Precise Indoor Path Planning Based on Hybrid Model of GeoSOT and BIM

With the improvement of urban infrastructure and the increase in the coverage of high-rise buildings, the demand for location information services inside buildings is becoming more and more urgent. Moreover, indoor path planning, as a prerequisite and basis for realizing path guidance inside buildings, has become a research focus in the field of location services. This makes the accurate planning of indoor paths an urgent problem to be solved at present. This requires dynamic and precise planning from static fuzzy planning, and the corresponding scene converted from a two-dimensional plane to a three-dimensional one. However, most of the existing indoor path planning methods focus on the use of two-dimensional floor plans in buildings to build indoor maps and rely on traditional path search algorithms for pathfinding, which lack in the efficient use of the building’s own geometric and attribute information and lack consideration of the internal spatial topology of the building, making it difficult to meet the needs of indoor multi-layer continuous space path planning. Considering this relationship, it is difficult to meet the path planning needs of indoor multi-layer continuous spaces. In addition, the two-dimensional expression dominated by arrows and line drawings also greatly reduces the intuitiveness and interactivity of path expression. Regarding this, this paper combines the GeoSOT grid with accurate real geographic information and the BIM model and proposes an accurate indoor path planning method. Finally, using Guanlan Commercial Street in Baiyin City as the experimental object, the precise planning and generation of indoor paths and the interaction of visual displays on the web page are realized. It has been verified that the method has certain reference and application values for meeting the demand of location information services in buildings and building an integrated indoor–outdoor navigation service platform

Precise Indoor Path Planning Based on Hybrid Model of GeoSOT and BIM

With the improvement of urban infrastructure and the increase in the coverage of high-rise buildings, the demand for location information services inside buildings is becoming more and more urgent. Moreover, indoor path planning, as a prerequisite and basis for realizing path guidance inside buildings, has become a research focus in the field of location services. This makes the accurate planning of indoor paths an urgent problem to be solved at present. This requires dynamic and precise planning from static fuzzy planning, and the corresponding scene converted from a two-dimensional plane to a three-dimensional one. However, most of the existing indoor path planning methods focus on the use of two-dimensional floor plans in buildings to build indoor maps and rely on traditional path search algorithms for pathfinding, which lack in the efficient use of the building’s own geometric and attribute information and lack consideration of the internal spatial topology of the building, making it difficult to meet the needs of indoor multi-layer continuous space path planning. Considering this relationship, it is difficult to meet the path planning needs of indoor multi-layer continuous spaces. In addition, the two-dimensional expression dominated by arrows and line drawings also greatly reduces the intuitiveness and interactivity of path expression. Regarding this, this paper combines the GeoSOT grid with accurate real geographic information and the BIM model and proposes an accurate indoor path planning method. Finally, using Guanlan Commercial Street in Baiyin City as the experimental object, the precise planning and generation of indoor paths and the interaction of visual displays on the web page are realized. It has been verified that the method has certain reference and application values for meeting the demand of location information services in buildings and building an integrated indoor–outdoor navigation service platform

A Digital Grid Model for Complex Time-Varying Environments in Civil Engineering Buildings

The indoor environment is typically a complex time-varying environment. At present, the problem of indoor modeling is still a hot research topic for scholars at home and abroad. This paper primarily studies indoor time-varying space. On the basis of the Beidou grid framework and time coding model, in the first scenario, a local space subdivision framework based on Beidou is proposed. The necessity of local space subdivision framework is analyzed. In the second scenario, based on the time coding model needle, a local temporal subdivision model, more suitable for a short time domain, is proposed. Then, for the spatial modeling of an indoor time-varying environment, an indoor time-varying mesh frame based on global subdivision, local space subdivision, and local time subdivision is proposed. Using this framework, the indoor environment is represented by the space–time grid, and the basic storage data structure is designed. Finally, the experiment of local subdivision coding in the indoor space–time grid, indoor space–time grid modeling, and an organization experiment is carried out using real data and simulation data. The experimental results verify the feasibility and correctness of the encoding and decoding algorithm of local subdivision encoding in space–time encoding and the calculation algorithm of the space–time relationship. The experimental results also verify the multi-space organization and the management ability of the indoor space–time grid model

BIM Data Model Based on Multi-Scale Grids in Civil Engineering Buildings

The construction of digital twin cities is a current research hotspot; GIS technology and BIM technology are widely used in the field of digital twin cities. However, BIM is still subject to major limitations in its applications, mainly due to huge amounts of model data, low query efficiency and accuracy, non-uniform marking systems, etc. The reason is that the BIM model itself focuses more on the expression of visual effects and lacks spatial calculation ability and the utilization of spatial location information. Secondly, the current lightweight processing methods for BIM models are mostly based on geometric transformation and rendering optimization, focusing more on the data compression and visual quality of the model, which essentially does not change the data structure of the BIM model, and it is difficult to establish the mapping relationship between spatial location and spatial data, information, and resources. In addition, current coding methods proposed for BIM models are mostly based on the line classification method, which realizes the identification of components based on the classification of their attributes, and the location information is stored according to the attributes or natural language descriptions, which need to be parsed and translated when they are used, and this procedure ignores the importance of spatial location in daily management and emergency management. The importance of spatial location in daily management and emergency management is also ignored. Based on this kind of identification code, it is impossible to directly analyze and apply spatial location data. Therefore, this paper takes the combination of GIS technology and BIM technology as the starting point and proposes a BIM data modeling method based on the BeiDou grid code, based on the efficiency of its underlying data organization and the accuracy of its real geographic location expression on the one hand and the completeness of the information expression by BIM and fine three-dimensional visualization on the other hand. Finally, a series of experiments are carried out based on the method. Through visualization modeling and efficiency experiments, different feature models are meshed to verify the feasibility and efficiency of the model. Through coding and information query experiments, the model′s data organization capability, data dynamic carrying capability, and efficient spatial computation capability and practical application capability are verified

Geometric Construction of Video Stereo Grid Space

The construction of digital twin cities is a current research hotspot. Video data are one of the important aspects of digital twin cities, and their digital modeling is one of the important foundations of its construction. For this reason, the construction and digital analysis of video data space has become an urgent problem to be solved. After in-depth research, this study found that the existing video space construction methods have three shortcomings: first, the problem of high requirements for objective conditions or low accuracy; second, the lack of easy and efficient mapping algorithms from 2D video pixel coordinates to 3D; and third, the lack of efficient correlation mechanisms between video space and external geographic information, making it difficult to integrate video space with external information, and thus prevent a more effective analysis. In view of the above problems, this paper proposes a video stereo grid geometric space construction method based on GeoSOT-3D stereo grid coding and a camera imaging model to form a video stereo grid space model. Finally, targeted experiments of video stereo grid space geometry construction were conducted to analyze the experimental results before and after optimization and compare the variance size to verify the feasibility and effectiveness of the model

Robot Path Planning Method Based on Indoor Spacetime Grid Model

In the context of digital twins, smart city construction and artificial intelligence technology are developing rapidly, and more and more mobile robots are performing tasks in complex and time-varying indoor environments, making, at present, the unification of modeling, dynamic expression, visualization of operation, and wide application between robots and indoor environments a pressing problem to be solved. This paper presents an in-depth study on this issue and summarizes three major types of methods: geometric modeling, topological modeling, and raster modeling, and points out the advantages and disadvantages of these three types of methods. Therefore, in view of the current pain points of robots and complex time-varying indoor environments, this paper proposes an indoor spacetime grid model based on the three-dimensional division framework of the Earth space and innovatively integrates time division on the basis of space division. On the basis of the model, a dynamic path planning algorithm for the robot in the complex time-varying indoor environment is designed, that is, the Spacetime-A* algorithm (STA* for short). Finally, the indoor spacetime grid modeling experiment is carried out with real data, which verifies the feasibility and correctness of the spacetime relationship calculation algorithm encoded by the indoor spacetime grid model. Then, experiments are carried out on the multi-group path planning algorithms of the robot under the spacetime grid, and the feasibility of the STA* algorithm under the indoor spacetime grid and the superiority of the spacetime grid are verified

A Precise Urban Component Management Method Based on the GeoSOT Grid Code and BIM

Currently, the rapid development of cities and the rapid increase in urban populations have led to a sharp increase in urban components, making precise urban component management, query efficiency, and operational visualization urgent problems to be solved. In this paper, an in-depth study is carried out, pointing out that the current two-dimensional map or component management method based on a real-life three-dimensional city has defects, including query difficulty, fuzzy management, and inefficiency, and it is impossible to accurately and efficiently manage urban components. Then, this paper uses a combination of GIS technology and BIM technology as the starting point. On one hand, this combined technology is based on the high efficiency of the underlying data organization of the GeoSOT grid code and the accuracy of real geographic location expression; on the other hand, based on the integrity of the building information representation and the accuracy of the relative position of internal components of BIM, a precise urban component management method based on GeoSOT grid code and BIM is proposed. Finally, based on this method, a real-time 3D Earth visualization platform is established by using the Cesium platform. Taking the fire hydrant component management of the commercial Guanlan Street in Baiyin City, Gansu Province, China as an example, the precise management of the components in this area is realized, which proves that the method can achieve precise urban component management

A Low-Altitude Flight Conflict Detection Algorithm Based on a Multilevel Grid Spatiotemporal Index

Flight conflict detection is fundamental to flight dispatch, trajectory planning, and flight safety control. An ever-increasing aircraft population and higher speeds, particularly the emergence of hypersonic/supersonic aircrafts, are challenging the timeliness and accuracy of flight conflict detection. Traditional trajectory conflict detection algorithms rely on traversing multivariate equations of every two trajectories, in order to yield the conflict result and involve extensive computation and high algorithmic complexity; these algorithms are often unable to provide the flight conflict solutions required quickly enough. In this paper, we present a novel, low-altitude flight conflict detection algorithm, based on the multi-level grid spatiotemporal index, that transforms the traditional trajectory-traversing multivariate conflict computation into a grid conflict state query of distributed grid databases. Essentially, this is a method of exchanging "storage space" for "computational time". First, we build the spatiotemporal subdivision and encoding model based on the airspace. The model describes the geometries of the trajectories, low-altitude obstacles, or dangerous fields and identifies the grid with grid codes. Next, we design a database table structure of the grid and create a grid database. Finally, we establish a multilevel grid spatiotemporal index, design a query optimization scheme, and examine the flight conflict detection results from the grid database. Experimental verification confirms that the computation efficiency of our algorithm is one order of magnitude higher than those of traditional methods. Our algorithm can perform real-time (dynamic/static) conflict detection on both individual aircraft and aircraft flying in formation with more efficient trajectory planning and airspace utilization