GDPR Compliance IoT Authentication Model for Smart Home Environment

Merzeh¹,

Kara²,

Aydın³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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The Internet of things (IoT) became quickly one of the most popular and most discussed topics in research. Studies paid attention to the Internet stuff, primarily to new products that aim to achieve greater efficiency and simplicity in life. IoT may cover several fields of the smart environment. Because of the data exposure that occurs when data is transferred via various channels, data protection issues have become a major problem as the company continues to expand. When user privacy and property are taken into consideration, the situation may become much worse. As a result, the authentication process for communicating entities has garnered considerable attention. In this paper, we proposed a secure authentication model for smart home applications, which privacy considered and complies with the General Data Protection Regulation GDPR. The proposed scheme improved the existing authentication schemes' performance and security level. This work based on the Elliptic curve cryptography ECC, one-way hash function, and XOR operation. The proposed lightweight authentication model is suitable for resource-constrained devices. This study is developing the offline direct authentication model to authenticate users and IoT devices in the local network. In addition, our scheme uses the online authentication server to authenticate all system parts.

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Section: Installation Phasementioning

confidence: 99%

GDPR Compliance IoT Authentication Model for Smart Home Environment

Merzeh¹,

Kara²,

Aydın³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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show abstract

“…Mesh can also depict manifold structures compared to surfel and NDT maps. In addition, mesh map is memory efficient, scalable, and retains the topological information [12] [13].…”

Section: Introductionmentioning

confidence: 99%

SLAMesh: Real-time LiDAR Simultaneous Localization and Meshing

Ruan¹,

Li²,

Wang³

et al. 2023

Preprint

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Most current LiDAR simultaneous localization and mapping (SLAM) systems build maps in point clouds, which are sparse when zoomed in, even though they seem dense to human eyes. Dense maps are essential for robotic applications, such as map-based navigation. Due to the low memory cost, mesh has become an attractive dense model for mapping in recent years. However, existing methods usually produce mesh maps by using an offline post-processing step to generate mesh maps. This two-step pipeline does not allow these methods to use the built mesh maps online and to enable localization and meshing to benefit each other. To solve this problem, we propose the first CPU-only real-time LiDAR SLAM system that can simultaneously build a mesh map and perform localization against the mesh map. A novel and direct meshing strategy with Gaussian process reconstruction realizes the fast building, registration, and updating of mesh maps. We perform experiments on several public datasets. The results show that our SLAM system can run at around 40Hz. The localization and meshing accuracy also outperforms the state-of-the-art methods, including the TSDF map and Poisson reconstruction. Our code and video demos are available at: https://github.com/lab-sun/SLAMesh.

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“…Mesh clustering is widely used for other applications as well, such as reducing the mesh transmission time [12], simplification [13], segmentation [14], etc. [15][16][17]. Geometric primitives could be reordered [3,18], structured [19], or processed in the GPU [3,20] to improve efficiency of visibility culling.…”

Section: Introductionmentioning

confidence: 99%

Mesh Clustering and Reordering Based on Normal Locality for Efficient Rendering

Kim

Lee

2022

Symmetry

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Recently, the size of models for real-time rendering has been significantly increasing for realism, and many graphics applications are being developed in mobile devices with relatively insufficient hardware power. Therefore, improving rendering speed is still important in graphics. Back-face culling is one of the core speed-up techniques to remove the back-facing polygons that are not drawn in the result image. In this paper, we present a mesh clustering and reordering method based on normal coherence for efficient back-face culling at an earlier stage than the current method, which removes back faces after the vertex shader on the GPU. In the pre-computation, our method first vertically clusters the mesh into multiple stripes based on the latitude of the face normal vector and sorts each stripe in ascending order of longitude. At runtime, our method computes a potentially visible set of faces at the current camera view by excluding back faces from the clustered and reordered faces list, and draws only the potentially visible set. Experiments have shown that the rendering using our method is more efficient than traditional methods, especially for large and static models.

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The Mesh Tools Package – Introducing Annotated 3D Triangle Maps in ROS

Cited by 10 publications

References 20 publications

GDPR Compliance IoT Authentication Model for Smart Home Environment

GDPR Compliance IoT Authentication Model for Smart Home Environment

SLAMesh: Real-time LiDAR Simultaneous Localization and Meshing

Mesh Clustering and Reordering Based on Normal Locality for Efficient Rendering

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