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UAS-Based Mapping: Laser Scanning

Learn the fundamentals of UAS-based mapping from laser scanning systems with emphasis on point positioning, system calibration, and quality control of derived point clouds/products.

UAS-Based Mapping: Laser Scanning

Hay una sesión disponible:

After a course session ends, it will be archivado.
Comienza el Mar 21, 2022
Termina el May 1, 2022
6 semanas estimadas
4–6 horas por semana
Al ritmo del instructor
Dictado por un instructor según un cronograma
Gratis
Cambio opcional de categoría disponible

Sobre este curso

Omitir Sobre este curso

The continuous developments in direct geo-referencing (i.e., integrated Global Navigation Satellite Systems – GNSS – and Inertial Navigation Systems – INS) and remote sensing (i.e., passive and active imaging sensors in the visible and infrared range – RGB cameras and laser scanning) is providing the professional geospatial community with ever-growing opportunities to provide accurate 3D information with rich set of attributes. These advances are also coupled with improvement in the sensors’ performance, reduction in the associated cost, and miniaturization of such sensors. Aside from the sensing systems, we are also enjoying the emerging of promising platforms such as Unmanned Airborne Vehicles (UAVs). UAVs equipped with consumer-grade imaging/ranging and direct geo-referencing systems have been proven as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. The advantages of UAV-based mapping can be attributed to the following facts: a) they can fly at lower elevation and slower speed than manned aircraft, thus providing high quality spatial data; b) they can be cost-effectively stored and deployed, which make them optimal for rapid response applications; c) they are easy to use with minimal training requirements; d) they can provide repetitive mapping at higher frequency with minimal cost; and e) they are less affected by weather conditions (e.g., they can fly under cloud cover). In spite of the proven potential of UAVs, the nature of their use in mapping applications can be describes as ad-hoc at best. This course is focusing on the principles and mathematical details of laser scanning-based 3D reconstruction using ranging sensors onboard UAVs.

De un vistazo

  • Institución: PurdueX
  • Tema:Ingeniería
  • Nivel:Intermediate
  • Prerrequisitos:

    Learners interested in taking this course should have successfully completed the edX course, UAS-Based Mapping: Basic Principles or equivalent.

    Students should also have knowledge in geometry, trigonometry, linear algebra, differential calculus, and have the ability to develop MATLAB scripts or gain the ability during the course with help provided.

  • Idioma:English
  • Transcripción de video:English

Lo que aprenderás

Omitir Lo que aprenderás
  • Explain the principles of LiDAR-based mapping.
  • Identify/select systems/sensors/platforms and design flight plan to meet the needs of a specific application.
  • Conduct a UAS-based mapping project using a LiDAR system.
  • Evaluate the quality of UAS-based ranging products.

Plan de estudios

Omitir Plan de estudios
  • Point Positioning Equations
  • LiDAR System Calibration
  • Quality Control of LiDAR Data
  • LiDAR Data Characterization
  • LiDAR Data Processing
  • LiDAR/Image Integration
  • Applications

Acerca de los instructores

¿Quién puede hacer este curso?

Lamentablemente, las personas residentes en uno o más de los siguientes países o regiones no podrán registrarse para este curso: Irán, Cuba y la región de Crimea en Ucrania. Si bien edX consiguió licencias de la Oficina de Control de Activos Extranjeros de los EE. UU. (U.S. Office of Foreign Assets Control, OFAC) para ofrecer nuestros cursos a personas en estos países y regiones, las licencias que hemos recibido no son lo suficientemente amplias como para permitirnos dictar este curso en todas las ubicaciones. edX lamenta profundamente que las sanciones estadounidenses impidan que ofrezcamos todos nuestros cursos a cualquier persona, sin importar dónde viva.

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