Detecting and Updating Changes in Lidar Point Clouds for Automatic 3D Urban Cartography

Abstract

ABSTRACT:This work presents a method that automatically detects, analyses and then updates changes in LiDAR point clouds for accurate 3D urban cartography. In the proposed method, the 3D point cloud obtained in each passage is first classified into 2 main object classes: Permanent and Temporary. The Temporary objects are then removed from the 3D point cloud to leave behind a perforated 3D point cloud of the urban scene. These perforated 3D point clouds obtained from different passages (in the same place) at different days and times are then matched together to complete the 3D urban landscape by incremental updating. Different natural or man-made changes occurring in the urban landscape over this period of time are detected and analyzed using cognitive functions of similarity and the resulting 3D cartography is progressively modified and updated accordingly. The results, evaluated on real data using different standard evaluation metrics, not only demonstrate the efficacy of the proposed method but also shows that this method is easily applicable and well scalable, making it suitable for handling large urban scenes. INTRODUCTIONIn this paper we present a new method for 3D urban cartography that automatically detects different man-made or natural changes occurring in urban environment and then effectively incorporates them in the resulting 3D point cloud of the cartography by incremental updating. Automatic detection of changes in urban environment for updating 3D urban models, cartography and maps recently become a hot topic in the scientific community as it continues to offer several challenges (Champion and Jurgen, 2009). Most of the proposed techniques detect changes in the urban environment from airborne data using DSMs (Digital Surface Models) i.e. (Vu et al., 2004). Vögtle and Steinle (Vögtle and Steinle, 2004) propose a methodology for detecting changes in urban areas following disastrous events. Instead of solely computing the difference between the laser-based DSMs, a region growing segmentation procedure is used to separate the objects and detect the buildings; only then, an object-based comparison is applied. But this method remains susceptible to misclassifications. Bouziani et al. (Bouziani et al., 2010) presented a knowledge-based change detection method for the detection of demolished and new buildings from very high resolution satellite images. Different object properties, including possible transitions and contextual relationships between object classes, were taken into account. Map data were used to determine processing parameters and to learn object properties. Unlike these methods, in our work we handle the 3D point cloud at 3D grid level for change detection, instead of object level, making it more robust. Most of the work using terrestrial laser scans focuses on deformation analysis for designated objects. Change is detected by subtraction of a re-sampled set of the data (Schäfer et al., 2004), or adjustment to surface models like planes and cylinders (Van Gosliga et al., 2006). In order to d...

Street environment change detection from mobile laser scanning point clouds

“…Finally, initial changed areas are optimized by graph cut. Aijazi et al (2013) firstly classify MLS data into permanent and temporary classes, and then construct similarity maps on the 3D voxels for multiple epoch data fusion to build a complete 3D urban map.…”

“…However, lidar (light detection and ranging) data (also referred to as laser scanning data, range data or lidar point clouds) have been proven to be an accurate data source for 3D urban reconstruction (Lafarge and Mallet, 2011;Chauve et al, 2010;Verma et al, 2006;Zhou and Neumann, 2010;Toshev et al, 2010;Banno et al, 2008;Poullis, 2013), infrastructure management and road inventory (Pu et al, 2011; Vosselman, 2012). Thus, mobile laser scanning (MLS) data is intensively studied nowadays (Weinmann et al, 2014;Demantké et al, 2011;Monnier et al, 2012;Yang and Dong, 2013;Aijazi et al, 2013;Serna and Marcotegui, 2014;Qin and Gruen, 2014).…”

Change Detection in Cadastral 3D Models and Point Clouds and Its Use for Improved Texturing

“…Especially LiDAR is useful for detecting small geometric differences, due to the geometric accuracy of LiDAR point clouds [5]. Post-processing of changes, by filtering out uninteresting changes such as pedestrians, cars and vegetation, is a further possibility [6] [7]. For cadastral data, change detection has also been performed, by using 2D image projections [8].…”

Occupancy Modelling for Moving Object Detection From Lidar Point Clouds: A Comparative Study

“…Nowadays, it is widely used for autonomous navigation and SLAM (simultaneous localization and mapping) (Wolf and Sukhatme, 2004;Moosmann and Stiller, 2013), so that a robot will know exactly where it is, even in a new environment. In geomatics, mapping is usually the primary goal, and laser scanning can provide a precise way of mapping the environment (Aijazi et al, 2013;Zhou and Vosselman, 2012). There are specific laser scanners for precise and dense mapping, for both indoor and outdoor, that can be operated from different platforms such as aircraft, mobile vehicles, and terrestrial tripods.…”