CN107091642B - Indoor positioning method based on different-plane anchor node mapping and rasterization deviation rectification - Google Patents
Indoor positioning method based on different-plane anchor node mapping and rasterization deviation rectification Download PDFInfo
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Abstract
The invention discloses an indoor positioning method based on different-plane anchor node mapping and rasterization deviation rectification, which comprises the following three steps: (a) establishing an out-of-plane test model; (b) carrying out dimension reduction processing on the projection plane; (c) and adjusting and correcting the precision of the same plane. The method provided by the invention simplifies the problem of a large amount of communication overhead of the existing non-line-of-sight distance side measurement and positioning investment, improves the positioning accuracy of a moving object needing positioning in a multi-obstacle environment and improves the reliability of path planning.
Description
Technical Field
The invention belongs to a positioning technology for reducing positioning errors in an indoor environment, and particularly relates to an indoor positioning method based on different-plane anchor node mapping and rasterization deviation rectification.
Background
With the improvement of living standard and the progress of technology level, a plurality of intelligent household products continuously enter our lives, great convenience is brought to people, and the living quality of people is greatly improved. The service robot is a product generated in response to the pursuit of society and people for high-quality relaxed life, and in China and even all countries around the world, the family service robot has huge client groups and markets, has become an important development trend of the development of the robot industry, and is also a key direction of the development of the robot in China.
In the development process of a service-type intelligent robot, a general robot or other products of intelligent home service mobility are mainly applied indoors, so that the positioning and path planning technologies directly affect the intelligence and the high efficiency of services, and for the indoor positioning technologies, the existing technologies include seven positioning technologies, namely an infrared positioning technology, an ultrasonic indoor positioning technology, a Radio Frequency Identification (RFID) indoor positioning technology, a bluetooth indoor positioning technology, a Wi-Fi indoor positioning technology, a ZigBee indoor positioning technology and an ultra wide band indoor positioning technology. In the indoor mobile positioning aspect of robots and other intelligent products, the main solution is the location information and the planning of the route to the destination. Under the indoor condition, due to the occlusion of buildings and the multipath propagation effect, the positioning effect is not ideal, and therefore, the indoor positioning is also a hotspot problem in the research of positioning. The existing indoor positioning adopts two main types of positioning: ranging-based indoor positioning and non-ranging-free indoor positioning. The former needs to know the distance or angle information between beacon nodes, and the latter mainly depends on the adjacent relation and connectivity between nodes. The indoor positioning method and algorithm based on ranging mainly include positioning algorithms based on TOA, Time Difference of Arrival (TDOA), Angle of Arrival (AOA), RSSI value, and the like, and obtain the position of a position node according to the measured distance or Angle information between nodes. The following ranging algorithm models are commonly used: trilateration, triangulation, maximum likelihood estimation. The non-ranging-based positioning algorithm does not need to utilize information such as coordinates, distances and angles among nodes, and can perform initial positioning by generally utilizing network connectivity, so that low cost of indoor positioning is realized, but positioning errors are larger than ranging algorithms. Common non-ranging positioning algorithms are: centroid algorithm, DV-Hop (Distance Vector Routing, DV), convex programming algorithm, RSSI algorithm based on fingerprint, approximate triangle interior Point measurement Algorithm (APIT).
The working environment of the existing positioning research and the actual working environment of the home service robot have a certain difference, the particularity of the working environment of the home service robot is not considered, the influence of a large number of walls, doors and soft clothing in a family is received, the positioned position information often has a certain deviation from the actual position information, and the signal is also influenced by factors such as multiple diffraction, absorption and multipath in the transmission process. Due to the existence of the factors, the home robot cannot accurately acquire the position information of the home robot and the position information of the target point, so that whether the home service robot can continuously service across a plurality of sub-working areas is influenced. Therefore, a positioning method more suitable for the home service robot needs to be provided according to the particularity of the home environment.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems of high moving and positioning accuracy of the existing home service robot under the interference of a large number of walls and doors and high communication overhead, high cost and the like caused by high-accuracy positioning due to the adoption of distance measurement based indoor positioning, the invention provides an indoor positioning method based on out-of-plane anchor node mapping and rasterization deviation correction, and the method is suitable for recalculating positioning information of the robot in a home environment and improving the accuracy of a trilateral positioning algorithm in an indoor NLOS environment.
The technical scheme is as follows: the invention relates to an indoor positioning method based on different-plane anchor node mapping and rasterization deviation rectification, which comprises the following three steps:
(a) establishing an out-of-plane test model;
(b) carrying out dimension reduction processing on the projection plane;
(c) and adjusting the precision of the same plane, correcting the deviation and obtaining the coordinates of the to-be-positioned point.
Wherein, the building of the out-of-plane test model in the step (a) comprises environment map rasterization and indoor anchor node position arrangement: the environment map rasterizing is to vertically project indoor obstacle objects to the same plane to obtain an indoor plane map, then perform expansion processing on the position size of the obstacle mapped on the indoor plane map, then uniformly rasterize the indoor plane map to obtain a grid map, divide the grid map into an obstacle area, a free area and a dynamic area according to the obstacle mapping, and then sequentially number the grid map; the indoor anchor node is arranged by arranging the iBeacon beacon nodes on the top of a roof, respectively measuring the linear distance between the position where the locating point is located and each anchor node and the vertical height of each iBeacon beacon node, measuring the RSSI value when the locating point and the iBeacon are different in distance more than two times, fitting the distance curve between the RSSI and the locating point and the anchor nodes through Matlab, establishing an indoor attenuation model simulating a suitable family environment, and obtaining the distance relation between the RSSI and the locating point and the anchor nodes.
The obstacle area is a fixed object and comprises an indoor wall and a static object; the free area is a barrier-free area; the dynamic area comprises a door and a position area projected by the movable object.
The dimension reduction processing of the projection plane in the step (b) is to convert the obtained RSSI values of the positioning points and the anchor nodes into distance information through an indoor attenuation model, and project the distance to a two-dimensional plane through the Pythagorean theorem to obtain the horizontal straight line distance between the positioning points and the anchor nodes; and then obtaining the estimated position of the point to be positioned through a ranging algorithm and the distance information D _ actual.
The precision adjustment and deviation rectification of the same plane in the step (c) is to calculate the position information from the point to be located to each anchor node and D _ virtual according to the estimated position obtained in the step (b), and then establish the position information of the virtual anchor node according to the distance information of the D _ actual and the D _ virtual; if the distance D _ virtual between the estimated position to be positioned and each anchor node is smaller than the value of D _ actual measured by RSSI, the node is corrected backwards according to the difference information, and if the distance D _ virtual between the estimated position to be positioned and each anchor node is larger than the value of D _ actual measured by RSSI, the node is corrected forwards; if the distance D _ virtual between the estimated position of the point to be located and each anchor node is equal to the value of D _ actual measured by RSSI, the node does not need to rectify the deviation; and then, according to the position information of the virtual anchor node and the value of the D _ actual, performing the ranging algorithm again, and judging whether the undetermined point is in the effective grid area to meet the precision requirement. If yes, displaying the position information and the number of the located grid area. If not, the deviation rectifying method is carried out again. And finally, displaying the information position and the grid area number.
Has the advantages that: compared with the prior art, the method has the obvious advantages that the method is suitable for positioning and path planning of indoor robots and products needing positioning, can well avoid obstacles and improve positioning accuracy and accurate path planning through dimension reduction from different planes to the same plane, simplifies algorithm process, provides accurate positioning for the robots, and enables the path planning to be more reliable and effective.
Drawings
FIG. 1 is a flow chart of the general steps of the present invention;
FIG. 2 is a flow chart of the present invention for building an out-of-plane test model;
FIG. 3 is a schematic diagram of rasterization of an indoor plan map according to the present invention;
FIG. 4 is a schematic diagram of the indoor anchor node layout and distance measurement of the present invention;
FIG. 5 is an effect diagram of the iBeacon layout of the present invention on the ground with measuring point step lengths of 1m and 0.5m, respectively;
FIG. 6 is a diagram showing the effect of the invention when the iBeacon is laid out on the roof in step (a) and the step lengths of the measuring points are 1m and 0.5m, respectively;
FIG. 7 is a graph comparing the effects of the iBeacon layout of the present invention on the floor and roof;
FIG. 8 is a flow chart of the projection plane processing calculation of the present invention;
FIG. 9 is a schematic diagram of the measurement of the projected height of the anchor node and the distance from the point to be measured according to the present invention;
FIG. 10 is a flowchart of the co-planar precision adjustment error correction method of the present invention;
FIG. 11 is a schematic diagram of the method for correcting the position of the virtual node with a distance smaller than D _ actual measured by RSSI during the same plane precision adjustment correction according to the present invention;
FIG. 12 is a schematic diagram of the method for correcting the deviation of D _ actual according to the invention, wherein the distance between the virtual nodes of the points to be located in the same plane is greater than the distance measured by RSSI.
Detailed Description
For the purpose of illustrating the technical solutions disclosed in the present invention in detail, the following description is further provided with reference to the accompanying drawings and specific examples.
As shown in fig. 1, an indoor positioning method based on out-of-plane anchor node mapping and rasterization deviation rectification includes the following three steps:
(a) establishing an out-of-plane test model;
(b) carrying out dimensionality reduction treatment on a projection plane obtained by mapping the different planes;
(c) and (5) adjusting and rectifying the precision of the same plane.
As shown in fig. 2, in the step (a) of establishing the different plane test model, the different plane is firstly projected onto the plane to obtain an indoor plane map, then the indoor plane map is uniformly rasterized,and then arranging indoor anchor nodes, preferably adopting iBeacon as a beacon node, acquiring the electric field signal intensity of the to-be-positioned point, converting the electric field signal intensity into distance information, and finally establishing an out-of-plane test model. Preferably, the indoor work environment of the home service robot is converted into a grid map, and the obstacle is expanded while only considering static obstacles such as walls and partitions in the work environment. The state of each grid is divided into three cases: free area, obstacle area, dynamic area. The dynamic area is an important area connecting all sub-working areas, such as an area where a door is located. By rasterizing the home plan and numbering the regions, location information may be effectively obtained, which obtains the grid numbers and establishes the coordinate system of the projection plane, as shown in fig. 3, where
Which indicates the area of the obstacle,
the free area is represented by the number of lines,
representing a dynamic region. The connectivity of the dynamic zone has uncertainty, such as the zone where the door is located. Then, the iBeacon positions are arranged on the top of the house, preferably, the iBeacon is adopted as an anchor node and a virtual base station, as shown in fig. 4, the anchor nodes B1, B2 and B3 are arranged on the same horizontal plane of the indoor roof where the home service robot works, the anchor nodes B1, B2 and B3 are vertically projected onto the same plane where the grid map is located, so that the vertical heights h of the projection points B1 ', B2', B3 ', the anchor nodes B1, B2 and B3 to the anchor node projection points B1', B2 'and B3' are obtained, the RSSI values at different distances from the iBeacon are respectively collected, the straight-line distances D1, D2 and D3 between the anchor nodes and the point P to be located are calculated according to the RSSI locating algorithm, the curves of the RSSI and the distances are fitted through Matlab, so that an indoor attenuation model suitable for the home environment is simulated, and the relationship between the RSSI and the distance D is obtained. As shown in FIG. 5, the anchor nodes are arranged on the same horizontal position of the indoor ground, and Matlab fitting is carried out when the measuring point step lengths are respectively 1m and 0.5mAnd (5) obtaining the effect graph of the distance from the RSSI to the iBeacon and the measured RSSI signal value. As shown in fig. 6, anchor nodes are laid out on the same horizontal position of the indoor roof, and an effect graph of the distance from RSSI to iBeacon and the measured RSSI signal value is fitted by Matlab when the measuring point step lengths are 1m and 0.5m, respectively.
As shown in FIG. 7, the effect of arranging anchor nodes on the ground and the roof is compared with the effect of arranging anchor nodes on the roof, and as can be seen from the graph, the method of arranging the iBeacon on the roof is preferably adopted.
As shown in fig. 8, in the dimension reduction processing of the projection plane obtained by mapping the different plane in step (b), the first step is to vertically project the anchor node onto the ground, the second step is to obtain the distance D corresponding to the RSSI of the point P to be located at this time through the different plane test model, the third step is to obtain the actual distance D _ actual through the distance D in a vertical projection manner, and the fourth step is to obtain the position information of the point P to be located through the ranging algorithm. In the step (B), distances are measured as shown in fig. 9, anchor nodes B1, B2 and B3 are arranged indoors, anchor nodes B1, B2 and B3 are projected onto the ground to obtain projection points B1 ', B2' and B3 ', distances D1, D2 and D3 from the anchor nodes to the point P to be positioned are obtained through the established different plane test model and the distance conversion of RSSI signal values, then distances D1, D2 and D3 are projected onto a grid map, h in fig. 9 represents the height of the anchor node iBeacon from the projection plane, and distances D1', D2 'and D3' are obtained through projection of distances D1, D2 and D3 onto the same plane, respectively, and distance values of D _ actual1, D _ actual2 and D _ actual3 are calculated according to a trilateration algorithm. The specific algorithm is as follows:
wherein: the height h is the vertical height from the roof to the point to be measured, D _ actuaL is the calculated linear distance between the point to be positioned P and the projection point of the anchor node, D is the RSSI value conversion distance of the point to be positioned P, and h is the height between the anchor node and the projection plane.
Wherein (x)i,yi) Is the location information of the anchor node, and (x, y) is the location information of the anchor point.
As shown in fig. 10, the in-plane precision adjustment and correction in step (c) is a method of calculating the positioning point information three times when the positioning point information calculated by the ranging algorithm is not ideal. Firstly, judging whether the positioning point is ideal or not according to whether the position of the to-be-positioned point and the actual position fall in the same grid area or not, stopping calculation if the positioning point is in the same grid area, and displaying the positioning information and the grid area at the moment. The concrete implementation is as follows:
and calculating the distance D _ virtual from the positioning point to each anchor node according to the positioning result. And establishing the position information of the virtual anchor node according to the distance information of the D _ virtual and the D _ actual. And if the distance D _ virtual between the positioning estimation position and each anchor node is smaller than the value of D _ actual measured by RSSI, correcting the node backwards according to the difference information. Otherwise, forward rectifying the node. And (4) performing the ranging algorithm again according to the position information of the virtual anchor node and the value of the D _ actual, and judging whether the point P to be located is in the effective grid area or not to meet the precision requirement. If yes, displaying the position information and the number of the located grid area. If not, the rectification is continued.
And calculating the distance of the D _ virtual at the moment by using an anchor node information position, an information position of the point P to be positioned and a distance formula between two points. And the position information of the virtual anchor node is calculated according to the distance between the D _ virtual and the D _ actual. If the value of the distance D _ virtual of the position estimated by the point P to be located from each anchor node is smaller than the value of D _ actual measured via RSSI, the implementation is as shown in fig. 11. The calculation method of the virtual base station is as follows:
and
otherwise, if the value of the distance D _ virtual between the position estimated by the point P to be located and each anchor node is greater than the value of D _ actual measured by RSSI, the virtual base station is calculated as follows, as shown in fig. 12:
and
wherein, (x ', y') is the position information of the virtual anchor node, (x, y) is the preliminarily calculated positioning information, (xi,yi) Is the information of the original anchor node.
The D _ virtual is obtained by using the calculated rough position (x, y) of the positioning point P and anchor nodes B1(x1, y1), B2(x2, y2), B3(x3, y3) as a straight line formula between two points, D _ virtual1, D _ virtual2, D _ virtual3, and D _ virtual are used in the rectification, and it is determined whether the rectification is inward or outward.
And recalculating the position information of the positioning node according to the position information of the virtual node acquired in the step. And obtaining a correction result of the preliminary positioning estimation. The same process can be repeated, and the number of iterations is determined according to the grid position and the requirement on positioning accuracy. Through continuous iteration, errors caused by non-line-of-sight, multipath and the like due to inaccurate RSSI measurement are counteracted, and therefore the purpose of improving the positioning accuracy is achieved. And finally, displaying the information position and the grid area number to obtain accurate positioning with positioning points.
Claims (4)
1. An indoor positioning method based on different-plane anchor node mapping and rasterization deviation rectification is characterized by comprising the following steps: the method comprises the following three steps:
(a) establishing an out-of-plane test model, including environment map rasterization and indoor anchor node position arrangement;
(b) performing dimension reduction processing on the projection plane, converting the obtained RSSI value of the positioning point and the anchor node into distance information through an indoor attenuation model, projecting the distance to a biplane through the pythagorean theorem to obtain the horizontal straight line distance between the positioning point and the anchor node, and then obtaining the estimated position of the point to be positioned through a distance measurement algorithm and distance information D _ actual;
(c) adjusting the precision of the same plane, rectifying deviation, obtaining the coordinates of the to-be-positioned point, calculating the position information and D _ virtual from the to-be-positioned point to each anchor node according to the estimated position obtained in the step (b), and then establishing the position information of the virtual anchor node according to the distance information of the D _ actual and the D _ virtual; if the distance D _ virtual between the estimated position to be positioned and each anchor node is smaller than the value of D _ actual measured by RSSI, the node is corrected backwards according to the difference information, and if the distance D _ virtual between the estimated position to be positioned and each anchor node is larger than the value of D _ actual measured by RSSI, the node is corrected forwards; if the distance D _ virtual between the estimated position of the point to be located and each anchor node is equal to the value of D _ actual measured by RSSI, the node does not need to rectify the deviation; then, according to the position information of the virtual anchor node and the value of the D _ actual, performing the ranging algorithm again, and judging whether the undetermined point is in an effective grid area to meet the precision requirement; if yes, displaying the position information and the number of the grid area where the position information and the number of the grid area are located, if not, re-performing the deviation rectifying method, and finally displaying the information position and the number of the grid area;
the forward node deviation correction and the backward node deviation correction are as follows:
if the value of the distance D _ virtual between the position estimated by the point P to be located and each anchor node is smaller than the value of D _ actual measured by RSSI, the virtual base station is calculated as follows:
and
if the value of the distance D _ virtual between the position estimated by the point P to be located and each anchor node is greater than the value of D _ actual measured by RSSI, the virtual base station is calculated as follows:
and
wherein, (x ', y') is the position information of the virtual anchor node, (x, y) is the preliminarily calculated positioning information, (xi,yi) Is the information of the original anchor node.
2. The indoor positioning method based on the out-of-plane anchor node mapping and the rasterization deviation rectification as recited in claim 1, wherein: the environment map rasterizing is to vertically project indoor obstacle objects to the same plane to obtain an indoor plane map, then perform expansion processing on the position size of the obstacle mapped on the indoor plane map, then uniformly rasterize the indoor plane map to obtain a grid map, divide the grid map into an obstacle area, a free area and a dynamic area according to the obstacle mapping, and then sequentially number the grid map.
3. The indoor positioning method based on the out-of-plane anchor node mapping and the rasterization deviation rectification as recited in claim 1, wherein: the indoor anchor node is arranged by arranging the iBeacon beacon nodes on the top of a roof, respectively measuring the linear distance between the position where the locating point is located and each anchor node and the vertical height of each iBeacon beacon node, measuring the RSSI value when the locating point and the iBeacon are different in distance more than two times, fitting the distance curve between the RSSI and the locating point and the anchor nodes through Matlab, establishing an indoor attenuation model simulating a suitable family environment, and obtaining the distance relation between the RSSI and the locating point and the anchor nodes.
4. The indoor positioning method based on the out-of-plane anchor node mapping and the rasterization deviation rectification as recited in claim 2, wherein: the obstacle area is a fixed object and comprises an indoor wall and a static object; the free area is a barrier-free area; the dynamic area comprises a door and a position area projected by the movable object.
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