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

Abstract

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.

Methodology for Identifying Optimal Pedestrian Paths in an Urban Environment: A Case Study of a School Environment in A Coruña, Spain

“…Most related studies we have reviewed consider factors such as slope (Hosseini et al, 2023) [13] to identify optimal pedestrian routes. Some, like Massin et al (2022) [14], analyze the influence of obstacles present in routes, while González-Collado et al (2024) [15] employ a combination of data acquired through an MLS and HMLS (handheld mobile laser scanner) to identify multiple elements existing along two kilometers of urban roadway. Incorporating obstacles in urban spaces seems necessary in studies of pedestrian mobility in cities.…”