IMU-Aided Registration of MLS Point Clouds Using Inertial Trajectory Error Model and Least Squares Optimization

Chen, Zhipeng; Li, Qingquan; Li, Jiayuan; Zhang, Dejin; Yu, Jingfei; Yin, Yu; Lv, Shiwang; Liang, Anbang

doi:10.3390/rs14061365

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…With such a taxonomy, researchers can choose the most suitable algorithm for a given data fusion task. As we focus on the overall mathematics of a data fusion pipeline, individual techniques embedded in an algorithmic pipeline (such as Kalman Filters [18] and its variants, such as recursive filtering [19], particle filtering [20], inertial measurement unit [21] etc.) are not included in this discussion.…”

Section: Metrology and Data Fusionmentioning

confidence: 99%

Applications of data fusion in optical coordinate metrology: a review

Zhang

Catalucci

Thompson

et al. 2022

Int J Adv Manuf Technol

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Data fusion enables the characterisation of an object using multiple datasets collected by various sensors. To improve optical coordinate measurement using data fusion, researchers have proposed numerous algorithmic solutions and methods. The most popular examples are the Gaussian process (GP) and weighted least-squares (WLS) algorithms, which depend on user-defined mathematical models describing the geometric characteristics of the measured object. Existing research on GP and WLS algorithms indicates that GP algorithms have been widely applied in both academia and industry, despite their use being limited to applications on relatively simple geometries. Research on WLS algorithms is less common than research on GP algorithms, as the mathematical tools used in the WLS cases are too simple to be applied with complex geometries. Machine learning is a new technology that is increasingly being applied to data fusion applications. Research on this technology is relatively scarce, but recent work has highlighted the potential of machine learning methods with significant results. Unlike GP and WLS algorithms, machine learning algorithms can autonomously learn the geometrical features of an object. To understand existing research in-depth and explore a path for future work, a new taxonomy of data fusion algorithms is proposed, covering the mathematical background and existing research surrounding each algorithm type. To conclude, the advantages and limitations of the existing methods are reviewed, highlighting the issues related to data quality and the types of test artefacts.

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Section: Metrology and Data Fusionmentioning

confidence: 99%

Applications of data fusion in optical coordinate metrology: a review

Zhang

Catalucci

Thompson

et al. 2022

Int J Adv Manuf Technol

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show abstract

“…Each point from the dataset contains coordinates X, Y, and Z, intensity I, and color shades R, G, and B [12,13]. Mobile laser scanning (MLS), placed on mobile platforms, such as a drone, car, boat, or backpack, is a current trend and consists of a scanner, an inertial system (gyroscopes and odometer), and two GNSS receivers [14]. A handheld scanner efficiently and quickly scans complex objects in 3D, enabling the creation of BIM (Building Information Model) of the existing buildings [15,16].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Determination of Corresponding Points from Available Providers of Spatial Data—A Case Study from Slovakia

Labant,

Petovsky,

Sustek

et al. 2024

Land

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Mapping the terrain and the Earth’s surface can be performed through non-contact methoYes, that is correct.ds such as laser scanning. This is one of the most dynamic and effective data collection methods. This case study aims to analyze the usability of spatial data from available sources and to choose the appropriate solutions and procedures for processing the point cloud of the area of interest obtained from available web applications. The processing of the point cloud obtained by airborne laser scanning results in digital terrain models created in selected software. The study also included modeling of different types of residential development, and the results were evaluated. Different data sources may have compatibility issues, which means that the position of the same object from different spatial data databases may not be identical. To address this, deviations of the corresponding points were determined from various data sources such as Real Estate Cadaster, ZBGIS Buildings, LiDAR point cloud, orthophoto mosaic, and geodetic measurements. These deviations were analyzed according to their size and orientation, with the average deviations ranging from 0.22 to 0.34 m and standard deviations from 0.11 to 0.20 m. The Real Estate Cadaster was used as the correct basis for comparison. The area of the building was also compared, with the slightest difference being present between the Real Estate Cadaster and geodetic measurement. The difference was zero after rounding the area to whole numbers. The maximum area difference was +5 m2 for ZBGIS Buildings.

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3D Point Cloud Generation Based on Multi-Sensor Fusion

Han

Sun

et al. 2022

Applied Sciences

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Traditional precise engineering surveys adopt manual static, discrete observation, which cannot meet the dynamic, continuous, high-precision and holographic fine measurements required for large-scale infrastructure construction, operation and maintenance, where mobile laser scanning technology is becoming popular. However, in environments without GNSS signals, it is difficult to use mobile laser scanning technology to obtain 3D data. We fused a scanner with an inertial navigation system, odometer and inclinometer to establish and track mobile laser measurement systems. The control point constraints and Rauch-Tung-Striebel filter smoothing were fused, and a 3D point cloud generation method based on multi-sensor fusion was proposed. We verified the method based on the experimental data; the average deviation of positioning errors in the horizontal and elevation directions were 0.04 m and 0.037 m, respectively. Compared with the stop-and-go mode of the Amberg GRP series trolley, this method greatly improved scanning efficiency; compared with the method of generating a point cloud in an absolute coordinate system based on tunnel design data conversion, this method improved data accuracy. It effectively avoided the deformation of the tunnel, the sharp increase of errors and more accurately and quickly processed the tunnel point cloud data. This method provided better data support for subsequent tunnel analysis such as 3D display, as-built surveying and disease system management of rail transit tunnels.

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IMU-Aided Registration of MLS Point Clouds Using Inertial Trajectory Error Model and Least Squares Optimization

Cited by 4 publications

References 39 publications

Applications of data fusion in optical coordinate metrology: a review

Applications of data fusion in optical coordinate metrology: a review

Accuracy of Determination of Corresponding Points from Available Providers of Spatial Data—A Case Study from Slovakia

3D Point Cloud Generation Based on Multi-Sensor Fusion

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