Visibility analysis for the occlusion detection and characterisation in street point clouds acquired with Mobile Laser Scanning

Barros-Ribademar, Jose; Balado, Jesús; Arias, Pablo; González-Collazo, Silvia María

doi:10.1080/10106049.2022.2032392

Cited by 5 publications

(2 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These reconstruction methods based on deep learning have achieved many encouraging results. However, due to the high computational cost of deep learning methods, most of the previous studies focused on the reconstruction of vehicles, building facades, and other small objects [19], but the reconstruction of large-scale objects, like road surface, is not much involved.…”

Section: Occlusion Reconstructionmentioning

confidence: 99%

Detection and reconstruction of static vehicle-related ground occlusions in point clouds from mobile laser scanning

Liu

Oosterom

Balado

et al. 2022

Automation in Construction

Self Cite

View full text Add to dashboard Cite

Section: Occlusion Reconstructionmentioning

confidence: 99%

Detection and reconstruction of static vehicle-related ground occlusions in point clouds from mobile laser scanning

Liu

Oosterom

Balado

et al. 2022

Automation in Construction

Self Cite

View full text Add to dashboard Cite

“…Point clouds are unstructured information, with noise, occlusions and strong point density variations, which must be processed in order to extract useful information (Yu et al, 2015). MLS data present occlusions in sidewalks, occurred by parked vehicles (Balado Frías et al, 2020;Barros-Ribademar et al, 2022;Z. Liu et al, 2022), which can present problems in order to obtain the navigable pedestrian space.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Urban Pathfinding for Pedestrians Through Fusion of MLS and HMLS Data

González-Collazo,

Balado,

Túñez-Alcalde

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Pedestrian pathfinding is crucial for enhancing pedestrian mobility in urban environments. In this research a method to generate navigation graphs based on Mobile Laser Scanning (MLS) and Handheld Laser Scanning (HMLS) data fusion is developed. The input data comprises a 2-kilometer urban street network that integrates both MLS and HMLS data, effectively mitigating sidewalk occlusions caused mainly by parked vehicles. The proposed method encompasses the following steps: (1) Deep Learning semantic segmentation of urban ground elements and navigation-related obstacles, (2) Static vs. Dynamic object differentiation to replicate conditions of unobstructed passage. (3) Sidewalk enrichment with inclination, preservation status, and width. (4) Physical accessibility estimation between sidewalks and crosswalks incorporating curb information. And (5) navigation graph computation based on the enhanced sidewalk data, crosswalks, with accurate node location and connections. Routes were calculated within a Geographical Information System, and results ensure that pedestrians can navigate in urban environments with precision and efficiency.

show abstract