The Experts below are selected from a list of 99 Experts worldwide ranked by ideXlab platform
Sihao Zhao - One of the best experts on this subject based on the ideXlab platform.
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a tightly coupled rtk ins algorithm with ambiguity resolution in the position domain for ground vehicles in harsh urban environments
Sensors, 2018Co-Authors: Xiaowei Cui, Sihao ZhaoAbstract:Vehicles driving in urban canyons are always confronted with a degraded Global Navigation Satellite System (GNSS) signal environment. The surrounding obstacles may cause signal reflections or blockages, which lead to large multipath noises and intermittent GNSS reception. Under these circumstances, it is not feasible to use conventional real-time kinematic (RTK) algorithms to maintain high-precision performance for positioning. In order to meet the special requirements of safety-critical applications under non-ideal observation conditions, a novel tightly coupled RTK/Inertial Navigation System (INS) algorithm is proposed in this paper, which can provide accurate and reliable positioning results continuously. Our integrated RTK/INS algorithm has three features. Firstly, INS measurements are used to help search for integer ambiguities in the position domain. INS solutions can provide a more accurate initial location and a more efficient search region. Secondly, the criterion for determining whether a Candidate position is the correct solution is only related to the fractional value of the carrier-phase measurement. Thus, the new algorithm is immune to cycle slips as well as large pseudorange noises. Thirdly, our algorithm can provide more accurate ranging information than the pseudorange, even though it may not necessarily be fixed successfully, because it selects the weighted ambiguity solution as the result rather than the Candidate Point with maximum probability. The proposed algorithm is demonstrated on both simulated and real datasets. Compared with single epoch RTK and conventional tightly coupled RTK/INS integrations that search integer ambiguities in the ambiguity domain, our method attains better accuracy and stability in a simulated environment. Moreover, the real experimental results are presented to validate the performance of the new integrated navigation algorithm.
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A Tightly Coupled RTK/INS Algorithm with Ambiguity Resolution in the Position Domain for Ground Vehicles in Harsh Urban Environments
MDPI AG, 2018Co-Authors: Xiaowei Cui, Sihao ZhaoAbstract:Vehicles driving in urban canyons are always confronted with a degraded Global Navigation Satellite System (GNSS) signal environment. The surrounding obstacles may cause signal reflections or blockages, which lead to large multipath noises and intermittent GNSS reception. Under these circumstances, it is not feasible to use conventional real-time kinematic (RTK) algorithms to maintain high-precision performance for positioning. In order to meet the special requirements of safety-critical applications under non-ideal observation conditions, a novel tightly coupled RTK/Inertial Navigation System (INS) algorithm is proposed in this paper, which can provide accurate and reliable positioning results continuously. Our integrated RTK/INS algorithm has three features. Firstly, INS measurements are used to help search for integer ambiguities in the position domain. INS solutions can provide a more accurate initial location and a more efficient search region. Secondly, the criterion for determining whether a Candidate position is the correct solution is only related to the fractional value of the carrier-phase measurement. Thus, the new algorithm is immune to cycle slips as well as large pseudorange noises. Thirdly, our algorithm can provide more accurate ranging information than the pseudorange, even though it may not necessarily be fixed successfully, because it selects the weighted ambiguity solution as the result rather than the Candidate Point with maximum probability. The proposed algorithm is demonstrated on both simulated and real datasets. Compared with single epoch RTK and conventional tightly coupled RTK/INS integrations that search integer ambiguities in the ambiguity domain, our method attains better accuracy and stability in a simulated environment. Moreover, the real experimental results are presented to validate the performance of the new integrated navigation algorithm
Jinhu Wang - One of the best experts on this subject based on the ideXlab platform.
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sigvox a 3d feature matching algorithm for automatic street object recognition in mobile laser scanning Point clouds
Isprs Journal of Photogrammetry and Remote Sensing, 2017Co-Authors: Jinhu Wang, R C Lindenbergh, M MenentiAbstract:Abstract Urban road environments contain a variety of objects including different types of lamp poles and traffic signs. Its monitoring is traditionally conducted by visual inspection, which is time consuming and expensive. Mobile laser scanning (MLS) systems sample the road environment efficiently by acquiring large and accurate Point clouds. This work proposes a methodology for urban road object recognition from MLS Point clouds. The proposed method uses, for the first time, shape descriptors of complete objects to match repetitive objects in large Point clouds. To do so, a novel 3D multi-scale shape descriptor is introduced, that is embedded in a workflow that efficiently and automatically identifies different types of lamp poles and traffic signs. The workflow starts by tiling the raw Point clouds along the scanning trajectory and by identifying non-ground Points. After voxelization of the non-ground Points, connected voxels are clustered to form Candidate objects. For automatic recognition of lamp poles and street signs, a 3D significant eigenvector based shape descriptor using voxels (SigVox) is introduced. The 3D SigVox descriptor is constructed by first subdividing the Points with an octree into several levels. Next, significant eigenvectors of the Points in each voxel are determined by principal component analysis (PCA) and mapped onto the appropriate triangle of a sphere approximating icosahedron. This step is repeated for different scales. By determining the similarity of 3D SigVox descriptors between Candidate Point clusters and training objects, street furniture is automatically identified. The feasibility and quality of the proposed method is verified on two Point clouds obtained in opposite direction of a stretch of road of 4 km. 6 types of lamp pole and 4 types of road sign were selected as objects of interest. Ground truth validation showed that the overall accuracy of the ∼170 automatically recognized objects is approximately 95%. The results demonstrate that the proposed method is able to recognize street furniture in a practical scenario. Remaining difficult cases are touching objects, like a lamp pole close to a tree.
Xiaowei Cui - One of the best experts on this subject based on the ideXlab platform.
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a tightly coupled rtk ins algorithm with ambiguity resolution in the position domain for ground vehicles in harsh urban environments
Sensors, 2018Co-Authors: Xiaowei Cui, Sihao ZhaoAbstract:Vehicles driving in urban canyons are always confronted with a degraded Global Navigation Satellite System (GNSS) signal environment. The surrounding obstacles may cause signal reflections or blockages, which lead to large multipath noises and intermittent GNSS reception. Under these circumstances, it is not feasible to use conventional real-time kinematic (RTK) algorithms to maintain high-precision performance for positioning. In order to meet the special requirements of safety-critical applications under non-ideal observation conditions, a novel tightly coupled RTK/Inertial Navigation System (INS) algorithm is proposed in this paper, which can provide accurate and reliable positioning results continuously. Our integrated RTK/INS algorithm has three features. Firstly, INS measurements are used to help search for integer ambiguities in the position domain. INS solutions can provide a more accurate initial location and a more efficient search region. Secondly, the criterion for determining whether a Candidate position is the correct solution is only related to the fractional value of the carrier-phase measurement. Thus, the new algorithm is immune to cycle slips as well as large pseudorange noises. Thirdly, our algorithm can provide more accurate ranging information than the pseudorange, even though it may not necessarily be fixed successfully, because it selects the weighted ambiguity solution as the result rather than the Candidate Point with maximum probability. The proposed algorithm is demonstrated on both simulated and real datasets. Compared with single epoch RTK and conventional tightly coupled RTK/INS integrations that search integer ambiguities in the ambiguity domain, our method attains better accuracy and stability in a simulated environment. Moreover, the real experimental results are presented to validate the performance of the new integrated navigation algorithm.
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A Tightly Coupled RTK/INS Algorithm with Ambiguity Resolution in the Position Domain for Ground Vehicles in Harsh Urban Environments
MDPI AG, 2018Co-Authors: Xiaowei Cui, Sihao ZhaoAbstract:Vehicles driving in urban canyons are always confronted with a degraded Global Navigation Satellite System (GNSS) signal environment. The surrounding obstacles may cause signal reflections or blockages, which lead to large multipath noises and intermittent GNSS reception. Under these circumstances, it is not feasible to use conventional real-time kinematic (RTK) algorithms to maintain high-precision performance for positioning. In order to meet the special requirements of safety-critical applications under non-ideal observation conditions, a novel tightly coupled RTK/Inertial Navigation System (INS) algorithm is proposed in this paper, which can provide accurate and reliable positioning results continuously. Our integrated RTK/INS algorithm has three features. Firstly, INS measurements are used to help search for integer ambiguities in the position domain. INS solutions can provide a more accurate initial location and a more efficient search region. Secondly, the criterion for determining whether a Candidate position is the correct solution is only related to the fractional value of the carrier-phase measurement. Thus, the new algorithm is immune to cycle slips as well as large pseudorange noises. Thirdly, our algorithm can provide more accurate ranging information than the pseudorange, even though it may not necessarily be fixed successfully, because it selects the weighted ambiguity solution as the result rather than the Candidate Point with maximum probability. The proposed algorithm is demonstrated on both simulated and real datasets. Compared with single epoch RTK and conventional tightly coupled RTK/INS integrations that search integer ambiguities in the ambiguity domain, our method attains better accuracy and stability in a simulated environment. Moreover, the real experimental results are presented to validate the performance of the new integrated navigation algorithm
M Menenti - One of the best experts on this subject based on the ideXlab platform.
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sigvox a 3d feature matching algorithm for automatic street object recognition in mobile laser scanning Point clouds
Isprs Journal of Photogrammetry and Remote Sensing, 2017Co-Authors: Jinhu Wang, R C Lindenbergh, M MenentiAbstract:Abstract Urban road environments contain a variety of objects including different types of lamp poles and traffic signs. Its monitoring is traditionally conducted by visual inspection, which is time consuming and expensive. Mobile laser scanning (MLS) systems sample the road environment efficiently by acquiring large and accurate Point clouds. This work proposes a methodology for urban road object recognition from MLS Point clouds. The proposed method uses, for the first time, shape descriptors of complete objects to match repetitive objects in large Point clouds. To do so, a novel 3D multi-scale shape descriptor is introduced, that is embedded in a workflow that efficiently and automatically identifies different types of lamp poles and traffic signs. The workflow starts by tiling the raw Point clouds along the scanning trajectory and by identifying non-ground Points. After voxelization of the non-ground Points, connected voxels are clustered to form Candidate objects. For automatic recognition of lamp poles and street signs, a 3D significant eigenvector based shape descriptor using voxels (SigVox) is introduced. The 3D SigVox descriptor is constructed by first subdividing the Points with an octree into several levels. Next, significant eigenvectors of the Points in each voxel are determined by principal component analysis (PCA) and mapped onto the appropriate triangle of a sphere approximating icosahedron. This step is repeated for different scales. By determining the similarity of 3D SigVox descriptors between Candidate Point clusters and training objects, street furniture is automatically identified. The feasibility and quality of the proposed method is verified on two Point clouds obtained in opposite direction of a stretch of road of 4 km. 6 types of lamp pole and 4 types of road sign were selected as objects of interest. Ground truth validation showed that the overall accuracy of the ∼170 automatically recognized objects is approximately 95%. The results demonstrate that the proposed method is able to recognize street furniture in a practical scenario. Remaining difficult cases are touching objects, like a lamp pole close to a tree.
Hidehiko Saito - One of the best experts on this subject based on the ideXlab platform.
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three distinct Candidate Point mutations of the von willebrand factor gene in four patients with type iia von willebrand disease
Thrombosis and Haemostasis, 1992Co-Authors: Isamu Sugiura, Tadashi Matsushita, Mitsune Tanimoto, Isao Takahashi, Tomio Yamazaki, Koji Yamamoto, Junki Takamatsu, Tadashi Kamiya, Hidehiko SaitoAbstract:Type IIA von Willebrand disease (vWD) is the most common type II vWD and is characterized by the selective loss of large and intermediate sized multimers. One explanation for this disorder has been postulated to be a qualitative defect in von Willebrand factor (vWF) which results in increased susceptibility to proteolysis at the bond between residues Tyr842 and Met843. Four missense mutations that may cause type IIA vWD have recently been identified near the cleavage site. We analyzed the molecular basis for type IIA vWD in six patients. A 512 bp DNA sequence spanning the proteolytic cleavage site was targeted for PCR amplification and sequencing. We exploited a difference in restriction sites between the vWF gene and the pseudogene and have designed allele-specific oligomer used with PCR to distinguish these two genes. Three Candidate missense mutations; Ser743 (TCG)----Leu (TTG), Leu799 (CTG)----Pro (CCG), and Arg834 (CGG)----Trp (TGG) were identified in 4 out of 6 patients. The amino acid substitution at Arg834 has been reported previously, but the other substitutions at Ser743 and Leu799 are novel Candidate mutations locating 99 and 43 amino acids to the N-terminal side of the cleavage site, respectively. Our results indicate that amino acid substitutions located relatively distant from the cleavage site may also be involved in type IIA vWD.
