Change detection in urban areas by object-based analysis and on-the-fly comparison of multi-view ALS data

Hebel, Marcus; Arens, Michael; Stilla, Uwe

doi:10.1016/j.isprsjprs.2013.09.005

Cited by 80 publications

(57 citation statements)

References 20 publications

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“…The occupancy of the scanned space could also be done by using the Dempster-Shafer theory of evidence (Shafer, 1976). In this case the spatial occupancy is represented by so-called belief masses describing the state of the space (e.g., 'empty' or 'occupied') in the nearby of a point p. However, definition of proper belief masses relies, in general, on a series of parameters (Hebel et al, 2013), which may be difficult to set up. In order to cope with this limitation a simpler approach based on a few and intuitive parameters was designed.…”

Section: Occlusion Labellingmentioning

confidence: 99%

Towards automatic indoor reconstruction of cluttered building rooms from point clouds

Previtali

Barazzetti

Brumana

et al. 2014

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

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ABSTRACT:Terrestrial laser scanning is increasingly used in architecture and building engineering for as-built modelling of large and medium size civil structures. However, raw point clouds derived from laser scanning survey are generally not directly ready for generation of such models. A manual modelling phase has to be undertaken to edit and complete 3D models, which may cover indoor or outdoor environments. This paper presents an automated procedure to turn raw point clouds into semantically-enriched models of building interiors. The developed method mainly copes with a geometric complexity typical of indoor scenes with prevalence of planar surfaces, such as walls, floors and ceilings. A characteristic aspect of indoor modelling is the large amount of clutter and occlusion that may characterize any point clouds. For this reason the developed reconstruction pipeline was designed to recover and complete missing parts in a plausible way. The accuracy of the presented method was evaluated against traditional manual modelling and showed comparable results.

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Section: Occlusion Labellingmentioning

confidence: 99%

Towards automatic indoor reconstruction of cluttered building rooms from point clouds

Previtali

Barazzetti

Brumana

et al. 2014

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

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“…In Hebel et al (2013), an approach based on the analysis of evidential conflicts between free and occupied 3D-space is used to detect changes between two airborne LiDAR scans. Xiao et al (2015) use a similar approach to detect moving objects in mobile LiDAR measurements based on conflict search between a reference and a target epoch.…”

Section: Detection Of Moving Objectsmentioning

confidence: 99%

An Approach to Extract Moving Objects From MLS Data Using a Volumetric Background Representation

Gehrung

Hebel

Arens

et al. 2017

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

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ABSTRACT:Data recorded by mobile LiDAR systems (MLS) can be used for the generation and refinement of city models or for the automatic detection of long-term changes in the public road space. Since for this task only static structures are of interest, all mobile objects need to be removed. This work presents a straightforward but powerful approach to remove the subclass of moving objects. A probabilistic volumetric representation is utilized to separate MLS measurements recorded by a Velodyne HDL-64E into mobile objects and static background. The method was subjected to a quantitative and a qualitative examination using multiple datasets recorded by a mobile mapping platform. The results show that depending on the chosen octree resolution 87-95 % of the measurements are labeled correctly.

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“…The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-1/W1, 2017 ISPRS Hannover Workshop: HRIGI 17 -CMRT 17 -ISA 17 -EuroCOW 17, 6-9 June 2017, Hannover, Germany (Hebel et al, 2013) present a framework to compare current aerial laser scan data of an urban environment to a reference on-the-fly. After they align the actual measurement with the reference, the resulting point cloud is analyzed pointwise using Bresenham's algorithm (Bresenham, 1965) for ray tracing.…”

Section: Introductionmentioning

confidence: 99%

“…They model the laser beam as a cone in order to improve the ray tracing results. In contrast to (Hebel et al, 2013) they do not use pre-classification, but avoid conflicts with penetrable objects by combining their method with a point-to-triangle distance based change detection.…”

Section: Introductionmentioning

confidence: 99%

Assessing Temporal Behavior in Lidar Point Clouds of Urban Environments

Schachtschneider

Schlichting

Brenner

2017

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

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ABSTRACT:Self-driving cars and robots that run autonomously over long periods of time need high-precision and up-to-date models of the changing environment. The main challenge for creating long term maps of dynamic environments is to identify changes and adapt the map continuously. Changes can occur abruptly, gradually, or even periodically. In this work, we investigate how dense mapping data of several epochs can be used to identify the temporal behavior of the environment. This approach anticipates possible future scenarios where a large fleet of vehicles is equipped with sensors which continuously capture the environment. This data is then being sent to a cloud based infrastructure, which aligns all datasets geometrically and subsequently runs scene analysis on it, among these being the analysis for temporal changes of the environment. Our experiments are based on a LiDAR mobile mapping dataset which consists of 150 scan strips (a total of about 1 billion points), which were obtained in multiple epochs. Parts of the scene are covered by up to 28 scan strips. The time difference between the first and last epoch is about one year. In order to process the data, the scan strips are aligned using an overall bundle adjustment, which estimates the surface (about one billion surface element unknowns) as well as 270,000 unknowns for the adjustment of the exterior orientation parameters. After this, the surface misalignment is usually below one centimeter. In the next step, we perform a segmentation of the point clouds using a region growing algorithm. The segmented objects and the aligned data are then used to compute an occupancy grid which is filled by tracing each individual LiDAR ray from the scan head to every point of a segment. As a result, we can assess the behavior of each segment in the scene and remove voxels from temporal objects from the global occupancy grid.

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Change detection in urban areas by object-based analysis and on-the-fly comparison of multi-view ALS data

Cited by 80 publications

References 20 publications

Towards automatic indoor reconstruction of cluttered building rooms from point clouds

Towards automatic indoor reconstruction of cluttered building rooms from point clouds

An Approach to Extract Moving Objects From MLS Data Using a Volumetric Background Representation

Assessing Temporal Behavior in Lidar Point Clouds of Urban Environments

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