Point cloud data have become the primary spatial data source for the 3D reconstruction of building engineering, where 3D reconstructed building information models can improve construction efficiency. In such applicati...
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Point cloud data have become the primary spatial data source for the 3D reconstruction of building engineering, where 3D reconstructed building information models can improve construction efficiency. In such applications, detecting windows and doors is essential. Previous research mainly used red-green-blue (RGB) information or semantic features for detection, where the combination of these two features was not considered. Therefore, this research proposed a practical approach to detecting windows and doors using point cloud data with the combination of semantic features and material characteristics. The point cloud data are first segmented using Gradient Filtering and Random Sample Consensus (RANSAC) to obtain the 3D indoor data without intrusions and protrusions. As input, the 3D indoor data are projected to horizontal planes as 2D point cloud data. The 2D point cloud data are then transformed to 2D images, representing the indoor area for feature extraction. On the 2D images, the 2D boundary of each potential opening is extracted using an improved bounding box algorithm, and the extraction result is transformed back to 3D data. Based on the 3D data, the reflectivity of building material is applied to differentiate windows and doors from potential openings, and the number of data points is used to check the opening condition of windows and doors. The abovementioned approach was tested using the point cloud data representing one campus building, including two big rooms and one corridor. The experimental results showed that accurate detection of windows and doors was successfully reached. The completeness of the detection is 100%, and the correctness of the detection is 90.32%. The total time for the feature extraction is 22.8 s for processing 2 million point cloud data, including time from reading data of 10.319 s and time from showing the results of 4.938 s.
Aiming at that bounding box algorithm's coverage is depend on the anchor nodes density in network and DV-Hop has a larger location error, this paper presents a hybrid algorithm that combines the boundingbox local...
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ISBN:
(纸本)9780769549354
Aiming at that bounding box algorithm's coverage is depend on the anchor nodes density in network and DV-Hop has a larger location error, this paper presents a hybrid algorithm that combines the boundingbox localization algorithm and DV-Hop localization algorithm, which is called BDV-Hop algorithm. Simulating in MATLAB platform, we analyze boundingbox, DV-Hop and the BDV-Hop algorithm respectively. Extensive simulation show that under a variety of network conditions, BDV-Hop algorithm inherits both the advantage of boundingbox that has smaller location error and cost, and the advantage of DV-Hop that has high location coverage. At the same time BDV-Hop algorithm have a better adaptability.
This work presents a localization scheme for use in wireless sensor networks (WSNs) that is based on a proposed connectivity-based RF localization strategy called the distributed Fermat-point location estimation algor...
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This work presents a localization scheme for use in wireless sensor networks (WSNs) that is based on a proposed connectivity-based RF localization strategy called the distributed Fermat-point location estimation algorithm (DFPLE). DFPLE applies triangle area of location estimation formed by intersections of three neighboring beacon nodes. The Fermat point is determined as the shortest path from three vertices of the triangle. The area of estimated location then refined using Fermat point to achieve minimum error in estimating sensor nodes location. DFPLE solves problems of large errors and poor performance encountered by localization schemes that are based on a bounding box algorithm. Performance analysis of a 200-node development environment reveals that, when the number of sensor nodes is below 150, the mean error decreases rapidly as the node density increases, and when the number of sensor nodes exceeds 170, the mean error remains below 1% as the node density increases. Second, when the number of beacon nodes is less than 60, normal nodes lack sufficient beacon nodes to enable their locations to be estimated. However, the mean error changes slightly as the number of beacon nodes increases above 60. Simulation results revealed that the proposed algorithm for estimating sensor positions is more accurate than existing algorithms, and improves upon conventional boundingbox strategies.
Similar to many technological developments, wireless sensor networks have emerged from military needs and found its way into civil applications. Today wireless sensor networks has become a key technology for different...
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ISBN:
(纸本)9781424451364
Similar to many technological developments, wireless sensor networks have emerged from military needs and found its way into civil applications. Today wireless sensor networks has become a key technology for different kinds of smart environments and an intense research effort is currently underway to enable the application of wireless sensor networks for a wide range of industrial problems. Wireless networks are of a particular importance when a large number of sensor nodes have to be deployed and/or in hazardouse situations. The research field in this paper is supposed to be on this issue that a lot of these sensors are distributed randomly and just a few of them are aware of their position [e.g. by GPS]. The purpose is to determine the best way that allows all the nodes to find their position. This paper presents an effective geometric algorithm for localization. Result of implementation show that this algorithm can be a better substitution for current methods because of lower expense and simple implementation.
This paper presents a distributed fermat-point range estimation strategy, which is important in the moving sensor localization applications. The fermat-point is defined as a point which minimizes the sum of distances ...
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ISBN:
(纸本)9781424424832
This paper presents a distributed fermat-point range estimation strategy, which is important in the moving sensor localization applications. The fermat-point is defined as a point which minimizes the sum of distances from three sensors inside a triangle. This point is indeed at the triangle's center of gravity. We solve the problems of large errors and poor performance in the bounding box algorithm. We obtain two results by performance analysis for a deployed environment with 200 sensor nodes. First, when the number of sensor nodes is below 150, the mean error decreases rapidly as the node density increases, and when the number of sensor nodes exceeds 170, the mean error stays below 1%. Second, when the number of beacon nodes is below 60, the normal nodes do not have sufficient number of accurate beacon nodes to help them estimate their locations. However, when the number of beacon nodes exceeds 60, the mean error changes slightly. Simulation results indicated that the proposed algorithm for sensor position estimation is more accurate than existing algorithms and improves on existing boundingbox strategies.
This paper presents a distributed fermat-point range estimation strategy, which is important in the moving sensor localization applications. The fermat-point is defined as a point which minimizes the sum of distances ...
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This paper presents a distributed fermat-point range estimation strategy, which is important in the moving sensor localization applications. The fermat-point is defined as a point which minimizes the sum of distances from three sensors inside a triangle. This point is indeed at the triangle's center of gravity. We solve the problems of large errors and poor performance in the bounding box algorithm. We obtain two results by performance analysis for a deployed environment with 200 sensor nodes. First, when the number of sensor nodes is below 150, the mean error decreases rapidly as the node density increases, and when the number of sensor nodes exceeds 170, the mean error stays below 1%. Second, when the number of beacon nodes is below 60, the normal nodes do not have sufficient number of accurate beacon nodes to help them estimate their locations. However, when the number of beacon nodes exceeds 60, the mean error changes slightly. Simulation results indicated that the proposed algorithm for sensor position estimation is more accurate than existing algorithms and improves on existing boundingbox strategies.
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