Combining Airborne Oblique Camera and Lidar Sensors: Investigation and New Perspectives

Toschi, I.; Remondino, Fabio; Rothe, Rasmus; Klimek, K.

doi:10.5194/isprs-archives-xlii-1-437-2018

Cited by 23 publications

(17 citation statements)

References 20 publications

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“…However, the following obstacles make the task difficult: (1) Due to differences in the descriptions of the same structures caused by voids and noise, the form and accuracy of the local neighborhood features extracted from the cross-source point clouds are inconsistent [10]. (2) Due to the differences in coverage caused by perspective, the number of strictly corresponding features is difficult to guarantee [11]. (3) Massive point-cloud data have significant redundancy and a high computing cost, which requires efficient processing.…”

Section: Introductionmentioning

confidence: 99%

Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

et al. 2021

Remote Sensing

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Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

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Section: Introductionmentioning

confidence: 99%

Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

et al. 2021

Remote Sensing

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“…Photogrammetric based approaches are applied to different stages of urban modelling, as for instance the automatic building rooftop extraction for building reconstruction or solar energy supply (13); the generation of level of detail (LOD2) building models (12), the creation of digital city models based on aerial photogrammetry for the generation of 3D hydrodynamic models focused on flood simulations (14), or even, the estimation of building density using machine learning approaches over high-resolution remote sensing images (15). It is worth to highlight, the quickly growing sector of oblique airborne cameras (16,17), with a potential for building façades reconstruction and accurate building footprint extraction (18,19).…”

Section: Introductionmentioning

confidence: 99%

Reconstrucción de edificios y análisis urbanístico de centros históricos con fotogrametría aérea

et al. 2021

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Los centros urbanos históricos son escenarios complejos para su reconstrucción tridimensional. Los avances en la reconstrucción automática son de gran utilidad para realizar análisis urbanísticos que de otra manera requerirían un elevado esfuerzo humano. En este artículo, se derivarán de forma automática parámetros urbanísticos para el análisis de los centros históricos. En particular, se utiliza un vuelo fotogramétrico como base para la obtención de modelos 3D de edificios con propiedades métricas. Los resultados revelan que la información geométrica de los edificios (alturas, áreas y volúmenes) y los atributos de densidad urbana (intensidad de ocupación del suelo en 2D y 3D) juegan un papel esencial en el diseño, planificación y gestión de los centros históricos. El enfoque propuesto fue validado en el centro histórico de la ciudad de Trento (Italia) utilizando datos catastrales y un sistema de cartografiado móvil como referencia geométrica.

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“…Given the nadir and optionally oblique viewing directions from airborne platforms makes them ideally suited for area-wide capturing for most of the topography, including natural as well as artificial objects upon the terrain. Due to the complimentary strengths of lidar and photos, more and more sensor combinations and integrated sensors, comprising a laser scanners and cameras became available (Toschi et al, 2018;Mandlburger et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Orientation of Airborne Lidar Point Clouds and Aerial Images

Glira

Pfeifer

Mandlburger

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Airborne LiDAR (Light Detection And Ranging) and airborne photogrammetry are both proven and widely used techniques for the 3D topographic mapping of extended areas. Although both techniques are based on different reconstruction principles (polar measurement vs. ray triangulation), they ultimately serve the same purpose, the 3D reconstruction of the Earth’s surface, natural objects or infrastructure. It is therefore obvious for many applications to integrate the data from both techniques to generate more accurate and complete results. Many works have been published on this topic of data fusion. However, no rigorous integrated solution exists for the first two steps that need to be carried out after data acquisition, namely (a) the lidar strip adjustment and (b) the aerial triangulation. A consequence of solving these two optimization problems independently can be large discrepancies (of up to several decimeters) between the lidar block and the image block. This is especially the case in challenging situations, e.g. corridor mapping with one strip only or in case few or no ground control data. To avoid this problem and thereby profit from many other advantages, a first rigorous integration of these two tasks, the hybrid orientation of lidar point clouds and aerial images, is presented in this work.</p>

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Combining Airborne Oblique Camera and Lidar Sensors: Investigation and New Perspectives

Cited by 23 publications

References 20 publications

Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

Reconstrucción de edificios y análisis urbanístico de centros históricos con fotogrametría aérea

Hybrid Orientation of Airborne Lidar Point Clouds and Aerial Images

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