CN109459024B - Indoor navigation data model construction and path planning method under different behavior modes - Google Patents
Indoor navigation data model construction and path planning method under different behavior modes Download PDFInfo
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Abstract
The invention provides an indoor navigation data model construction and path planning method under different behavior modes, which is characterized by comprising the following steps: step 1, reading map data, constructing a map navigation data model, and constructing a map navigation database, so that separation of data processing construction nodes from an actual application stage is realized, and efficiency is improved; step 2, reading navigation model data from an offline database, and constructing a navigation data model; and 3, planning the path, acquiring a path planning result, and optimizing and smoothing the result. The indoor navigation data model and the path planning method based on different indoor behavior modes can obtain reasonable path planning results in indoor scenes, have obvious advantages in the indoor navigation method considering the multi-behavior mode, and can be compatible with various scene requirements.
Description
Technical Field
The invention belongs to the field of indoor positioning navigation, and particularly relates to an indoor navigation data model construction and path planning method under different behavior modes
Background
With the development and change of modern cities, the indoor range of large parking lots and buildings gradually increases to large scale, multiple floors and complicated development, and indoor road finding becomes more and more difficult. The existing navigation function is limited to outdoor map path finding, the indoor path finding function is rarely available, people's life is limited to outdoors, the demand for solving the indoor path finding problem is more urgent along with the increase of the proportion of indoor life in people's life, and the solution of the indoor path finding problem is generated.
Many existing researches propose a node-based topological model which is used for an organization mode of an indoor map, but the ambiguity problem of an indoor space road network cannot be solved well; a grid-based model is also provided, and data organization under the model can reasonably provide the path planning of indoor pedestrian, but cannot provide a reasonable path planning result of a vehicle traveling mode under an indoor parking lot model with accurate road network information.
Disclosure of Invention
Aiming at the problems in the prior art, the technical scheme adopted by the invention for solving the problems in the prior art is as follows:
a method for building an indoor navigation data model and planning a path under different behavior modes is characterized by comprising the following steps:
step 1, reading map data, constructing a map navigation data model, and constructing a map navigation database, so that separation of data processing construction nodes from an actual application stage is realized, and efficiency is improved;
step 2, reading navigation model data from an offline database, and constructing a navigation data model;
and 3, planning the path, acquiring a path planning result, and optimizing and smoothing the result.
The establishing of the data model in the step 1 specifically comprises the following processes:
step 1.1, data preprocessing, namely preprocessing parking lot map data with road network information to extract all communicable road network data; secondly, extracting boundary data of the parking lot map and the common indoor map;
step 1.2, carrying out grid formation on common data, wherein the boundary data of a common indoor map and the boundary data of a parking lot map are used as inaccessible paths, and grid formation is carried out to form grid map data based on regular hexagons;
step 1.3, grid-forming road network data, namely grid-forming the road network data of the parking lot map in the same manner as in step 1.2, wherein due to the particularity of the map in the vehicle driving mode, only 'road network' is used as a passage in the vehicle driving mode, so that all grid data needs to be negated after grid-forming, namely the road network data is passable, and the rest is not passable;
step 1.4, model data are constructed, and for a parking lot map, two pieces of model data can be obtained, wherein one piece of model data is data based on a pedestrian mode, and the other piece of model data is data suitable for a vehicle driving mode; the ordinary indoor map does not support driving, so only one piece of model data is obtained.
The path planning in the step 3 specifically comprises the following processes:
step 3.1, in order to adapt to the a × algorithm, firstly, the grid data needs to be calculated by adjacent grid data, and the information of the adjacent grid is stored in each grid data, so that the path searching and using are facilitated;
step 3.2, the Manhattan distance is used as a heuristic search factor, so that a terminal point can be found as soon as possible during path search, and the operation efficiency of the algorithm is improved;
step 3.3, optimizing the searched path result, and based on the idea of 'visibility', judging whether the connection line between the two points is 'visibility' from the two ends of the path, namely whether all grids in the area where the connection line between the two points is located can be viewed, if yes, filtering the rest nodes between the two points, and finally obtaining the optimal path planning result;
step 3.4, due to the particularity of the grid data, the finally obtained path result may have "jaggy", which is particularly easy to appear in a narrow road between two boundaries, and therefore, the path result needs to be smoothed, and a general smoothing method is easy to generate a "wall through" phenomenon, so that the problem needs to be considered in the smoothing process. The method adopts a Douglas simplification method to smooth the path, and considers that the boundary grid is widened by two times, so that the threshold value is set to be 1.5 times of the grid width, the smooth result can ensure that the wall is not penetrated and attached, and a more reasonable planned path can be obtained.
The navigation data model obtained in step 1 has the following characteristics:
in addition to the basic grid data model, the map data model also needs to include related map information data, including interlayer connection point data, for performing cross-layer path planning, where the interlayer connection points may include inter-vehicle layer connection points and inter-pedestrian layer connection points, the inter-vehicle connection points need to be merged into the vehicle data model, and the inter-pedestrian layer connection points may include connection point modes such as "elevator" and "stair", and in order to provide multiple types of selections, the connection points may also need to be classified and stored into the pedestrian data model; indoor and outdoor connection point data are used for realizing the integration of indoor and outdoor path planning, and meanwhile, the data are divided into vehicles and pedestrians to be merged according to categories. In addition to some of the above data information, some related grid information, such as the side length of the grid, the range of the data model, etc., needs to be stored.
The path planning result data obtained in the step 3 has the following characteristics:
the method has the advantages that the phenomenon of 'detour' does not exist, the method accords with the actual situation, can support the planning requirements under various behavior modes, solves the problem of 'saw tooth' in the path planning result under the grid model, and can simplify the path planning result as much as possible on the premise of ensuring no 'wall penetration' to obtain the smooth and reasonable optimal path planning result.
The invention has the following advantages:
the indoor navigation data model and the path planning method based on different indoor behavior modes can obtain reasonable path planning results in indoor scenes, have obvious advantages in the indoor navigation method considering the multi-behavior mode, and can be compatible with various scene requirements; the data model of the invention gives consideration to the path planning requirements under different indoor behavior modes, reasonably optimizes and smoothes the path planning result, and can obtain the indoor path planning result according with objective facts.
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FIG. 1 is an overall block diagram of the present invention;
FIG. 2 is a data processing flow diagram;
fig. 3 is a flowchart of path planning and planning result processing.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings, and the main idea of the technical scheme of the invention is as follows: considering that the extractable relevant information of different indoor maps are considered under different indoor behavior modes, carrying out pretreatment and grid meshing in different modes on the indoor maps, and constructing an indoor map data model in a compatible mode; secondly, path planning is carried out on the constructed data model by adopting an A-star algorithm, and heuristic search is carried out by adopting Manhattan distance as a consideration factor in order to improve the speed of the algorithm; and finally, optimizing and smoothing the searched path planning result to obtain a reasonable path planning result.
Compared with the common topological data model, the problem of fuzzy indoor road network can be better solved by adopting the regular hexagonal grid model, and in addition, compared with the traditional square grid, the regular hexagonal grid has the characteristic of the same distance to the adjacent grid.
In specific implementation, the automatic operation of the method flow can be realized by using a program, and the implementation flow of the method provided by the embodiment comprises the following steps:
step 1, extracting boundary data based on the existing indoor map shapefile data, and additionally extracting road network data for a parking lot map containing road network information;
step 2, performing data gridding on the boundary data and the road network data according to different requirements;
the step 2 of gridding the data specifically comprises the following sub-steps:
step 2.1, gridding the boundary data, setting grid attributes of the region where the boundary is located as impassable, and setting other positions as passable, so as to avoid the problem of 'wall penetration' of the path planning result caused by the excessive sparsity of the grid, appropriately widening the grid, namely, appropriately widening the grid when assigning the grid attributes to the boundary region;
and 2.2, carrying out grid formation on the road network data, and carrying out grid formation in the mode of the step 2.1, wherein the road network is passable data and is opposite to the boundary data, so that when the grid attributes are assigned, the grid attributes of the region where the road network is located are set to be passable, and the grid attributes of the rest regions are set to be impassable.
In the concrete implementation, it is necessary to perform appropriate processing in consideration of relevant characteristics of the map, for example, data other than the boundary wall, and data such as "suspended area", "door", and "inaccessible area" that are partially inaccessible, and to make a determination according to actual conditions.
Step 3, path planning is carried out by adopting an A-star algorithm, and the path planning is optimized and smoothed;
the path planning in step 3 in the embodiment specifically includes the following substeps:
step 3.1, in order to adapt to the a × algorithm, firstly, the grid data needs to be calculated by adjacent grid data, and the information of the adjacent grid is stored in each grid data, so that the path searching and using are facilitated;
step 3.2, the Manhattan distance is used as a heuristic search factor, so that a terminal point can be found as soon as possible during path search, and the operation efficiency of the algorithm is improved;
step 3.3, optimizing the searched path result, and based on the idea of 'visibility', judging whether the connection line between the two points is 'visibility' from the two ends of the path, namely whether all grids in the area where the connection line between the two points is located can be visible, if so, filtering the rest nodes between the two points, and finally obtaining the optimal path planning result;
step 3.4, due to the particularity of the grid data, the finally obtained path result may have "jaggy", which is particularly easy to appear in a narrow road between two boundaries, and therefore, the path result needs to be smoothed, and a general smoothing method is easy to generate a "wall through" phenomenon, so that the problem needs to be considered in the smoothing process. The method adopts a Douglas simplification method to smooth the path, and considers that the boundary grid is widened by two times, so that the threshold value is set to be 1.5 times of the grid width, the smooth result can ensure that the wall is not penetrated and attached, and a more reasonable planned path can be obtained.
In specific implementation, besides the basic grid data model, the map data model also comprises related map information data and interlayer connection point data, which are used for performing cross-layer path planning, the interlayer connection points may include vehicle interlayer connection points and pedestrian interlayer connection points at the same time, the vehicle connection points need to be merged into the vehicle data model, the pedestrian interlayer connection points may include connection point modes such as 'elevator' and 'stair', and in order to provide multi-type selection, the connection points may also need to be classified and stored into the pedestrian data model; indoor and outdoor connection point data are used for realizing the integration of indoor and outdoor path planning, and meanwhile, the data are divided into vehicles and pedestrians to be merged according to categories. In addition to some of the above data information, some related grid information, such as the side length of the grid, the range of the data model, etc., needs to be stored. In the embodiment, a json format is adopted to organize and store the series of data, the data can be stored in a database, data reading and model construction are carried out when needed, data production and data application are separated, and efficiency can be effectively improved.
The protective scope of the present invention is not limited to the above-described embodiments, and it is apparent that various modifications and variations can be made to the present invention by those skilled in the art without departing from the scope and spirit of the present invention. It is intended that the present invention cover the modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents.
Claims (2)
1. A method for building an indoor navigation data model and planning a path under different behavior modes is characterized by comprising the following steps:
step 1, reading map data, constructing a map navigation data model, and constructing a map navigation database, so that separation of data processing construction nodes from an actual application stage is realized, and efficiency is improved;
step 2, reading navigation model data from an offline database, and constructing a navigation data model;
step 3, planning the path, obtaining a path planning result, and optimizing and smoothing the result;
the establishing of the data model in the step 1 specifically comprises the following processes:
step 1.1, data preprocessing, namely preprocessing parking lot map data with road network information to extract all connectable road network data; secondly, extracting boundary data of the parking lot map and a common indoor map;
step 1.2, carrying out grid formation on common data, wherein the boundary data of a common indoor map and the boundary data of a parking lot map are used as inaccessible paths, and grid formation is carried out to form grid map data based on regular hexagons;
step 1.3, grid-netting road network data, namely grid-netting the parking lot map road network data in the same way as in step 1.2, wherein due to the particularity of the map in the vehicle driving mode, only 'road network' is taken as a passage in the vehicle driving mode, so that all grid data need to be negated after grid-netting, namely the road network data are passable, and the rest are not passable;
step 1.4, constructing model data, and obtaining two pieces of model data for a parking lot map, wherein one piece of model data is based on data in a pedestrian mode, and the other piece of model data is suitable for data in a vehicle driving mode; the ordinary indoor map does not support driving, so only one piece of model data is obtained.
2. The method for indoor navigation data model construction and path planning in different behavior modes according to claim 1, wherein: the path planning in the step 3 specifically comprises the following processes:
step 3.1, in order to adapt to the a-x algorithm, firstly, the grid data needs to be calculated by adjacent grid data, and the adjacent grid information of each grid data is stored, so that the path searching and using are facilitated;
step 3.2, the Manhattan distance is used as a heuristic search factor, so that a terminal point can be found as soon as possible during path search, and the operation efficiency of the algorithm is improved;
step 3.3, optimizing the searched path result, and judging whether the connection line between the two points is in a 'through view' state from the two ends of the path based on the 'through view' idea, namely whether all grids in the area where the connection line between the two points is located can be in a through view, if so, the rest nodes between the two points can be filtered, and finally, the optimal path planning result is obtained;
and 3.4, smoothing the path result by adopting a Douglas simplification method.
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