阅读说明：本技术 一种林区点云快速配准方法 (Forest region point cloud rapid registration method ) 是由 王迪 全英汇 徐楷杰 别博文 肖国尧 于 2021-07-26 设计创作，主要内容包括：本发明涉及一种林区点云快速配准方法,包括：步骤1：获取多视角下的每个单站的原始点云数据；步骤2：根据原始点云数据,提取对应的特征点；步骤3：根据特征点,构建Delaunay三角网,计算得到特征点的特征描述向量；步骤4：根据特征描述向量,计算得到不同单站之间对应的相似度矩阵；步骤5：根据相似度矩阵,采用线性分配算法,构建分配总成本TC,使分配总成本TC最小以获得不同单站之间的最终匹配特征点对,根据最终匹配特征点对得到粗配准结果；步骤6：对粗配准结果进行精配准,得到精配准结果。本发明的林区点云快速配准方法,避免了穷竭搜索,极大地提升了计算效率和林区点云配准的效率。(The invention relates to a forest region point cloud rapid registration method, which comprises the following steps: step 1: acquiring original point cloud data of each single station under multiple viewing angles; step 2: extracting corresponding characteristic points according to the original point cloud data; and step 3: constructing a Delaunay triangulation network according to the feature points, and calculating to obtain feature description vectors of the feature points; and 4, step 4: calculating to obtain corresponding similarity matrixes among different single stations according to the feature description vectors; and 5: according to the similarity matrix, a linear distribution algorithm is adopted to construct a distribution total cost TC, the distribution total cost TC is minimized to obtain a final matching characteristic point pair among different single stations, and a coarse registration result is obtained according to the final matching characteristic point pair; step 6: and carrying out fine registration on the coarse registration result to obtain a fine registration result. The forest region point cloud rapid registration method avoids exhaustive search, and greatly improves the calculation efficiency and the forest region point cloud registration efficiency.)

1. A forest region point cloud rapid registration method is characterized by comprising the following steps:

step 1: acquiring original point cloud data of each single station under multiple viewing angles;

step 2: extracting corresponding characteristic points according to the original point cloud data;

and step 3: constructing a Delaunay triangulation network according to the feature points, and calculating to obtain feature description vectors of the feature points;

and 4, step 4: calculating to obtain corresponding similarity matrixes among different single stations according to the feature description vectors;

and 5: according to the similarity matrix, a linear distribution algorithm is adopted to construct a distribution total cost TC, the distribution total cost TC is minimized to obtain a final matching characteristic point pair among different single stations, and a coarse registration result is obtained according to the final matching characteristic point pair;

step 6: and carrying out fine registration on the coarse registration result to obtain a fine registration result.

2. The forest area point cloud fast registration method according to claim 1, wherein in the step 2, the feature points are tree positions.

3. The forest area point cloud rapid registration method according to claim 1, wherein the step 3 comprises:

step 31: constructing a Delaunay triangulation network according to the characteristic points;

step 32: according to the Delaunay triangulation, calculating the distance between each feature point and the first-stage adjacent feature point thereof and the distance between the second-stage adjacent feature points thereof;

step 33: and the distance between the characteristic point and the first-stage adjacent characteristic point thereof and the distance between the characteristic point and the second-stage adjacent characteristic point thereof form a characteristic description vector F corresponding to the characteristic point.

4. The forest area point cloud rapid registration method of claim 3,

the first-level adjacent feature points are feature points which are directly connected with the feature points in the Delaunay triangulation;

the secondary adjacent feature points are feature points in the Delaunay triangulation network, which are directly connected with the primary adjacent feature points of the feature points.

5. The forest area point cloud rapid registration method according to claim 1, wherein the step 4 comprises:

according to said characteristicsCalculating the similarity SM of each feature point in two different single stations according to the vector_i,jAccording to the similarity SM_i,jAnd constructing to obtain the similarity matrix,

SM_i,j＝F_i∩F_j/min(‖F_i‖,‖F_j‖)，

wherein, F_iFeature description vectors F, F corresponding to the ith feature point in the raw point cloud data S1 representing the first single station_jThe feature description vector F, | F corresponding to the jth feature point in the raw point cloud data S2 representing the second single station_iII denotes a feature description vector F_iLength of | F_jII denotes a feature description vector F_jLength of (d).

6. The forest area point cloud rapid registration method according to claim 5, wherein the step 5 comprises:

step 51: obtaining a Cost matrix Cost according to the similarity matrix,

wherein, the value Cost of ith row and jth column in the Cost matrix Cost_i,jIndicating the Cost, of assigning the ith row to the jth column_i,j＝1-SM_i,j；

Step 52: from said Cost matrix Cost, a total Cost of distribution TC is constructed,

wherein M represents the total number of rows of the cost matrix, n represents the total number of columns of the cost matrix, p represents the total number of rows and columns that have been matched, M + n-2p represents the total number of rows and columns that have failed to match, and M (i,1) and M (i,2) represent a set of matched rows and columns, respectively;

step 5.3: solving to minimize the total distribution cost TC so as to obtain a plurality of corresponding matching characteristic point pairs;

step 5.4: removing error matching point pairs in the matching feature point pairs by using a random sampling maximum likelihood estimation algorithm to obtain final matching feature point pairs;

step 5.5: and obtaining a space transformation matrix Q according to the final matching characteristic point pair, and carrying out primary registration according to the space transformation matrix Q to obtain a coarse registration result.

7. The forest area point cloud rapid registration method according to claim 1, wherein the step 6 comprises: and performing secondary registration on the coarse registration result by using a closest point iterative algorithm to obtain a fine registration result.

Technical Field

The invention belongs to the field of forest structure measurement, and particularly relates to a forest region point cloud rapid registration method.

Background

A LiDAR (Light Detection And Ranging) is an active remote sensing technology, which obtains three-dimensional geometric information And radiation characteristics of a target object by measuring propagation time, energy, spectrum amplitude, phase information change And the like of a laser signal, wherein the laser signal is emitted by a sensor, reflected by the target object And then received by the sensor. The data acquired by the lidar is a three-dimensional spatially discrete set of points, called a point cloud, which has unique advantages in characterizing the three-dimensional structural information of the study object.

Forests play an important role in the terrestrial ecosystem and in human productive life. The accurate forest structure measurement has important significance for forest management and management, forest carbon sink estimation and dynamic change monitoring thereof, and even global climate change research. Common parameters used to quantitatively describe forest structures include tree position, breast height, tree species, etc. The laser radar point cloud can rapidly, accurately and nondestructively estimate the parameters due to the unique three-dimensional structure measurement capability, so that the laser radar point cloud is widely applied to the field of forest structure measurement in recent years. Due to the fact that laser cannot penetrate objects and the forest environment is complex in shielding condition, the laser radar point cloud data acquired from a single visual angle cannot meet the requirement for accurate forest structure parameter measurement, and the point cloud data of the laser radar acquired from multiple visual angles need to be registered to acquire complete information under the same coordinate frame.

The point cloud registration is generally divided into a coarse registration part and a fine registration part, wherein the coarse registration is to perform preliminary alignment on the point cloud with a large distance and a large rotation distance, and the fine registration is to perform precise alignment on the preliminarily aligned point cloud. At present, the registration research of forest region point clouds mainly focuses on a coarse registration part. The forest point cloud has the characteristics of high structural complexity, tree self-similarity, lack of obvious geometric feature points and the like, and common point cloud feature points such as contour points, straight line points, high curvature points and the like cannot be directly applied. The most common forest area point cloud rough registration method at present uses the position information of trees as feature points, and then calculates a transformation matrix between the positions of the trees in different point clouds by using Random Consensus (Random Sampling RANSAC) and the like. However, the method has the problem of high computational complexity, and the solution of three-dimensional space transformation needs at least three homonymous points, and the computational complexity of the method increases with the number of trees to the third power.

Disclosure of Invention

In order to solve the problems in the prior art, the invention provides a forest region point cloud rapid registration method. The technical problem to be solved by the invention is realized by the following technical scheme:

the invention provides a forest region point cloud rapid registration method, which comprises the following steps:

step 1: acquiring original point cloud data of each single station under multiple viewing angles;

step 2: extracting corresponding characteristic points according to the original point cloud data;

and step 3: constructing a Delaunay triangulation network according to the feature points, and calculating to obtain feature description vectors of the feature points;

and 4, step 4: calculating to obtain corresponding similarity matrixes among different single stations according to the feature description vectors;

and 5: according to the similarity matrix, a linear distribution algorithm is adopted to construct a distribution total cost TC, the distribution total cost TC is minimized to obtain a final matching characteristic point pair among different single stations, and a coarse registration result is obtained according to the final matching characteristic point pair;

step 6: and carrying out fine registration on the coarse registration result to obtain a fine registration result.

In one embodiment of the present invention, in the step 2, the feature point is a tree position.

In one embodiment of the present invention, the step 3 comprises:

step 31: constructing a Delaunay triangulation network according to the characteristic points;

step 32: according to the Delaunay triangulation, calculating the distance between each feature point and the first-stage adjacent feature point thereof and the distance between the second-stage adjacent feature points thereof;

step 33: and the distance between the characteristic point and the first-stage adjacent characteristic point thereof and the distance between the characteristic point and the second-stage adjacent characteristic point thereof form a characteristic description vector F corresponding to the characteristic point.

In one embodiment of the present invention, the first-level neighboring feature points are feature points in the Delaunay triangulation network directly connected to the feature points;

the secondary adjacent feature points are feature points in the Delaunay triangulation network, which are directly connected with the primary adjacent feature points of the feature points.

In one embodiment of the present invention, the step 4 comprises:

according to the feature description vector, calculating the similarity SM of each feature point in two different single stations_i,jAccording to the similarity SM_i,jAnd constructing to obtain the similarity matrix,

SM_i,j＝F_i∩F_j/min(‖F_i‖,‖F_j‖)，

wherein, F_iFeature description vectors F, F corresponding to the ith feature point in the raw point cloud data S1 representing the first single station_jThe feature description vector F, | F corresponding to the jth feature point in the raw point cloud data S2 representing the second single station_iII denotes a feature description vector F_iLength of | F_jII denotes a feature description vector F_jLength of (d).

In one embodiment of the present invention, the step 5 comprises:

step 51: obtaining a Cost matrix Cost according to the similarity matrix,

wherein, the value Cost of ith row and jth column in the Cost matrix Cost_i,jIndicating the Cost, of assigning the ith row to the jth column_i,j＝1-SM_i,j；

Step 52: from said Cost matrix Cost, a total Cost of distribution TC is constructed,

wherein M represents the total number of rows of the cost matrix, n represents the total number of columns of the cost matrix, p represents the total number of rows and columns that have been matched, M + n-2p represents the total number of rows and columns that have failed to match, and M (i,1) and M (i,2) represent a set of matched rows and columns, respectively;

step 5.3: solving to minimize the total distribution cost TC so as to obtain a plurality of corresponding matching characteristic point pairs;

step 5.4: removing error matching point pairs in the matching feature point pairs by using a random sampling maximum likelihood estimation algorithm to obtain final matching feature point pairs;

step 5.5: and obtaining a space transformation matrix Q according to the final matching characteristic point pair, and carrying out primary registration according to the space transformation matrix Q to obtain a coarse registration result.

In one embodiment of the present invention, the step 6 comprises: and performing secondary registration on the coarse registration result by using a closest point iterative algorithm to obtain a fine registration result.

Compared with the prior art, the invention has the beneficial effects that:

according to the rapid forest area point cloud registration method, the corresponding similarity matrixes among different single stations are obtained through calculation, the total distribution cost TC is constructed according to the similarity matrixes by adopting a linear distribution algorithm, the maximum subset matching is obtained by solving a linear distribution problem, all possible matching points tested by an exhaustion method are avoided, the calculation complexity is reduced to be linear, exhaustive search is avoided, and the calculation efficiency and the forest area point cloud registration efficiency are greatly improved.

The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented in accordance with the content of the description, and in order to make the above and other objects, features, and advantages of the present invention more clearly understood, the following preferred embodiments are described in detail with reference to the accompanying drawings.

Drawings

Fig. 1 is a schematic diagram of a fast registration method for forest point clouds according to an embodiment of the present invention;

fig. 2 is a block flow diagram of a fast registration method for forest point clouds according to an embodiment of the present invention;

fig. 3 is a schematic diagram of a Delaunay triangulation network and first-level and second-level neighboring feature points according to an embodiment of the present invention;

fig. 4 is a matching feature point pair obtained by solving using a linear distribution algorithm according to an embodiment of the present invention;

FIG. 5 is a final matched feature point pair optimized by a random sample maximum likelihood estimation algorithm according to an embodiment of the present invention;

fig. 6 is a schematic diagram of a coarse registration result provided by the embodiment of the present invention.

Detailed Description

In order to further explain the technical means and effects of the present invention adopted to achieve the predetermined invention purpose, the following will explain in detail a fast registration method of forest point clouds according to the present invention with reference to the accompanying drawings and the detailed implementation.

The foregoing and other technical matters, features and effects of the present invention will be apparent from the following detailed description of the embodiments, which is to be read in connection with the accompanying drawings. The technical means and effects of the present invention adopted to achieve the predetermined purpose can be more deeply and specifically understood through the description of the specific embodiments, however, the attached drawings are provided for reference and description only and are not used for limiting the technical scheme of the present invention.

Example one

Referring to fig. 1 and fig. 2 in combination, fig. 1 is a schematic diagram of a fast registration method for forest point clouds according to an embodiment of the present invention; fig. 2 is a block flow diagram of a fast registration method for forest point clouds according to an embodiment of the present invention. As shown in the figure, the fast registration method for forest area point clouds in the embodiment includes:

step 1: acquiring original point cloud data of each single station under multiple viewing angles;

due to the fact that laser cannot penetrate objects and the forest environment is complex in shielding condition, point cloud data of the laser radar acquired from a single visual angle cannot meet the requirement for accurate forest structure parameter measurement, therefore, the point cloud data of the laser radar need to be acquired from multiple visual angles, and then the point cloud data are registered to obtain complete information under the same coordinate frame.

In this embodiment, the raw point cloud data for each single station is the point cloud data of the lidar acquired from a single perspective.

Step 2: extracting corresponding characteristic points according to the original point cloud data;

in this embodiment, the feature points are tree positions, that is, each single-tree position in the forest region is taken as a feature point in the original point cloud data.

It should be noted that, optionally, the tree position may be extracted from the original point cloud data by methods such as clustering and circle detection, in this embodiment, no limitation is imposed on a specific algorithm for extracting the tree position, and any single-tree position sensing algorithm is applicable.

And step 3: constructing a Delaunay triangulation network according to the feature points, and calculating to obtain feature description vectors of the feature points;

in this embodiment, to avoid exhaustive search, first, features of feature points are calculated, spatial attributes of the feature points are described, and relative spatial distribution between the feature points is used as feature description.

Specifically, step 3 includes:

step 31: constructing a Delaunay triangulation network according to the characteristic points;

step 32: calculating the distance between each feature point and the first-stage adjacent feature point thereof and the distance between the second-stage adjacent feature points thereof according to the Delaunay triangulation;

referring to fig. 3, fig. 3 is a schematic diagram of a Delaunay triangulation network and first-level and second-level neighboring feature points according to an embodiment of the present invention. As shown in the figure, in this embodiment, the first-level neighboring feature points of the feature point are feature points in the Delaunay triangulation directly connected to the feature point; the second-level adjacent feature points of the feature points are feature points which are directly connected with the first-level adjacent feature points of the feature points in the Delaunay triangulation.

Step 33: and the distance between the characteristic point and the first-stage adjacent characteristic point thereof and the distance between the characteristic point and the second-stage adjacent characteristic point thereof form a characteristic description vector F corresponding to the characteristic point.

In this embodiment, the feature description vector F is a vector with a one-dimensional length of n, and represents a planar position of a certain tree position (i.e., a feature point) from its first-order neighbor tree and second-order neighbor trees, where the total number of the first-order neighbor trees and the second-order neighbor trees at the tree position is n.

And 4, step 4: calculating to obtain corresponding similarity matrixes among different single stations according to the feature description vectors;

taking the calculation of the similarity between each feature point in the original point cloud data S1 of the first single station and each feature point in the original point cloud data S2 of the second single station as an example, the following is specifically described:

specifically, step 4 includes:

according to the feature description vector, calculating the similarity SM of each feature point in two different single stations_i,jAccording to the similarity SM_i,jAnd constructing a similarity matrix, wherein,

SM_i,j＝F_i∩F_j/min(‖F_i‖,‖F_j‖) (1)，

wherein, F_iFeature description vectors F, F corresponding to the ith feature point in the raw point cloud data S1 representing the first single station_jThe feature description vector F, | F corresponding to the jth feature point in the raw point cloud data S2 representing the second single station_iII denotes a feature description vector F_iLength of | F_jII denotes a feature description vector F_jLength of (d).

In the present embodiment, according to the similarity SM_i,jConstructing a similarity matrix with the size of N_s1×N_s2Wherein N is_s1The number of feature points, N, in the original point cloud data S1 representing the first single station_s2The number of feature points in the raw point cloud data S2 of the second single station is represented.

It should be noted that, in the actual calculation, since there is an error in the calculation of the feature point (i.e., the position of the tree), the feature description vectors intersect (i.e., F is calculated) in the calculation_i∩F_j) When there is a deviation e_FIn this embodimentIn the example, the deviation e_FSet to 0.2 m, i.e. assume that the error magnitude of the tree position estimation is 0.2 m.

And 5: according to the similarity matrix, a linear distribution algorithm is adopted to construct a distribution total cost TC, the distribution total cost TC is minimized to obtain a final matching characteristic point pair among different single stations, and a coarse registration result is obtained according to the final matching characteristic point pair;

among them, the linear allocation algorithm is a method of allocating matrix rows to columns, requiring that each row is allocated to one column, and the total cost of allocation is minimized or maximized.

Specifically, step 5 comprises:

step 51: obtaining a Cost matrix Cost according to the similarity matrix,

wherein the value Cost of ith row and jth column in the Cost matrix_i,jIndicating the Cost, of assigning the ith row to the jth column_i,j＝1-SM_i,j；

In this embodiment, the Cost of the Cost matrix Cost is the non-similarity, and an unallocated Cost is allocated to each unmatched row or column, for example, if a certain row and column is unmatched, the total Cost is increased by 2 CostUnmatched.

Step 52: from the Cost matrix Cost, a total Cost of allocation TC is constructed,

where M represents the total number of rows of the cost matrix, n represents the total number of columns of the cost matrix, p represents the total number of rows and columns that have been matched, M + n-2p represents the total number of rows and columns that have failed to match, and M (i,1) and M (i,2) represent a set of matched rows and columns, respectively.

In the present embodiment, the size of M is p × 2.

In this embodiment, the Cost matrix Cost is the non-similarity between feature points, and CostUnmatched is set to 0.25, which means that if the non-similarity between two feature points in two different single stations is greater than 50%, matching should not be performed, because the system only increases the Cost of 2CostUnmatched when the non-matching is performed, which is lower than the Cost after the two matching.

Step 5.3: solving to minimize the total distribution cost TC so as to obtain a plurality of corresponding matching characteristic point pairs;

in this embodiment, a series of matching feature point pairs M are obtained by solving through a linear distribution algorithm_i,jThe method avoids testing all possible matching points by an exhaustion method, reduces the calculation complexity to be linear and greatly improves the calculation efficiency.

Step 5.4: removing wrong matching point pairs in the matching feature point pairs by using a random sampling maximum likelihood estimation algorithm to obtain final matching feature point pairs;

further, the matching characteristic point pair M obtained by solving based on the linear distribution algorithm_i,jAnd further removing a series of wrong matching feature point pairs in the matching feature point pairs by using a random sampling maximum likelihood estimation (MSAC) algorithm to obtain a final matching feature point pair. The specific algorithm is similar to the conventional MSAC algorithm, and is not described herein again.

The specific steps of the random sampling maximum likelihood estimation algorithm are not described in detail herein.

Step 5.5: and obtaining a space transformation matrix Q according to the final matching characteristic point pair, and carrying out primary registration according to the space transformation matrix Q to obtain a coarse registration result.

In this embodiment, a spatial transformation matrix Q is obtained according to the final matching feature point pair, so as to achieve the purpose of plane registration.

The spatial transformation matrix Q consists of a translation transformation matrix T and a rotation matrix R, and the matrix form is as follows:

wherein, t_xAnd t_xRepresenting the translation in the X and Y directions, respectively, and theta represents the rotation angle around the origin.

At least two pairs of matched characteristic points are needed for solving the transformation matrix Q, the MSAC algorithm randomly selects two pairs of matched characteristic points in each iteration, the transformation matrix is solved, the matching degree of the original point cloud data of two single stations after transformation, namely the number of matched characteristic points, is tested, and therefore the optimal solution is selected through multiple iterations.

And then, aiming at the Z-axis translation transformation, respectively calculating Z-value median values of respective coordinate systems of the matched feature point pairs, so that the median values are aligned to obtain a coarse registration result.

In the present embodiment, step 5 is how to find a corresponding point pair between the original point cloud data S1 of the first single station and the original point cloud data S2 of the second single station, thereby calculating a transformation relationship. Two pairs of corresponding points are needed to solve the planar transformation, and three pairs of corresponding points are needed to solve the three-dimensional transformation. The traditional method is exhaustive in testing all possible point pairs, and the calculation amount is huge. In the embodiment, the linear distribution algorithm is adopted to quickly solve the two-dimensional plane transformation on the basis of the similarity matrix, and then the Z-axis translation transformation is independently solved, so that the purpose of roughly registering two-station three-dimensional point clouds is achieved, and a rough registration result is obtained.

Step 6: and carrying out fine registration on the coarse registration result to obtain a fine registration result.

Specifically, the coarse registration result is subjected to secondary registration by using a closest point iterative algorithm to obtain a fine registration result.

It should be noted that the multi-station Point cloud after the coarse registration is only preliminary alignment, and an Iterative Closest Point (ICP) algorithm needs to be further adopted for fine registration.

ICP is the most classical algorithm of point cloud precise registration, and the basic idea is that the closest point pair between two stations of data is adopted in each iteration process to obtain a space transformation matrix, and the two stations of data gradually approach after iteration to finally achieve convergence. Unlike the feature point pairs in step 5, the point pairs are only the spatially closest points, so the ICP algorithm requires that the two-site cloud data have been preliminarily registered, mostly for fine registration.

Similar to the two-dimensional transformation matrix in the step 5, the ICP solves the three-dimensional transformation matrix, including translation transformation:

rotating and transforming around an X axis:

rotating and transforming around the Y axis:

and (3) rotating and converting around the Z axis:

the specific algorithm is similar to the existing ICP algorithm and is not described herein again.

According to the fast forest area point cloud registration method, the corresponding similarity matrixes among different single stations are obtained through calculation, the total distribution cost TC is constructed according to the similarity matrixes by adopting a linear distribution algorithm, the problem of solving the linear distribution problem is utilized to obtain the maximum subset matching, the test of all possible matching points by an exhaustion method is avoided, the calculation complexity is reduced to be linear, the exhaustive search is avoided, and the calculation efficiency and the forest area point cloud registration efficiency are greatly improved.

Example two

In this embodiment, experimental verification is performed on the registration effect of the fast forest area point cloud registration method in the first embodiment.

Specifically, forest point cloud data acquired by a Faro foundation laser radar scanner is used for testing. Please refer to fig. 4-6 in combination, fig. 4 is a diagram illustrating a pair of matching feature points obtained by solving using a linear distribution algorithm according to an embodiment of the present invention; FIG. 5 is a final matched feature point pair optimized by a random sample maximum likelihood estimation algorithm according to an embodiment of the present invention; fig. 6 is a schematic diagram of the coarse registration result provided by the embodiment of the present invention (in the diagram, solid dots represent feature points in the raw point cloud data S1 of the first single station, and open circles represent feature points in the raw point cloud data S2 of the second single station). According to the quantitative test result, the rotation error is 0.03 degrees after the coarse registration, the X-axis translation error is 0.34 meters, the Y-axis translation error is 0.07 meters, and the Z-axis translation error is 0.06 meters; after ICP fine registration, the rotation error is 0.12 degrees, the X-axis translation error is 0.02 meters, the Y-axis translation error is 0.05 meters, and the Z-axis translation error is 0.04 meters.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that an article or device that comprises a list of elements does not include only those elements but may include other elements not expressly listed. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of additional like elements in the article or device comprising the element.

The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.