Design, Calibration, and Evaluation of a Backpack Indoor Mobile Mapping System

Karam, S.; Vosselman, G.; Peter, Michael; Hosseinyalamdary, Siavash; Lehtola, Ville V.

doi:10.3390/rs11080905

Cited by 60 publications

(39 citation statements)

References 22 publications

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“…Point cloud data for the two case studies were collected with different scanners ( Figure 5). The data for the Braunschweig building were collected with an ITC Indoor Mobile Mapping System (ITC-IMMS) (epoch1) [43] and a Zeb-Revo (epoch2) [44]. For the ITC building, we used the Riegl [45] terrestrial laser scanning system and a Viametris device.…”

Section: Case Studiesmentioning

confidence: 99%

“…In our approach, categories number 1 and 2 were dealt with as important changes for 3D Cadastre, and categories 3 and 4 were just inevitable differences in two epochs that occurred because of data acquisition systems and were not relevant to the 3D Cadastre. We acquired two point clouds of two time periods with two different laser scanners, one a Zeb-Revo [44] handheld MLS and the other an ITC-IMMS [43]. The motivation to use different sensors is to explore all realistic possible causes of differences between epochs.…”

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confidence: 99%

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Towards 3D Indoor Cadastre Based on Change Detection from Point Clouds

et al. 2019

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3D Cadastre models capture both the complex interrelations between physical objects and their corresponding legal rights, restrictions, and responsibilities. Most of the ongoing research on 3D Cadastre worldwide is focused on interrelations at the level of buildings and infrastructures. So far, the analysis of such interrelations in terms of indoor spaces, considering the time aspect, has not been explored yet. In The Netherlands, there are many examples of changes in the functionality of buildings over time. Tracking these changes is challenging, especially when the geometry of the spaces changes as well; for example, a change in functionality, from administrative to residential use of the space or a change in the geometry when merging two spaces in a building without modifying the functionality. To record the changes, a common practice is to use 2D plans for subdivisions and assign new rights, restrictions, and responsibilities to the changed spaces in a building. In the meantime, with the advances of 3D data collection techniques, the benefits of 3D models in various forms are increasingly being researched. This work explores the opportunities for using 3D point clouds to establish a platform for 3D Cadastre studies in indoor environments. We investigate the changes in time of the geometry of the building that can be automatically detected from point clouds, and how they can be linked with a Land Administration Model (LADM) and included in a 3D spatial database, to update the 3D indoor Cadastre. The results we have obtained are promising. The permanent changes (e.g., walls, rooms) are automatically distinguished from dynamic changes (e.g., human, furniture) and are linked to the space subdivisions.

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Section: Case Studiesmentioning

confidence: 99%

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confidence: 99%

Towards 3D Indoor Cadastre Based on Change Detection from Point Clouds

et al. 2019

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“…In the last ten years, 3D LiDAR SLAM (Simultaneous Localization And Mapping) is an active research topic in Robotics and 3D Vision (Zhang and Singh, 2017). On the one hand, backpack and handheld mobile mapping system (MMS) which generally are equipped with laser scanners, cameras, inertial measurement units (IMU) utilizes this technology to derive the position and orientation (POSE) of the MMS for 3D mapping (Nüchter et al, 2015;Kaarta Stencil 2, 2018;Leica BLK2GO, 2019;Karam et al, 2019). On the other hand, taken the LiDAR SLAM as the core technology for localizing and mapping, flexible unmanned ground vehicles (UGVs) are developed for various applications such as autonomous driving (Claussmann et al, 2019), building information modeling (Blaser et al, 2018), etc.…”

Section: Introductionmentioning

confidence: 99%

Segment-Based Lidar Odometry for Less Structured Outdoor Scene

Chen

et al. 2020

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

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Abstract. Accurate registration of sparse sequential point clouds data frames acquired by a 3D light detection and ranging (LiDAR) sensor like VLP-16 is a prerequisite for the back-end optimization of general LiDAR SLAM algorithms to achieve a globally consistent map. This process is also called LiDAR odometry. Aiming to achieve lower drift and robust LiDAR odometry in less structured outdoor scene using a low-cost wheeled robot-borne laser scanning system, a segment-based sampling strategy for LiDAR odometry is proposed in this paper. Proposed method was tested in two typical less structured outdoor scenes and compared with other two state of the art methods. The results reveal that the proposed method achieves lower drift and significantly outperform the state of the art.

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“…The implementation of a coarse-to-fine strategy and a shortest-path initialization method eliminates the necessity of IMUs for indoor mobile mapping. As there are already some papers evaluating the performance of popular IMU-based mobile mapping systems and providing quantitative accuracy assessments [14][15][16]24], the results produced by the proposed system are compared with the point clouds captured by a TLS as reference and show acceptable accuracy in the settings of a laboratory, a lecture hall, a stairwell, and an outdoor terrace.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Plane Extraction from Low-Resolution Inhomogeneous Point Clouds and its Application to a Customized Low-Cost Mobile Mapping System

Fan¹,

Shi²,

Xiang³

et al. 2019

Remote Sensing

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Over the last decade, increasing demands for building interior mapping have brought the challenge of effectively and efficiently acquiring geometric information. Most mobile mapping methods rely on the integration of Simultaneous Localization And Mapping (SLAM) and costly Inertial Measurement Units (IMUs). Meanwhile, the methods also suffer misalignment errors caused by the low-resolution inhomogeneous point clouds captured using multi-line Mobile Laser Scanners (MLSs). While point-based alignments between such point clouds are affected by the highly dynamic moving patterns of the platform, plane-based methods are limited by the poor quality of the planes extracted, which reduce the methods’ robustness, reliability, and applicability. To alleviate these issues, we proposed and developed a method for plane extraction from low-resolution inhomogeneous point clouds. Based on the definition of virtual scanlines and the Enhanced Line Simplification (ELS) algorithm, the method extracts feature points, generates line segments, forms patches, and merges multi-direction fractions to form planes. The proposed method reduces the over-segmentation fractions caused by measurement noise and scanline curvature. A dedicated plane-to-plane point cloud alignment workflow based on the proposed plane extraction method was created to demonstrate the method’s application. The implementation of the coarse-to-fine procedure and the shortest-path initialization strategy eliminates the necessity of IMUs in mobile mapping. A mobile mapping prototype was designed to test the performance of the proposed methods. The results show that the proposed workflow and hardware system achieves centimeter-level accuracy, which suggests that it can be applied to mobile mapping and sensor fusion.

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Design, Calibration, and Evaluation of a Backpack Indoor Mobile Mapping System

Cited by 60 publications

References 22 publications

Towards 3D Indoor Cadastre Based on Change Detection from Point Clouds

Towards 3D Indoor Cadastre Based on Change Detection from Point Clouds

Segment-Based Lidar Odometry for Less Structured Outdoor Scene

A Novel Method for Plane Extraction from Low-Resolution Inhomogeneous Point Clouds and its Application to a Customized Low-Cost Mobile Mapping System

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