Cpp-Taskflow: Fast Task-Based Parallel Programming Using Modern C++

Huang, Tsung‐Wei; Lin, Chun-Xun; Guo, Guannan; Wong, D.F.

doi:10.1109/ipdps.2019.00105

Cited by 60 publications

(25 citation statements)

References 11 publications

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“…The OpenMP library will be used to parallelize iteration independent loops such as triangle filtering. Additionally we will explore task-based parallelization by making use of the data independence between polygons, i.e., spawning polygon extraction tasks immediately after a plane is segmented [22].…”

Section: Discussionmentioning

confidence: 99%

Polylidar - Polygons From Triangular Meshes

Castagno

Atkins

2020

IEEE Robot. Autom. Lett.

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This letter presents Polylidar, an efficient algorithm to extract non-convex polygons from 2D point sets, including interior holes. Plane segmented point clouds can be input into Polylidar to extract their polygonal counterpart, thereby reducing map size and improving visualization. The algorithm begins by triangulating the point set and filtering triangles by user configurable parameters such as triangle edge length. Next, connected triangles are extracted into triangular mesh regions representing the shape of the point set. Finally each region is converted to a polygon through a novel boundary following method which accounts for holes. Real-world and synthetic benchmarks are presented to comparatively evaluate Polylidar speed and accuracy. Results show comparable accuracy and more than four times speedup compared to other concave polygon extraction methods. Index Terms-Aerial systems: perception and autonomy, reactive and sensor-based planning, computational geometry. I. INTRODUCTION V IDEO and LiDAR data are widely used in robotics to provide rich information about the environment. LiDAR and RGBD cameras generate point clouds for localization and mapping [1], 3D modelling [2], and scene classification for autonomous navigation [3]. Flat surfaces such as walls and floors are key environmental elements to identify; they are often extracted using planar segmentation techniques [4]-[6]. However points clouds are dense incurring a high computational cost when used directly. A common simplifying approach transforms point clouds into lower dimensional representations such as lines and planes [7]. Furthermore, polygonal representations of planes reduces map size and may accelerate matching for localization [8]. Convex polygon representations of planar segments were proposed by [7]. Convex polygons are simple and efficient to generate but ignore boundary concavities and overestimate area of the enclosed point set. Non-convex polygon representations may be generated using techniques such as boundary following outlined in [8] or α-shapes as proposed in [9]. However few methods also capture the interior holes within non-convex polygons. Safe robot navigation demands accurate capture of non-convex polygons with interior holes in real-time, requiring both speed and robustness.

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Section: Discussionmentioning

confidence: 99%

Polylidar - Polygons From Triangular Meshes

Castagno

Atkins

2020

IEEE Robot. Autom. Lett.

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“…Region growing is decomposed using task-based parallelism, where l dominant plane normals create l separate tasks of regions growing. These tasks are executed in parallel by a threadpool and can themselves spawn additional dynamic tasks [51]. Each independent task performs a serial region growing procedure that is similar to our previous work on 2D meshes [14] and was inspired by [10].…”

Section: Planar Segmentationmentioning

confidence: 99%

Polylidar3D-Fast Polygon Extraction from 3D Data

Castagno

Atkins

2020

Sensors

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Flat surfaces captured by 3D point clouds are often used for localization, mapping, and modeling. Dense point cloud processing has high computation and memory costs making low-dimensional representations of flat surfaces such as polygons desirable. We present Polylidar3D, a non-convex polygon extraction algorithm which takes as input unorganized 3D point clouds (e.g., LiDAR data), organized point clouds (e.g., range images), or user-provided meshes. Non-convex polygons represent flat surfaces in an environment with interior cutouts representing obstacles or holes. The Polylidar3D front-end transforms input data into a half-edge triangular mesh. This representation provides a common level of abstraction for subsequent back-end processing. The Polylidar3D back-end is composed of four core algorithms: mesh smoothing, dominant plane normal estimation, planar segment extraction, and finally polygon extraction. Polylidar3D is shown to be quite fast, making use of CPU multi-threading and GPU acceleration when available. We demonstrate Polylidar3D’s versatility and speed with real-world datasets including aerial LiDAR point clouds for rooftop mapping, autonomous driving LiDAR point clouds for road surface detection, and RGBD cameras for indoor floor/wall detection. We also evaluate Polylidar3D on a challenging planar segmentation benchmark dataset. Results consistently show excellent speed and accuracy.

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“…People use it to acquire timing report of their design, and therefore we have been dealing with many surrounding tasks to improve the usability. Cpp-Taskflow is a parallel programming library we developed to help C++ developers quickly write parallel task programs [5,6]. This project targets at developers.…”

Section: Understand Your Usersmentioning

confidence: 99%

Essential Building Blocks for Creating an Open-source EDA Project

Huang¹,

Lin²,

Guo³

et al. 2019

Proceedings of the 56th Annual Design Automation Conference 2019

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Open source has started energizing both industrial and academic research and development in electronic design automation (EDA) systems. By moving to open source, we can speed up our effort and work with others who are working toward the same goals, while reducing costs and improving end products. However, building an open-source project is much more than placing the codebase on the web. In this paper, we will talk about essential building blocks to create an impactful open-source project, including source repository, project landing page, documentation, and continuous integration. We will also cover the use of web-based frameworks to design a showcase project to bring community's attention. We will then share our experience in developing an open-source timing analyzer (OpenTimer) and a parallel task programming library (Cpp-Taskflow), both of which are being used in many industrial and academic EDA research projects.

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Cpp-Taskflow: Fast Task-Based Parallel Programming Using Modern C++

Cited by 60 publications

References 11 publications

Polylidar - Polygons From Triangular Meshes

Polylidar - Polygons From Triangular Meshes

Polylidar3D-Fast Polygon Extraction from 3D Data

Essential Building Blocks for Creating an Open-source EDA Project

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