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Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and t, Optical and Laser Remote Sensing

Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and t, Optical and Laser Remote Sensing

Heritage documentation is implemented by digitally recording historical artifacts for the conservation and protection of these cultural heritage objects. As efficient spatial data acquisition tools, laser scanners have been widely used to collect highly accurate three-dimensional (3D) point clouds without damaging the original structure and the environment. To ensure the integrity and quality of the collected data, field inspection (i.e., on-spot checking the data quality) should be carried out to determine the need for additional measurements (i.e., extra laser scanning for areas with quality issues such as data missing and quality degradation). To facilitate inspection of all collected point clouds, especially checking the quality issues in overlaps between adjacent scans, all scans should be registered together. Thus, a point cloud registration method that is able to register scans fast and robustly is required. To fulfill the aim, this study proposes an efficient probabilistic registration for free-form cultural heritage objects by integrating the proposed principal direction descriptor and curve constraints. We developed a novel shape descriptor based on a local frame of principal directions. Within the frame, its density and distance feature images were generated to describe the shape of the local surface. We then embedded the descriptor into a probabilistic framework to reject ambiguous matches. Spatial curves were integrated as constraints to delimit the solution space. Finally, a multi-view registration was used to refine the position and orientation of each scan for the field inspection. Comprehensive experiments show that the proposed method was able to perform well in terms of rotation error, translation error, robustness, and runtime and outperformed some commonly used approaches., Optical and Laser Remote Sensing

Localization is a problem of ’where we are’. Localization techniques help people understand their surrounding environment based on extracted position information in a geographic reference map. The development of global navigation satellite system (GNSS), light detection and ranging (LiDAR), computer vision (CV), etc., enables us to apply localization techniques for more specific tasks. Autonomous driving or robotics needs a reliable localization technique that both instantly retrieves an accurate positioning result and detailed information of the environment, which is challenging to realize in an urban environment. In our research, we propose a 3D point cloud localization framework in the urban environment to realize localization of terrestrial laser scanning (TLS) static scans in mobile laser scanning (MLS) point clouds. 3D point cloud localization consists of two steps: place recognition which aims to find the location of a TLS point cloud in a big MLS point cloud and pose refinement which accurately aligns TLS and corresponding MLS point clouds. In place recognition, about 100 cylinder-like objects with 2D coordinate information are retrieved per million points in a TLS scan or a MLS scene, taking nearly 15 seconds. 5 TLS static scans in the experiment are all successfully matched to corresponding MLS scenes by the Gaussian mixture model (GMM) probabilistic estimation based on extracted cylinder-like objects. Results indicate that it is possible to realize place recognition by designing an object feature descriptor instead of a fine feature descriptor, even though TLS and MLS point clouds are collected at different times (2016 and 2020). A cylinder-like object feature descriptor saves time, compared with taking more than 100 seconds per million points during point feature extraction. In pose refinement, point clouds of TLS scans and corresponding MLS scenes are resampled and inputted to a neural network which consists of feature extraction block (FEB), correspond, Geoscience and Remote Sensing

Heritage documentation is implemented by digitally recording historical artifacts for the conservation and protection of these cultural heritage objects. As efficient spatial data acquisition tools, laser scanners have been widely used to collect highly accurate three-dimensional (3D) point clouds without damaging the original structure and the environment. To ensure the integrity and quality of the collected data, field inspection (i.e., on-spot checking the data quality) should be carried out to determine the need for additional measurements (i.e., extra laser scanning for areas with quality issues such as data missing and quality degradation). To facilitate inspection of all collected point clouds, especially checking the quality issues in overlaps between adjacent scans, all scans should be registered together. Thus, a point cloud registration method that is able to register scans fast and robustly is required. To fulfill the aim, this study proposes an efficient probabilistic registration for free-form cultural heritage objects by integrating the proposed principal direction descriptor and curve constraints. We developed a novel shape descriptor based on a local frame of principal directions. Within the frame, its density and distance feature images were generated to describe the shape of the local surface. We then embedded the descriptor into a probabilistic framework to reject ambiguous matches. Spatial curves were integrated as constraints to delimit the solution space. Finally, a multi-view registration was used to refine the position and orientation of each scan for the field inspection. Comprehensive experiments show that the proposed method was able to perform well in terms of rotation error, translation error, robustness, and runtime and outperformed some commonly used approaches., Optical and Laser Remote Sensing

Localization is a problem of ’where we are’. Localization techniques help people understand their surrounding environment based on extracted position information in a geographic reference map. The development of global navigation satellite system (GNSS), light detection and ranging (LiDAR), computer vision (CV), etc., enables us to apply localization techniques for more specific tasks. Autonomous driving or robotics needs a reliable localization technique that both instantly retrieves an accurate positioning result and detailed information of the environment, which is challenging to realize in an urban environment. In our research, we propose a 3D point cloud localization framework in the urban environment to realize localization of terrestrial laser scanning (TLS) static scans in mobile laser scanning (MLS) point clouds. 3D point cloud localization consists of two steps: place recognition which aims to find the location of a TLS point cloud in a big MLS point cloud and pose refinement which accurately aligns TLS and corresponding MLS point clouds. In place recognition, about 100 cylinder-like objects with 2D coordinate information are retrieved per million points in a TLS scan or a MLS scene, taking nearly 15 seconds. 5 TLS static scans in the experiment are all successfully matched to corresponding MLS scenes by the Gaussian mixture model (GMM) probabilistic estimation based on extracted cylinder-like objects. Results indicate that it is possible to realize place recognition by designing an object feature descriptor instead of a fine feature descriptor, even though TLS and MLS point clouds are collected at different times (2016 and 2020). A cylinder-like object feature descriptor saves time, compared with taking more than 100 seconds per million points during point feature extraction. In pose refinement, point clouds of TLS scans and corresponding MLS scenes are resampled and inputted to a neural network which consists of feature extraction block (FEB), correspond, Geoscience and Remote Sensing

Global Navigation Satellite System (GNSS) has been developed in recent several decades, which provides a new technique for timing, positioning and navigation. And GNSS positioning is a basic service to us, both in daily life and scientific research. During high-precise positioning, ambiguity resolution is a key factor that has a huge influence on the accuracy of the result. And the wrong fixing is themain source of lose of accuracy, so many methods of test are proposed to validate the fixing result. We are interested in the performance of solutions that have been labeled as wrong fixing, and want to check if those wrong fixings should be excluded or accepted during estimation. At firstwe introduce the basic model and challenges of ambiguity fixing, aswell as the widely usedmethod integer least squares and Z-transformation. Some previous research of distribution of fixed solution also enables us to compute the bounds of baseline residual. Then we focus on an example to do some pre-research, and find out the main research question - how to accept wrong fixing during estimation and the impact under different scenarios or estimation methods. We use a simulation-basedmethod to do the research but the measurements are generated based on the real ephemeris in certain day. After giving the double difference measurement model based on code and phase with respect to single or dual frequencies, in short baseline scenario, we define some parameters 1-norm, infinity-norm, weighted 2-norm, and good/bad performance of wrong fixings that might be useful for analysis. And some detailed information in chosen epochs are shown with multiple figures and we derive those which are helpful, such as infinity-normratio. Then we develop 4 validation methods, Infinity-normratio detection (RD),Weighted 2-norm detection (WD), 1-norm baseline residual detection (BD1) and Infinity-norm baseline residual detection (BDi), to check if we could recognize wrong fixings with good performance, Additional Thesis AES4011-10, Geoscience and Remote Sensing

Global Navigation Satellite System (GNSS) has been developed in recent several decades, which provides a new technique for timing, positioning and navigation. And GNSS positioning is a basic service to us, both in daily life and scientific research. During high-precise positioning, ambiguity resolution is a key factor that has a huge influence on the accuracy of the result. And the wrong fixing is themain source of lose of accuracy, so many methods of test are proposed to validate the fixing result. We are interested in the performance of solutions that have been labeled as wrong fixing, and want to check if those wrong fixings should be excluded or accepted during estimation. At firstwe introduce the basic model and challenges of ambiguity fixing, aswell as the widely usedmethod integer least squares and Z-transformation. Some previous research of distribution of fixed solution also enables us to compute the bounds of baseline residual. Then we focus on an example to do some pre-research, and find out the main research question - how to accept wrong fixing during estimation and the impact under different scenarios or estimation methods. We use a simulation-basedmethod to do the research but the measurements are generated based on the real ephemeris in certain day. After giving the double difference measurement model based on code and phase with respect to single or dual frequencies, in short baseline scenario, we define some parameters 1-norm, infinity-norm, weighted 2-norm, and good/bad performance of wrong fixings that might be useful for analysis. And some detailed information in chosen epochs are shown with multiple figures and we derive those which are helpful, such as infinity-normratio. Then we develop 4 validation methods, Infinity-normratio detection (RD),Weighted 2-norm detection (WD), 1-norm baseline residual detection (BD1) and Infinity-norm baseline residual detection (BDi), to check if we could recognize wrong fixings with good performance, Additional Thesis AES4011-10, Geoscience and Remote Sensing

The deep genesis of the Jiaodong granites and related gold deposits is attributed to strong activation of the Mesozoic mantle in eastern China, resulting in partial melting of the Jiaodong Group, crystallisation of granite and concentration of Au. The Cishan granite is mainly biotite monzogranite in composition, its major minerals being plagioclase, K-feldspar, quartz and biotite. Its characteristics differ from those of the other three major ore-bearing granites, Linglong, Guojialing and Kunyushan, indicating that their gold-bearing deposits are also different. The Cishan granite has features favouring possible metallisation, indicating that the Nanzhangjia deposit which it hosts should not be discounted despite its relatively small metallogenic capacity and low grades., The deep genesis of the Jiaodong granites and related gold deposits is attributed to strong activation of the Mesozoic mantle in eastern China, resulting in partial melting of the Jiaodong Group, crystallisation of granite and concentration of Au. The Cishan granite is mainly biotite monzogranite in composition, its major minerals being plagioclase, K-feldspar, quartz and biotite. Its characteristics differ from those of the other three major ore-bearing granites, Linglong, Guojialing and Kunyushan, indicating that their gold-bearing deposits are also different. The Cishan granite has features favouring possible metallisation, indicating that the Nanzhangjia deposit which it hosts should not be discounted despite its relatively small metallogenic capacity and low grades.