Morphological Operations to Extract Urban Curbs in 3D MLS Point Clouds

Rodríguez-Cuenca, Borja; García-Cortés, Silverio; Ordóñez, Celestino; Alonso, María C.

doi:10.3390/ijgi5060093

Cited by 25 publications

(22 citation statements)

References 26 publications

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“…The combination of such data with ALS data sets allows automated 3D modeling and keeping maps updated. Algorithms have been developed for extracting road markings [57], curbs [80], road edges [81], and pole-like objects [82] from MLS data. For dense MLS data sets, methods for automatic identification and segmentation of various urban furniture have been developed [83,84].…”

Section: Built and Road Environmentsmentioning

confidence: 99%

Nationwide Point Cloud—The Future Topographic Core Data

Virtanen

Kukko

Kaartinen

et al. 2017

IJGI

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Topographic databases maintained by national mapping agencies are currently the most common nationwide data sets in geo-information. The application of laser scanning as source data for surveying is increasing. Along with this development, several analysis methods that utilize dense point clouds have been introduced. We present the concept of producing a dense nationwide point cloud, produced from multiple sensors and containing multispectral information, as the national core data for geo-information. Geo-information products, such as digital terrain and elevation models and 3D building models, are produced automatically from these data. We outline the data acquisition, processing, and application of the point cloud. As a national data set, a dense multispectral point cloud could produce significant cost savings via improved automation in mapping and a reduction of overlapping surveying efforts.

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Section: Built and Road Environmentsmentioning

confidence: 99%

Nationwide Point Cloud—The Future Topographic Core Data

Virtanen

Kukko

Kaartinen

et al. 2017

IJGI

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“…Table 5 lists the evaluation metrics computed by the five existing methods and our approach using the 20 publicly available data sets without and with furniture. = total number of pixels matched total number of pixels detected automatically (2) = total number of pixels matched total number of pixels detected manually Finally, quantitative evaluations are carried out using three different measures: correctness, completeness, and absolute deviation [40,43]. The correctness and completeness measures were calculated pixel-by-pixel in comparing the automatically segmented map with the ground truths.…”

Section: Comparison With Existing Methods Using Publicly Available Damentioning

confidence: 99%

“…This was attributable to the inability of the detection window in the initial segmentation phase to pass through the areas where there were lots of occlusions, thus resulting in over-production of the initial segments. Finally, quantitative evaluations are carried out using three different measures: correctness, completeness, and absolute deviation [40,43]. The correctness and completeness measures were calculated pixel-by-pixel in comparing the automatically segmented map with the ground truths.…”

Section: Comparison With Existing Methods Using Publicly Available Damentioning

confidence: 99%

Automatic Room Segmentation of 3D Laser Data Using Morphological Processing

Jung

Stachniss

Kim

2017

IJGI

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Abstract:In this paper, we introduce an automatic room segmentation approach based on morphological processing. The inputs are registered point-clouds obtained from either a static laser scanner or a mobile scanning system, without any required prior information or initial labeling satisfying specific conditions. The proposed segmentation method's main concept, based on the assumption that each room is bound by vertical walls, is to project the 3D point cloud onto a 2D binary map and to close all openings (e.g., doorways) to other rooms. This is achieved by creating an initial segment map, skeletonizing the surrounding walls of each segment, and iteratively connecting the closest pixels between the skeletonized walls. By iterating this procedure for all initial segments, the algorithm produces a "watertight" floor map, on which each room can be segmented by a labeling process. Finally, the original 3D points are segmented according to their 2D locations as projected on the segment map. The novel features of our approach are: (1) its robustness against occlusions and clutter in point-cloud input; (2) high segmentation performance regardless of the number of rooms or architectural complexity; and (3) straight segmentation boundary generation, all of which were proved in experiments with various sets of real-world, synthetic, and publicly available data. Additionally, comparisons with the five popular existing methods through both qualitative and quantitative evaluations demonstrated the feasibility of the proposed approach.

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“…Recent works of (Serna and Marcotegui, 2013) and (Rodríguez-Cuenca, 2016), based on mobile mapping laser point cloud, show the potential of fully automated point cloud processing for extracting urban curbs. (Balado et.al, 2017) proposed an approach to automatically detect structural elements of the buildings such as steps or ramps.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Smartphone Based Photogrammetric Point Clouds for the Generation of Accessibility Maps

Angelats

Parés

Kumar

2018

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

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ABSTRACT:Accessible cities with accessible services are an old claim of people with reduced mobility. But this demand is still far away of becoming a reality as lot of work is required to be done yet. First step towards accessible cities is to know about real situation of the cities and its pavement infrastructure. Detailed maps or databases on street slopes, access to sidewalks, mobility in public parks and gardens, etc. are required. In this paper, we propose to use smartphone based photogrammetric point clouds, as a starting point to create accessible maps or databases. This paper analyses the performance of these point clouds and the complexity of the image acquisition procedure required to obtain them. The paper proves, through two test cases, that smartphone technology is an economical and feasible solution to get the required information, which is quite often seek by city planners to generate accessible maps. The proposed approach paves the way to generate, in a near term, accessibility maps through the use of point clouds derived from crowdsourced smartphone imagery.

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Morphological Operations to Extract Urban Curbs in 3D MLS Point Clouds

Cited by 25 publications

References 26 publications

Nationwide Point Cloud—The Future Topographic Core Data

Nationwide Point Cloud—The Future Topographic Core Data

Automatic Room Segmentation of 3D Laser Data Using Morphological Processing

Feasibility of Smartphone Based Photogrammetric Point Clouds for the Generation of Accessibility Maps

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