CN104143194B - A kind of point cloud segmentation method and device - Google Patents

Abstract

The invention discloses a kind of point cloud segmentation method and device, it is related to three-dimensional reconstruction field, the described method comprises the following steps：S1：Tested scene is scanned by column by depth transducer, to obtain the depth information of the tested scene, the depth information of the tested scene Coordinate Conversion is subjected to, to obtain three-dimensional information of the tested scene under local coordinate；S2：Ground point cloud is partitioned into from the three-dimensional information；S3：Cluster segmentation is carried out to non-ground points cloud by the cluster segmentation mode of adaptive threshold, the non-ground points cloud is other clouds in the three-dimensional information in addition to the ground point cloud.The present invention carries out cluster segmentation by the cluster segmentation mode of adaptive threshold to non-ground points cloud, effectively prevent the less divided and over-segmentation problem carried out using fixed threshold to non-ground points cloud caused by cluster segmentation.

Description

A kind of point cloud segmentation method and device

Technical field

The present invention relates to three-dimensional reconstruction field, more particularly to a kind of point cloud segmentation method and device.

Background technology

For the three-dimensional reconstruction of large scene, due to its weight in terms of three-dimensional city map, road upkeep, urban planning Apply, receive very big concern.Fixed station is based on using depth transducer and position and attitude sensor or mobile platform is gathered The three-dimensional information of surrounding environment, is widely adopted due to its efficient, real-time, high-precision characteristic.Due to the scene bag of scanning Containing different types of object, such as ground, building, trees, vehicle, it is necessary to pass through a cloud before three-dimensional reconstruction is carried out Segmentation is separated from one another by the corresponding cloud data of different types of object, to carry out a cloud modeling respectively to each object.

Current most of point cloud segmentation methods are that unordered, discrete point cloud is handled, without using data The order that depth transducer is scanned successively by column in gatherer process.In point cloud segmentation method, cluster segmentation method especially base In the cluster segmentation method of distance, due to its, method complexity is low, be easily achieved, and is most commonly used to the segmentation of spatial point cloud.But Due to the presence of ground point cloud in outdoor large scene, and be used alone the clustering method based on distance be difficult to effectively ground-to-ground face and Non- ground object is split, and this method is generally combined with other methods, first extracts the point cloud for belonging to ground, then to non-ground points Cloud carries out cluster segmentation.

In non-ground points cloud cluster segmentation part, the cluster segmentation side based on fixed threshold radius of current more use Method, the selection of threshold value influences very big to the result of segmentation, if threshold value chooses excessive, the nearer wisp of spacing distance may not It can be separated (less divided)；If threshold value chooses too small, the larger object of spacing distance (such as building) may be divided into many Individual cluster (over-segmentation).

The content of the invention

In order to avoid being asked using fixed threshold the less divided caused by non-ground points cloud progress cluster segmentation and over-segmentation Topic, the invention provides a kind of point cloud segmentation method, the described method comprises the following steps：

S1：Tested scene is scanned by column by depth transducer, to obtain the depth information of the tested scene, The depth information of the tested scene is subjected to Coordinate Conversion, to obtain three-dimensional letter of the tested scene under local coordinate Breath；

S2：Ground point cloud is partitioned into from the three-dimensional information；

S3：Cluster segmentation, the non-ground points are carried out to non-ground points cloud by the cluster segmentation mode of adaptive threshold Cloud is other clouds in the three-dimensional information in addition to the ground point cloud.

Wherein, in step S2, ground point cloud is partitioned into from the three-dimensional information and is specifically included：

S201：To arrange the three-dimensional information is traveled through for unit, and using traverse one row as working as prostatitis；

S202：According to the direction traversal each point for working as prostatitis from bottom to up；

S203：Calculate the Z axis coordinate of current point traversed and the depth transducer position ground level it The absolute value of difference, if the absolute value is less than height threshold, using the current point as first initial point Ps (1), is performed Step S204, otherwise return to step 202；

S204：Detect the cut off Pe (k) after the initial point Ps (k), and by the initial point Ps (k) and cut off Pe (k) is used as a pair；

S205：The initial point Ps (k+1) after cut off Pe (k) is detected, if detecting initial point Ps (k+1), k=is made K+1, and return to step S204, otherwise by each pair initial point Ps (k) and cut off Pe (k) and each pair initial point Ps (k) and cut-off Point between point Pe (k) performs step S206 as the ground point addition ground point cloud；

S206：Judge whether each row of the three-dimensional information are traversed to, if so, then performing step S3, otherwise return Return step S202.

Wherein, in step S3, cluster segmentation tool is carried out to non-ground points cloud by the cluster segmentation mode of adaptive threshold Body includes：

S301：Cluster segmentation is carried out to the non-ground points cloud using fixed initial threshold dth (0)；

S302：Each cluster that traversal cluster segmentation is obtained；

S303：The adaptive threshold dth (w) of the current cluster traversed is calculated according to the cluster scale of construction, judges described adaptive Answer whether threshold value dth (w) is less than the initial threshold dth (0), if so, then based on the adaptive threshold dth (w) to described Current cluster is further split；

S304：Judge whether each cluster that the cluster segmentation is acquired is traversed to, if it is not, then return to step S303。

Wherein, in step S303, had according to the adaptive threshold dth (w) that the cluster scale of construction calculates the current cluster traversed Body includes：

S3031：Each point in the current cluster is traveled through, it is flat that the current point traversed is combined into the fitting of its neighborhood point Face, and normal vector of the normal vector for obtaining plane as the current point will be fitted；

S3032：Calculate in the current cluster normal vector a little average value, and using the average value as described The normal vector currently clustered；

S3033：Institute in the current cluster is a little projected to projection plane, chosen on the projection plane The minimum rectangle of all subpoints is included, the projection plane is the plane vertical with the normal vector currently clustered；

S3034：The adaptive threshold dth (w) currently clustered is calculated by following formula,

Dth (w)=k₁·L(w)+k₂

Wherein, L (w) is the bond length of the minimum rectangle, k₁And k₂For constant.

Wherein, in step S3, cluster segmentation tool is carried out to non-ground points cloud by the cluster segmentation mode of adaptive threshold Body includes：

S311：Each point in the non-ground points cloud is traveled through, the adaptive threshold of the current point traversed is calculated；

S312：Cluster minute is carried out to the non-ground points cloud according to the adaptive threshold each put in the non-ground points cloud Cut.

Wherein, in step S311, the adaptive threshold dth of the current point traversed is calculated by following formula,

Wherein, L is the depth information of the current point, and A is the angular resolution of the depth transducer, and v is the depth The translational speed of sensor in the horizontal direction, f is the scan frequency of the depth transducer, and α is plane where scan line and v Angle between direction, plane where the scan line is the central point of the depth transducer and the T of the tested scene Plane where row, the T is classified as the row including the current point, and β is plane where scan line and quilt in tested scene The angle of body surface is surveyed, θ is the angle between the T row and horizontal direction of the tested scene, A_wSwept for depth transducer The effective angle retouched, Δ L is L error amount, and Δ v is v error amount, and Δ α is α error amount.

Wherein, after step S3, in addition to：

S4：Merged in the cluster that step S3 is obtained by the cluster of over-segmentation.

Wherein, step S4 is specifically included：

S401：Each cluster that traversal cluster segmentation is acquired；

S402：Each point in the current cluster q that traverses is traveled through, the current point traversed is combined into its neighborhood Point fit Plane, and normal vector of the normal vector for obtaining plane as the current point will be fitted；

S403：Calculate in the current cluster q normal vector a little average value, and using the average value as described The normal vector currently clustered；

S404：Institute in the current cluster q is a little projected to projection plane, calculates the current cluster q's Projected area, the projection plane is the plane vertical with the normal vector of the current cluster q；

S405：Judge whether the projected area is more than preset area, if so, then using the current cluster q as substantially Amount cluster；

S406：Judge whether each cluster that cluster segmentation is acquired is traversed to, if so, then performing step S407, otherwise return to step S402；

S407：If the spacing distance between two big scale of construction cluster is less than pre-determined distance, by the corresponding two big scale of construction Cluster merging is a cluster, until the spacing distance between the big scale of construction cluster of any two is all higher than the pre-determined distance, institute It is the distance between nearest two points in two clusters to state the spacing distance between two big scale of construction cluster.

Wherein, also include after step S407：

S408：Each cluster that traversal step S407 is acquired；

S409：Calculate the difference DELTA H, the current cluster r between the current cluster r traversed height and ground level Height for the current cluster r somewhat middle Z axis coordinate minimum value, the ground level of the current cluster r is described The maximum of Z axis coordinate in first initial point of corresponding row is each put in current cluster r；

S410：Judge whether the difference DELTA H is more than preset difference value, if so, then that the current cluster r is poly- as suspending Class；

S411：Whether each cluster that judgment step S407 is acquired is traversed to, if so, then performing step S412, otherwise return to step S409；

S412：By each suspension Cluster merging to the cluster closest with it, until each cluster that suspends is incorporated into In one non-cluster that suspends.

The invention also discloses a kind of point cloud segmentation device, described device includes：

Scan conversion module, it is described tested to obtain for being scanned by column by depth transducer to tested scene The depth information of scene, carries out Coordinate Conversion, to obtain the tested scene local by the depth information of the tested scene Three-dimensional information under coordinate system；

Module is split on ground, for being partitioned into ground point cloud from the three-dimensional information；

Module is split on non-ground, and cluster point is carried out to non-ground points cloud for the cluster segmentation mode by adaptive threshold Cut, the non-ground points cloud is other clouds in the three-dimensional information in addition to the ground point cloud.

The present invention carries out cluster segmentation by the cluster segmentation mode of adaptive threshold to non-ground points cloud, effectively prevent Less divided and over-segmentation problem caused by cluster segmentation is carried out to non-ground points cloud using fixed threshold.

Brief description of the drawings

When Fig. 1 is that the present invention is scanned to tested scene, the structured flowchart for the scanning system being based on；

Fig. 2 is the depth information of the tested scene collected according to Fig. 1 scanning system；

Fig. 3 is the flow chart of the point cloud segmentation method of one embodiment of the present invention；

Fig. 4 is the schematic diagram for the scan line that an embodiment of the present invention is gathered in depth transducer；

Fig. 5 is the schematic diagram of the point cloud in the scan line shown in Fig. 4；

Fig. 6 is schematic diagram of an embodiment of the present invention when ground is split；

Fig. 7 a are that first point in the scan line direction from bottom to up of an embodiment of the present invention belongs to the signal of ground point Figure；

Fig. 7 b are that first point in the scan line direction from bottom to up of an embodiment of the present invention belongs to showing for non-ground points It is intended to；

Fig. 8 is that the bond length of the minimum rectangle of an embodiment of the present invention obtains flow chart；

Fig. 9 is the schematic diagram of the cluster projection area of an embodiment of the present invention；

Figure 10 is the design sketch for being partitioned into ground point cloud of an embodiment of the present invention；

Figure 11 is an embodiment of the present invention by the design sketch after being merged by the cluster of over-segmentation；

Figure 12 is the target point A of an embodiment of the present invention₁Neighbourhood model；

Figure 13 is the structured flowchart of the point cloud segmentation device of one embodiment of the present invention.

Embodiment

With reference to the accompanying drawings and examples, the embodiment to the present invention is described in further detail.Implement below Example is used to illustrate the present invention, but is not limited to the scope of the present invention.

Reference picture 1, during to tested scene scanning, the scanning system being based on by storage control module, depth transducer and Position and attitude sensor group is into depth transducer and the position and attitude sensor is both placed in above mobile platform.Storage control Molding block provides order so that the depth information (reference picture 2) for the tested scene that depth transducer acquisition scans are arrived, meanwhile, position Attitude transducer record is put in information such as the position of each moment mobile platform, posture and mobile platform gait of march.Will be in depth The depth information obtained under degree sensor coordinate system, can be obtained under the attitude transducer coordinate system of position by coordinate transform The three-dimensional information of scene is tested, then obtains being tested the three-dimensional information of scene under local coordinate by coordinate transform, due to collection Outdoor scene be typically made up of substantial amounts of spatial point, the set of these spatial points is also referred to as a cloud.

In present embodiment, the depth transducer selects laser radar, and position and attitude sensor selects GPS, institute's rheme Put attitude transducer and also can select GPS/IMU (inertial navigation) integrated navigation system.

Fig. 3 is the flow chart of the point cloud segmentation method of one embodiment of the present invention；Reference picture 3, methods described include with Lower step：

S1：Tested scene is scanned by column by depth transducer, to obtain the depth information of the tested scene, The depth information of the tested scene is subjected to Coordinate Conversion, to obtain three-dimensional letter of the tested scene under local coordinate Breath；

S2：Ground point cloud is partitioned into from the three-dimensional information；

S3：Cluster segmentation, the non-ground points are carried out to non-ground points cloud by the cluster segmentation mode of adaptive threshold Cloud is other clouds in the three-dimensional information in addition to the ground point cloud.

In step S1, when being scanned by column by depth transducer to tested scene, it can use from bottom to up or from upper Scanning direction under is scanned by column, and it is scan line starting point to scan first obtained point, and last point is scanning Line terminating point.

Depth transducer is scanned column by column during mobile platform is moved with fixed angular resolution, obtains correspondence ring The depth information in border, every column data of scanning is also referred to as scan line.There is fixed acquisition order and points per column data, most The depth information of the tested scene obtained eventually is by by showing all scanline groups of sequence into the corresponding data of each testee It is to be collectively constituted by the partial dot in some scan lines.Because depth transducer takes the mode gathered by column, the points of each column It is fixed with order, therefore the depth information obtained is with by the characteristic for showing sequence.Shown in Fig. 4 as under a given scenario, The schematic diagram for the scan line that depth transducer on mobile platform is gathered in the course of the work, depth transducer is with fixed Angular resolution is scanned from bottom to top, and ground, wisp, ground and building are arrived in scanning successively, obtain the corresponding depth of each point Spend information.Triangle pair should be the data on the data on the ground collected, the circular non-ground object for corresponding to collect. The information for the depth information binding site attitude transducer that depth transducer is collected, by coordinate transform, finally gives The coordinate of each scanning element under local coordinate, three-dimensional information of the as described tested scene under local coordinate.

Initially face amount is determined to be accurate, while connecting in the segmentation having using the ground point on each column scan line, section Continuous the characteristics of, extracts ground, to improve the accurate segmentation to ground point cloud, it is preferable that in step S2, from the three-dimensional information Ground point cloud is partitioned into specifically include：

S201：To arrange the three-dimensional information is traveled through for unit, and using traverse one row as working as prostatitis；

S202：According to the direction traversal each point for working as prostatitis from bottom to up；

S203：Calculate the Z axis coordinate of current point traversed and the depth transducer position ground level it The absolute value of difference, if the absolute value is less than height threshold, using the current point as first initial point Ps (1), is performed Step S204, otherwise return to step 202；

S204：Detect the cut off Pe (k) after the initial point Ps (k), and by the initial point Ps (k) and cut off Pe (k) is used as a pair；

S205：The initial point Ps (k+1) after cut off Pe (k) is detected, if detecting initial point Ps (k+1), k=is made K+1, and return to step S204, otherwise by each pair initial point Ps (k) and cut off Pe (k) and each pair initial point Ps (k) and cut-off Point between point Pe (k) performs step S206 as the ground point addition ground point cloud；

S206：Judge whether each row of the three-dimensional information are traversed to, if so, then performing step S3, otherwise return Return step S202.

Two ways can be used by realizing the cluster segmentation of the non-ground points cloud of adaptive threshold：A kind of is oneself based on the scale of construction Threshold segmentation mode is adapted to, another is the adaptive threshold fuzziness mode based on multi-parameter.

For ease of realizing the adaptive threshold fuzziness mode based on the scale of construction, it is preferable that in step S3, pass through adaptive threshold Cluster segmentation mode to non-ground points cloud carry out cluster segmentation specifically include：

S301：Cluster segmentation is carried out to the non-ground points cloud using fixed initial threshold dth (0)；

S302：Each cluster that traversal cluster segmentation is obtained；

S303：The adaptive threshold dth (w) of the current cluster traversed is calculated according to the cluster scale of construction, judges described adaptive Answer whether threshold value dth (w) is less than the initial threshold dth (0), if so, then based on the adaptive threshold dth (w) to described Current cluster is further split；

S304：Judge whether each cluster that the cluster segmentation is acquired is traversed to, if it is not, then return to step S303。

For ease of being determined according to the scale of construction in adaptive threshold, step S303, according to cluster the scale of construction calculate traverse it is current The adaptive threshold dth (w) of cluster is specifically included：

S3031：Each point in the current cluster is traveled through, it is flat that the current point traversed is combined into the fitting of its neighborhood point Face, and normal vector of the normal vector for obtaining plane as the current point will be fitted；

S3032：Calculate in the current cluster normal vector a little average value, and using the average value as described The normal vector currently clustered；

S3033：Institute in the current cluster is a little projected to projection plane, chosen on the projection plane The minimum rectangle of all subpoints is included, the projection plane is the plane vertical with the normal vector currently clustered；

S3034：The adaptive threshold dth (w) currently clustered is calculated by following formula,

Dth (w)=k₁·L(w)+k₂

Wherein, L (w) is the bond length of the minimum rectangle, k₁And k₂For constant.

For ease of realizing the adaptive threshold fuzziness mode based on multi-parameter, it is preferable that in step S3, pass through adaptive thresholding The cluster segmentation mode of value carries out cluster segmentation to non-ground points cloud and specifically included：

S311：Each point in the non-ground points cloud is traveled through, the adaptive threshold of the current point traversed is calculated；

S312：Cluster minute is carried out to the non-ground points cloud according to the adaptive threshold each put in the non-ground points cloud Cut.

For ease of being determined according to multi-parameter in adaptive threshold, step S311, the current point traversed is calculated by following formula Adaptive threshold dth,

Wherein, L is the depth information of the current point, and A is the angular resolution of the depth transducer, and v is the depth The translational speed of sensor in the horizontal direction, f is the scan frequency of the depth transducer, and α is plane where scan line and v Angle between direction, plane where the scan line is the central point of the depth transducer and the T of the tested scene Plane where row, the T is classified as the row including the current point, and β is plane where scan line and quilt in tested scene The angle of body surface is surveyed, θ is the angle between the T row and horizontal direction of the tested scene, A_wSwept for depth transducer The effective angle retouched, Δ L is L error amount, and Δ v is v error amount, and Δ α is α error amount.

After the problem of to solve over-segmentation, step S3, in addition to：

S4：Merged in the cluster that step S3 is obtained by the cluster of over-segmentation.

To solve the problem of big scale of construction cluster is by over-segmentation, it is preferable that step S4 is specifically included：

S401：Each cluster that traversal cluster segmentation is acquired；

S402：Each point in the current cluster q that traverses is traveled through, the current point traversed is combined into its neighborhood Point fit Plane, and normal vector of the normal vector for obtaining plane as the current point will be fitted；

S403：Calculate in the current cluster q normal vector a little average value, and using the average value as described The normal vector currently clustered；

S404：Institute in the current cluster q is a little projected to projection plane, calculates the current cluster q's Projected area, the projection plane is the plane vertical with the normal vector of the current cluster q；

S405：Judge whether the projected area is more than preset area, if so, then using the current cluster q as substantially Amount cluster；

S406：Judge whether each cluster that cluster segmentation is acquired is traversed to, if so, then performing step S407, otherwise return to step S402；

S407：If the distance between central point of two big scale of construction cluster is less than pre-determined distance, big by corresponding two Scale of construction Cluster merging is a cluster, until the distance between central point of the big scale of construction cluster of any two is all higher than described preset Distance.

The problem of for the cluster that solves to suspend by over-segmentation, it is preferable that also include after step S407：

S408：Each cluster that traversal step S407 is acquired；

S409：Calculate the difference DELTA H, the current cluster r between the current cluster r traversed height and ground level Height for the current cluster r somewhat middle Z axis coordinate minimum value, the ground level of the current cluster r is described The maximum of Z axis coordinate in first initial point of corresponding row is each put in current cluster r；

S410：Judge whether the difference DELTA H is more than preset difference value, if so, then that the current cluster r is poly- as suspending Class；

S411：Whether each cluster that judgment step S407 is acquired is traversed to, if so, then performing step S412, otherwise return to step S409；

S412：By each suspension Cluster merging to the cluster closest with it, until each cluster that suspends is incorporated into In one non-cluster that suspends.

Embodiment 1

The data in scan line are analyzed, as shown in Figure 5, it can be seen that point p₁To point, pointTo point For the cloud data on ground, pointTo pointAnd pointTo point p_nFor the cloud data on non-ground object. It can be obtained with reference to most scene analysis, in a scan line, the cloud data for belonging to ground is segmented in presence and section continuously mostly, Such as Fig. 5 midpoints p₁To pointPoint cloud tract, pointTo pointPoint cloud tract be two sections and belong to ground Point cloud section；Same section belong to ground point cloud, local relief is smaller between points, and elevation difference is also smaller.Divide with reference to more than Analysis, is effective extraction ground point cloud, and detection column by column belongs to the point cloud tract on ground.Belong to the point cloud sequence on ground at every section In, it is initial point Ps to define first point, and last point is cut off Pe, and initial point and cut off are distinguished in corresponding diagram 5 Point p₁,And point,。

The present invention is illustrated with a specific embodiment below, but does not limit protection scope of the present invention.In above-mentioned reality Apply on the basis of mode, ground point cloud is partitioned into from the three-dimensional information and is specifically included：

By row to be handled, the point cloud section for belonging to ground in every scan line is extracted column by column.Processed one is set The initial point that kth section belongs to the point cloud tract on ground in bar scan line is Ps (k), and cut off is Pe (k), and first paragraph belongs to ground The corresponding initial point of point cloud tract in face is Ps (1), and cut off is Pe (1), and the data to a scan line carry out ground distributor The method cut mainly is made up of three links, is first initial point Ps (1) of detection, detection cut off Pe (k) and detection respectively Initial point Ps (k+1) after cut off.Data to a row carry out flow such as Fig. 6 of ground segmentation, and specific steps include：

Step S2-1：Detect first initial point Ps (1)；

As illustrated in figs. 7 a and 7b, triangular representation ground point, round dot represents non-ground points (wherein, p_s1With above-mentioned Ps (1), p_e1With above-mentioned Pe (1), second initial point is p_s2, p_e2For the p_s2Corresponding detection cut off, the expression of other marks Mode ibid, is not being repeated again), first point in scan line direction from bottom to up both may belong to ground point, also may be used To be non-ground points, in order to obtain the accurate segmentation of a scan line upper ground surface part and non-above ground portion it may first have to ensure The correctness of first ground reference point.The detection of first initial point Ps (1) in scan line can be utilized based on mobile flat The known element of platform, due to the position (X of the depth transducer scanning center under local coordinate_lidar, Y_lidar, Z_lidar) it is real When store, the height (abbreviation podium level) of depth transducer scanning center to mesa base, as shown in H in Fig. 4, Ke Yizuo Use, install on a mobile platform after equipment for systematic parameter, platform can be drawn by being tested in the environment of wide flat Height H, in the case where system is not dismounted again, podium level H is constant.Utilize the position peace of depth transducer scanning center Platform height, with reference to the elevation information that measuring point is detected in scan line, can detect first initial point for belonging to ground in scan line Ps (1), i.e. above-mentioned steps S2-1, comprise the following steps：

(1)：To processed J row depth transducer depth informations, and set-point sequence number i (i=0,1 ..., n) and correspondingly I-th point of local coordinate system (X_i, Y_i, Z_i), first point correspondence i=0, corresponding local seat in the row depth information are set It is designated as (X₀, Y₀, Z₀)；

(2)：Calculate the Z axis coordinate Z of lower i-th point of local coordinate system_iWith the ground level of depth transducer position (Z_lidar- H) difference DELTA h_i, calculation formula is：Δh_i=| (Z_lidar-H)-Z_i|；

(3)：Judge corresponding difference in height Δ h_iWhether threshold value Zth is less than, if Δ h_i<Zth, then it is assumed that i-th point is to be somebody's turn to do Belong to first initial point Ps (1) on ground in column scan point；Otherwise make i=i+1, return to step (2)；

Situations such as threshold value Zth allows for measurement noise, ground local relief (including deceleration strip, traffic stud etc.), scan line Patient difference in height between upper two ground point.China national standard path provides that the height of projection on the road surface such as deceleration strip is not 5 centimetres can be higher than, considering further that in the planarization on ground and the influence of measurement noise, the present embodiment can tolerate between two ground points Difference in height Zth 15 centimetres are set to ensure enough allowances.

Step S2-2：Detect the cut off Pe (k) after initial point Ps (k)；

Detect the initial point Ps (k) of kth section ground point cloud tract afterwards, it is necessary to further detect this section of point cloud sequence Cut off Pe (k), and then kth section ground point cloud section could be extracted.In the method as proposed in the present invention, i-th is judged After individual point is the initial point Ps (k) of kth section ground point cloud tract, using adjacent 2 points after initial point Ps (k) of lines with The angle, θ of XOY plane formation judges whether the point after i points belongs to kth section ground point successively as the examination criteria of ground point Cloud tract, the cut off Pe (k) until detecting kth section ground point cloud tract, it is thus regarded that to point by point Ps (k) What Pe (k) ended belongs to a little ground.

Under local coordinate, the point u and v of difference two for belonging to ground in same scan line, their line is put down with XOY The angle, θ that face is formed_{U, v}For characterizing the fluctuating quantity on ground between 2 points of u, v, the angle, θ as ground point examination criteria. θ specific formula for calculation is as follows：

For certain section of ground point set G={ Ps (k) ..., Pe (k) } being partitioned into, due to the fluctuating between adjacent 2 points compared with Small, this section of ground point concentrates the corresponding angle, θ of the point of arbitrary neighborhood two j, j+1_{J, j+1}Certain threshold value is should be less than, i.e., should be met θ_{J, j+1}<θ_th.Thus ground sequence can further be detected by adjacent 2 points of corresponding angle information θ after initial point Ps (k), Until finding cut off Pe (k), i.e. above-mentioned steps S2-2, following steps are specifically included：

(1) it is j points, j=i+1 to set first point after initial point Ps (k) (i-th point in correspondence scan line)；

(2) under local coordinate, the line and the angle, θ of XOY plane of j points and j+1 points are calculated_{J, j+1}, it is specific to calculate public Formula is as follows：

Due to there is the noise that radar and GPS are produced, only by with adjacent a little corresponding angle, θ_{J, j+1}Cut to detect Stop Pe (k) method is excessively sensitive, to avoid next point (i.e. j+2 of j point consecutive points in influence of noise, the present embodiment Point), also take in simultaneously, i.e., also calculate angle, θ simultaneously_{J, j+2}；

(3) θ is judged_{J, j+1}、θ_{J, j+2}Whether threshold θ is both greater than_th, if it is, thinking that the fluctuating of consecutive points is excessive, judge j Point is cut off Pe (k)；Conversely, j=j+1 is then made, into step (2)；

Threshold θ_thTogether decided on by the fluctuating quantity and system noise on local ground, it is considered to above-mentioned various shadows that may be present Threshold θ in the factor of sound, the present embodiment_thIt is set as 17 ° to retain enough allowances.

Step S2-3：Detect the initial point Ps (k+1) after cut off Pe (k)；

After the cut off Pe (k) for obtaining kth section ground point cloud tract, the point from point Ps (k) to point Pe (k) is judged to It is set to the point for belonging to ground, set-point Pe (k) (i.e. in step S2-2 corresponding j-th point) local coordinate system is (X_j, Y_j, Z_j)。 , it is necessary to detect next section of ground point on the basis of the cut off Pe (k) for putting cloud sequence that the preceding paragraph belongs to ground has been obtained Initial point Ps (k+1), and then continue to detect the next section of point cloud sequence for belonging to ground.

For the point m after cut off Pe (k) on the column scan line (i.e. j-th points of the row), 2 points of correspondences of j, m are used Angle, θ_{J, m}And elevation Z_{J, m}Constraint judge whether point m belongs to next initial point Ps (k+1), if meeting θ_{J, m}It is less than Threshold θ_thAnd Z_{J, m}Less than threshold value Z_th, then decision-point m is next initial point Ps (k+1), on the contrary then think that point m is not initial Point, continuation is judged the point after m points.

Wherein 2 points of corresponding angle, θs of j, m_{J, m}It is the angle for the line and XOY plane formation for giving directions j, m, definition and meter Calculate angle, θ in formula and step S2-2_{U, v}It is identical；Elevation Z_{J, m}It is point j and height differences of the point m under local coordinate, calculating Formula is：Z_{J, m}=| Z_j-Z_m|；Threshold θ_thDefinition and value and step S2-2 in threshold θ_thIt is identical；Threshold value Zth definition and Value is identical with the threshold value Zth in step S2-1.

Step S2-3 specific implementation step is as follows：

(1) it is m points, i.e. m=j+1 to set at i.e. j-th point at first point after cut off Pe (k) ()；

(2) under local coordinate, cut off Pe (k) and point m difference in height Z are calculated_{J, m}, Z_{J, m}=| Z_j-Z_m|；Calculate cut-off The line of point Pe (k) and m points and the angle, θ of XOY plane_{J, m}；

(3) Z is judged whether_{J, m}Less than threshold value Zth and θ_{J, m}Less than threshold θ_th, if it is, thinking that m points are next section of ground The initial point Ps (k+1) of sequence；It is on the contrary then make m=m+1, into step (2)；

Next initial point Pss (k+1) of the step S2-3 after detecting cut off Pe (k) is performed, or to the row Institute after midpoint Pe (k) is a little all judged, then carries out step S2-4.

Step S2-4：Judge whether detect new initial point Ps (k+1) after cut off Pe (k), if in cut off New initial point Ps (k+1) is detected after Pe (k), then k=k+1 return to step S2-2 can be made to continue executing with；If in cut-off New initial point Ps (k+1) is not detected after point Pe (k), then the institute in the scan line after cut off Pe (k) is somewhat equal Non-ground points, it is possible to determine that the institute of the row cloud data is a little processed, all ground point cloud tracts of the row all by Extract.

For next scan line, synchronize the processing of rapid S2-1 to S2-4 identicals, thus by column, each column it is right paragraph by paragraph Ground point cloud is extracted, you can complete the segmentation of ground point cloud.

Embodiment 2

According to the ground dividing method based on scan line, it is necessary to non-after ground point cloud and non-ground points cloud are split Ground point cloud carries out cluster segmentation, and the point cloud that different objects are belonged in non-ground points cloud is separated from each other.

In order to avoid the above-mentioned cluster over-segmentation occurred using same fixed threshold and less divided phenomenon, the present embodiment is adopted With the relation between required threshold value when the adaptive threshold fuzziness mode based on the scale of construction, the i.e. scale of construction based on object and segmentation, lead to For often, the spacing distance between the object (such as building) of the big scale of construction is larger, and threshold value should take when splitting the object of the big scale of construction Obtain larger, and the spacing distance between the object (such as automobile) of small volume can be smaller, threshold value should during segmentation small volume object Obtain smaller, it is, the threshold radius of cluster segmentation and the scale of construction of object, which exist, is incremented by relation.

The adaptive threshold fuzziness mode is comprised the following steps that：

Step S3-1：Using fixed initial threshold dth0, the base based on European clustering method is carried out to non-ground points cloud Plinth is split；

Set and non-ground points are based on after fixed threshold dth0 clusters terminate, obtain W cluster, cluster numbering w, first Cluster correspondence w=1；

S3-2：For carrying out clustering obtained cluster w based on fixed threshold dth0, calculate w based on the adaptive of the scale of construction Threshold value dth (w)；

For carrying out clustering obtained cluster w, it is necessary to calculate its corresponding adaptive threshold dth based on fixed threshold dth0 (w).Analyze as described above, spacing distance is larger between the object (such as building) of the big scale of construction, and the object (such as automobile) of small volume Between spacing distance can be smaller.To characterize the incremental relation between threshold size and the object scale of construction, while complicated for calculating Consideration in terms of degree, self-adaption cluster radial design be to the linear relationship that is directly proportional of cluster size, w-th cluster based on body The calculation formula of the adaptive threshold of amount is shown below：

Dth (w)=k₁·L(w)+k₂

In the parameter that above formula characterizes linear relationship, coefficient k 1 is by analyzing distance and object body between general familiar object Relation is obtained between amount, the object often occurred in such as building, automobile, bicycle, street lamp scene, and k1=is taken in the present embodiment 0.01m；Coefficient k 2 is gathered by vehicle-mounted mobile platform depth transducer in minimum distinguishable distance between two articles, the present embodiment The resolution ratio of data is determined, k2=0.18m is taken in the present embodiment.

Length L (w) in formula is the length for clustering w, and its concrete operation process is as shown in Figure 8.Point in figure is represented Cluster the point in w.As shown in (a1) in Fig. 8, for the point in cluster w, it is fitted by the point in the range of each surrounding neighbors Plane, obtains the normal vector of the normal vector of the plane, the i.e. point.According to identical method, after the normal vector for obtaining each point, by w In the normal vector n that middle normal vector a little is averaged as cluster w, such as Fig. 8 shown in (a2).By the normal vector with the cluster Vertical plane elects projection plane as, by all spot projections in w to the perspective plane, and the point in Fig. 8 in (a3) on perspective plane is The subpoint obtained after cluster projection.The minimum rectangle that all subpoints can be included is chosen in the projection plane, In the both sides of the rectangle, shorter length of side L is defined as in current cluster w length L (w), such as Fig. 8 rectangle shorter edge in (a4) Shown in long L.

S3-3：Judge whether the adaptive threshold dth (w) for clustering w is less than initial threshold radius dth0, if it is, base Cluster w is further split in adaptive threshold dth (w), the object segmentation not separated during so as to by initial segmentation Fully；It is on the contrary then think cluster w it is divided complete, no longer carry out the segmentation based on adaptive threshold.

S3-4：Judge whether all to have handled all W clusters, if it is, the cluster based on scale of construction adaptive threshold Segmentation terminates；Conversely, w=w+1 is then made, return to step S3-2；

Embodiment 3

Carried out based on adaptive threshold radius to being tested non-ground points all in scene after cluster segmentation, because scanning hides The reason such as gear, mobile platform swerve and testee is irregular, cluster segmentation result can have object showing by over-segmentation As mainly having two class situations, one kind is for irregularly shaped objects such as trees, by taking trees as an example, because trunk, each branch etc. are closed Connection is smaller, it is easy to be divided into multiple clusters, wherein only trunk is cluster on the ground, and other clusters are suspended in ground On, referred to as suspend cluster.Also one kind be exactly for the big scale of construction object such as building, because threshold radius sets smaller, Vehicle-mounted mobile platform swerves or scanned to deposit can cause big scale of construction cluster to be too cut into multiple clusters under occlusion Situation.Therefore need to merge the cluster of over-segmentation processing.

When merging processing to the big scale of construction of over-segmentation cluster, tested cluster projection is hung down to the normal vector with the cluster Straight plane, then extracts the profile on perspective plane to calculate the projected area of tested cluster.If the projected area of a cluster More than empirical value S_u, the cluster is judged as a big scale of construction cluster.If the spacing distance between the big scale of construction cluster of any two Less than being required between buildings in general apart from l_u, the two clusters are considered to belong to same building thing and need to be merged into one Spacing distance between cluster, the big scale of construction cluster of two of which is the distance between two points nearest in the two clusters.Cross The combining step of the big scale of construction cluster of segmentation is specific as follows：

(1) set and carried out based on adaptive threshold after cluster segmentation, non-ground points cloud is divided into Q cluster, set poly- Class sequence number q (q=1,2 ..., Q), sets first cluster correspondence q=1 in segmentation result；

(2) for the point in cluster q, similar to the process in Fig. 8 in S2-2 shown in (a1) to (a3), each point week is passed through The point fit Plane in contiguous range is enclosed, the normal vector of the normal vector of the plane, the i.e. point is obtained, according to identical method, obtains After the normal vector of each point, institute's normal vector a little in q is averaged as the normal vector n for clustering q, will a little be thrown in q Shadow is to the plane vertical with n.Extract the profiles that project on the projection surface of cluster q to calculate tested cluster by convex hull method afterwards Q projected area S (q), as shown in Figure 9；

(3) judge whether cluster q projected area S (q) is more than threshold value Su, if it is, thinking that q clusters for the big scale of construction； It is on the contrary then think that q is not belonging to big scale of construction cluster；

(4) determine whether to handle all Q clusters, if it is, into step (5)；It is on the contrary then make q=q+ 1, into step (2)；

(5) processing is merged to the big scale of construction of over-segmentation cluster, if that is, the big scale of construction of any two cluster between away from From less than requiring distance l between buildings in general_u, the two clusters are considered to belong to same building thing, the two are clustered It is merged into a cluster.Until the distance between big scale of construction cluster of any two is all higher than l_uWhen, then the big scale of construction of over-segmentation is gathered Class, which merges, to be terminated；Otherwise step (5) is continued executing with；

In the present embodiment, it is contemplated that tree and building is caused in itself blocks, the area of big scale of construction cluster is determined whether Threshold value S_uIt is set to empirical value 30m², the general spacing l of two buildings_uIt is set to empirical value 10m.

When merging processing to the levitated object of over-segmentation, it is necessary first to which whether judge cluster is the cluster that suspends.On ground Face point cloud segmentation link, obtains the ground point cloud and non-ground points cloud in every scan line by column, and then for each non-ground Point can match corresponding ground level Zg, i.e., to each non-ground points on any one surface sweeping line j, all correspond to j row First initial ground point of all non-ground points and the jth row for arranging jth in the ground level Zg of position, the present embodiment Z axis coordinate value matchings of the Ps (1) under local coordinate, it is believed that point Ps (1) Z axis coordinate value reflects the non-ground of jth row The ground level of point position；

Set and R cluster is had after terminating to the big scale of construction Cluster merging of over-segmentation, to wherein any one cluster r, need The difference DELTA H between cluster r height and position and corresponding ground level is calculated, and then judges whether it suspends.Due to Arbitrary point t have matched a corresponding ground level Zg (t) in cluster r, take cluster in r Z axis coordinate a little most Small value is that min { Z (t) } is cluster r height and position, takes the maximum max in the ground level Zg that all-pair is answered in cluster r { Zg (t) } is the corresponding ground levels of cluster r, it is believed that the difference DELTA H between cluster r height and position and corresponding ground level =| min { Z (t) }-max { Zg (t) } |.For any cluster r, if corresponding Δ H is more than threshold value Fth, then it is assumed that cluster r is outstanding Floating cluster.For the cluster of suspension, the spacing distance between cluster is calculated, and the Cluster merging is clustered recently to it, until The cluster that suspends all is ultimately to be incorporated into the non-suspension belonged on ground a cluster.Comprise the following steps that：

(1) set after the processing of big scale of construction Cluster merging, have R and cluster, setting cluster sequence number r (r=1,2 ..., R), first cluster correspondence r=1；

(2) for cluster r, the height min { Z (t) } and corresponding ground level max { Zg (t) } of the cluster are calculated respectively, And then calculate difference DELTA H, i.e. Δ H=between cluster r height and corresponding ground level | min { Z (t) }-max { Zg (t) } |；

(3) judge that the corresponding Δ H of cluster r are no and be more than threshold value Fth, if it is, thinking that r clusters to suspend；It is on the contrary then recognize The cluster that suspends is not belonging to for r；

In the present embodiment, it is considered to which the influence of error simultaneously stays certain allowance, given threshold Fth is 1m.

(4) determine whether all R clusters have all been carried out suspending to judge, if it is, into step (5)；It is on the contrary then R=r+1 is made, into step (2)；

(5) e is clustered for any one suspension to be combined, calculates the spacer between the cluster of other in cluster e and R From, and cluster e is merged into closest cluster with interval.If finally be all merged into one belongs to ground to the cluster that suspends On non-suspension cluster on, then the merging treatment of the suspension cluster of over-segmentation is completed, on the contrary then continue executing with (5)；Wherein, two Spacing distance between the cluster that suspends refers to the distance of the central point of two clusters, and the coordinate of the central point of cluster is the cluster Coordinate value a little be averaging.

Figure 10 show the design sketch being partitioned into after ground point cloud.To the ground segmentation proposed by the present invention based on scan line When method carries out Performance Evaluation, for method accuracy from two class error evaluations：A) originally belong to ground point but be mistaken for non- Ground point, b) originally belonging to non-ground points is mistaken for ground point.To assess above-mentioned error, we are counted manually from special scenes Ground is counted and non-ground points number, and carries out comparing calculation with testing obtained result.Scan line is based on by proposed by the present invention The ground dividing method (R.B.Rusu, " based on random sampling coherence method that proposes of ground dividing method and R.B.Rusu Semantic 3D object maps for everyday manipulation in human living Environments, " KI-K ü nstliche Intelligenz, vol.24, no.4, pp.345-348, Nov.2010.) carry out Compare, by related experiment and calculating, draw set forth herein the ground dividing method error rate based on scan line be The error rate of 0.674%, R.B.Rusu method is 1.52%.In terms of method time complexity, both of which is operated in The computer platform of Intel E7400,2.8GHz, 2GB internal memory, method proposed by the present invention and R.B.Rusu method operation Time is 0.028 second and 4.820 seconds respectively, illustrates method proposed by the present invention faster, and average each second can be handled up to 22358179 data.Specific performance comparison result such as following table：

Method	Error a (%)	Error b (%)	Overall error (%)	Run time (s)
					The inventive method	0.401	0.273	0.674	0.028
R.B.Rusu methods	0.919	0.623	1.52	4.820

From performance comparison result, the overall performance of the inventive method is higher than R.B.Rusu method.

Figure 11 is shown to carry out splitting based on scale of construction adaptive threshold to non-ground points cloud, then to the cluster of over-segmentation The corresponding cloud data of different vehicles has different gray values in result after merging, Figure 11, has represented method Different vehicles and separated through determining, it can be seen that method can carry out accurate to the large scene three-dimensional point cloud of acquisition True segmentation, can split accurately even for the slope pavement in scene, automobile etc..

Embodiment 4

In order to avoid the above-mentioned cluster over-segmentation occurred using same fixed threshold and less divided phenomenon, the present embodiment is adopted With the adaptive threshold fuzziness mode based on multi-parameter, the adaptive threshold fuzziness mode based on multi-parameter is specifically included：

First, the distance model of consecutive points in point cloud is set up：Based on mobile platform operation logic, as platform is to reach Dynamic, depth transducer is scanned by column successively to tested scene, it is assumed that in subrange, where the point of proximity around a certain target point Face is similar to plane, and collection schematic diagram is as shown in figure 12, to target point A₁And its modeling of neighborhood point, the selection of neighborhood point is as in Figure 12 The point A of mark₂, B₁, B₂Deng respective distances are followed successively by target point A₁It is up and down between consecutive points apart from A in the scan line of place₁A₂, Apart from A between the point nearest with it and the point in its adjacent two scan line₁B₁, it is nearest with it in its adjacent two scan line Point B₁Up and down 2 points with the point apart from A₁B₂.Based on consecutive points distance model, derive between consecutive points apart from A₁A₂、A₁B₂、A₁B₁'s Calculation expression.Due to A₁B₂>A₁B₁, and A₁B₂>A₁A₂, the consecutive points distance around each target point can pass through A₁B₂With reference to It is determined that.In view of radar scanning center to measured point apart from L, mobile platform pace v, plane where radar scanning line with Angle α is respectively present error delta L, Δ v and Δ α between car travel direction, the cluster segmentation threshold radius each put such as following formula institute Show：

Wherein, L is the depth information of the current point, and A is the angular resolution of the depth transducer, and v is the depth The translational speed of sensor in the horizontal direction, f is the scan frequency of the depth transducer, and α is plane where scan line and v Angle between direction, plane where the scan line is the central point of the depth transducer and the T of the tested scene Plane where row, the T is classified as the row including the current point, and β is plane where scan line and testee surface Angle (because the parameter is a uncertain value, so the present embodiment using a default angle as the parameter value), θ is described Angle between the T row and horizontal direction of tested scene, A_wThe effective angle scanned for depth transducer, Δ L is L error Value, Δ v is v error amount, and Δ α is α error amount.

In the parameter that above-mentioned formula is related to, L, A, A_w, α, f has determined as constant, gathered data mistake before acquisition Constant in journey, depth transducer range error Δ L is 0.03 meter in the present embodiment, and angular resolution A is the radian of 0.5 °, i.e. π/360, Scan effective range A_wFor 90 °, i.e. pi/2 radian, α is 45 ° and 135 °, and scan frequency is 75 hertz.β values are one and preset Value, takes β=15 °.L, v, Δ v and Δ α are the real time data that is obtained in scanning process, and wherein radar scanning center is to being scanned The distance between point L is obtained by depth transducer, mobile platform travel speed v, velocity error Δ v, scan line angular error Δ α Obtained by position and attitude sensor.

The N number of non-ground points arranged for jth, set-point sequence number I (I=1,2 ..., N), according to above-mentioned based on multi-parameter Cluster segmentation threshold expression, calculates each self-corresponding cluster segmentation threshold value of each non-ground points respectively, sets i-th point correspondence Threshold value be d_thI；First non-ground points correspondence I=1 that jth is arranged is set, for target point I, according to the corresponding threshold value of point Radius d_thI, judge whether non-ground points to target point I that the row of jth -1 and jth are arranged distance are less than threshold value d_thIIf, It is, then it is assumed that corresponding point is target point I neighbor point, neighbor point and target point I is gathered for a class；Repeat the above steps, directly To the cluster segmentation processing all carried out to all non-ground points based on multi-parameter adaptive threshold, then non-ground points are based on The cluster segmentation of multi-parameter adaptive threshold terminates.

Even if not carrying out the merging treatment of over-segmentation cluster also, the cluster segmentation method based on multi-parameter adaptive threshold Obtained segmentation result is very fully and very close to the result finally clustered, simultaneously as this method is directly right All non-ground points cloud carries out the cluster segmentation once based on oneself corresponding threshold radius of each point, and based on the scale of construction from A based fragmentation will first be carried out by adapting to the cluster segmentation method of threshold value, then based fragmentation result is carried out to be based on adaptive threshold Cluster segmentation, therefore the cluster segmentation method time complexity of the adaptive threshold based on multi-parameter is lower.

Embodiment 5

It is different from depth transducer in embodiment 1-4 and uses fixed effective angle scope A_w(A_wLess than 360 degree) carry out by Depth transducer is by the way of 360 degree of scannings in column scan, the present embodiment, i.e., the mistake that depth transducer is moved in system platform Gather the depth information of surrounding scene in journey by 360 degree of rotation sweeps with fixed angular resolution, depth transducer often rotates One circle correspondence obtains one and encloses depth information.Under normal circumstances, can more efficient, more fully it be obtained by the way of 360 degree of scannings The depth information of tested scene is taken, for example, when movement is scanned system platform on road；According in embodiment 1-4 Scan mode, i.e., depth transducer is with fixed effective angle scope A_w(A_wLess than 360 degree) tested scene is swept by column When retouching, depth transducer is tended not to while the corresponding depth information of the scene for collecting both sides of the road；If depth transducer is adopted With the mode of 360 degree of scannings, because the angle of scanning is sufficiently large, depth transducer can be scanned in the range of 360 degree simultaneously The depth information of tested scene.

Depth transducer is actually by the effective angle of depth transducer in embodiment 1-4 by the way of 360 degree of scannings Spend scope A_w360 degree have been increased to, obtain each is scanned by column in each circle depth information for collecting correspondence embodiment 1-4 Row depth information, if thinking, the circle depth information in embodiment 5 is " row " data, and 360 degree of scannings mode is obtained The characteristics of data, is consistent with the data characteristicses handled in embodiment 1-4, and point cloud segmentation method proposed by the invention is applied to deep Spend the situation of sensor 360 degree of scannings.

The invention also discloses a kind of point cloud segmentation device, reference picture 13, described device includes：

Scan conversion module, it is described tested to obtain for being scanned by column by depth transducer to tested scene The depth information of scene, carries out Coordinate Conversion, to obtain the tested scene local by the depth information of the tested scene Three-dimensional information under coordinate system；

Module is split on ground, for being partitioned into ground point cloud from the three-dimensional information；

Module is split on non-ground, and cluster point is carried out to non-ground points cloud for the cluster segmentation mode by adaptive threshold Cut, the non-ground points cloud is other clouds in the three-dimensional information in addition to the ground point cloud.

The system is corresponding with the above method, in addition to other are to realize the module of the above method, submodule, unit, son Unit etc., to avoid redundancy, so do not illustrate herein.

Embodiment of above is merely to illustrate the present invention, and not limitation of the present invention, about the common of technical field Technical staff, without departing from the spirit and scope of the present invention, can also make a variety of changes and modification, therefore all Equivalent technical scheme falls within scope of the invention, and scope of patent protection of the invention should be defined by the claims.

Claims (9)

1. a kind of point cloud segmentation method, it is characterised in that the described method comprises the following steps：

S1：Tested scene is scanned by column by depth transducer, to obtain the depth information of the tested scene, by institute The depth information for stating tested scene carries out Coordinate Conversion, to obtain three-dimensional information of the tested scene under local coordinate；

S2：Ground point cloud is partitioned into from the three-dimensional information；

S3：Cluster segmentation is carried out to non-ground points cloud by the cluster segmentation mode of adaptive threshold, the non-ground points cloud is Other clouds in the three-dimensional information in addition to the ground point cloud,

Wherein, in step S2, ground point cloud is partitioned into from the three-dimensional information and is specifically included：

S201：To arrange the three-dimensional information is traveled through for unit, and using traverse one row as working as prostatitis；

S202：According to the direction traversal each point for working as prostatitis from bottom to up；

S203：Calculate the difference of the Z axis coordinate of the current point traversed and the ground level of the depth transducer position Absolute value, if the absolute value is less than height threshold, using the current point as first initial point Ps (1), performs step S204, otherwise return to step S 202；

S204：The cut off Pe (k) after initial point Ps (k) is detected, and the initial point Ps (k) and cut off Pe (k) are made For a pair；

S205：The initial point Ps (k+1) after cut off Pe (k) is detected, if detecting initial point Ps (k+1), k=k+1 is made, And return to step S204, otherwise by each pair initial point Ps (k) and cut off Pe (k) and each pair initial point Ps (k) and cut off Pe (k) point between performs step S206 as the ground point addition ground point cloud；

S206：Judge whether each row of the three-dimensional information are traversed to, if so, step S3 is then performed, otherwise will be current The next column of row as working as prostatitis, and return to step S202,

Wherein, the line and the angle of XOY plane formation of the cut off and next consecutive points are more than angle threshold.

2. the method as described in claim 1, it is characterised in that in step S3, passes through the cluster segmentation mode of adaptive threshold Cluster segmentation is carried out to non-ground points cloud to specifically include：

S301：Cluster segmentation is carried out to the non-ground points cloud using fixed initial threshold dth (0)；

S302：Each cluster that traversal cluster segmentation is obtained；

S303：The adaptive threshold dth (w) of the current cluster traversed is calculated according to the cluster scale of construction, the adaptive thresholding is judged Whether value dth (w) is less than the initial threshold dth (0), if so, then based on the adaptive threshold dth (w) to described current Cluster is further split；

S304：Judge whether each cluster that the cluster segmentation is acquired is traversed to, if it is not, then will currently cluster Next cluster is clustered as current, and return to step S303.

3. method as claimed in claim 2, it is characterised in that in step S303, according to working as that cluster scale of construction calculating is traversed The adaptive threshold dth (w) of preceding cluster is specifically included：

S3031：Each point in the current cluster is traveled through, the current point traversed is combined into its neighborhood point fit Plane, and The normal vector of the plane obtained will be fitted as the normal vector of the current point；

S3032：Calculate in the current cluster normal vector a little average value, and using the average value as described current The normal vector of cluster；

S3033：Institute in the current cluster is a little projected to projection plane, chooses and includes on the projection plane The minimum rectangle of all subpoints, the projection plane is the plane vertical with the normal vector currently clustered；

S3034：The adaptive threshold dth (w) currently clustered is calculated by following formula,

Dth (w)=k₁·L(w)+k₂

Wherein, L (w) is the bond length of the minimum rectangle, k₁And k₂For constant.

4. the method as described in claim 1, it is characterised in that in step S3, passes through the cluster segmentation mode of adaptive threshold Cluster segmentation is carried out to non-ground points cloud to specifically include：

S311：Each point in the non-ground points cloud is traveled through, the adaptive threshold of the current point traversed is calculated；

S312：Cluster segmentation is carried out to the non-ground points cloud according to the adaptive threshold each put in the non-ground points cloud.

5. method as claimed in claim 4, it is characterised in that in step S311, the current point traversed is calculated by following formula Adaptive threshold dth,

Wherein, L is the depth information of the current point, and A is the angular resolution of the depth transducer, and v is the depth sensing The translational speed of device in the horizontal direction, f is the scan frequency of the depth transducer, and α is the direction of plane and v where scan line Between angle, plane where the scan line is the central point of the depth transducer and the T row institute of the tested scene Plane, the T is classified as the row including the current point, and β is plane where scan line and measured object in tested scene The angle in body surface face, A_wThe effective angle scanned for depth transducer, Δ L is L error amount, and Δ v is v error amount, and Δ α is α error amount.

6. the method as described in claim 1, it is characterised in that after step S3, in addition to：

S4：Merged in the cluster that step S3 is obtained by the cluster of over-segmentation.

7. method as claimed in claim 6, it is characterised in that step S4 is specifically included：

S401：Each cluster that traversal cluster segmentation is acquired；

S402：Each point in the current cluster q that traverses is traveled through, the current point traversed is intended with reference to its neighborhood point Plane is closed, and the normal vector of the plane obtained will be fitted as the normal vector of the current point；

S403：Calculate in the current cluster q normal vector a little average value, and using the average value as described current The normal vector of cluster；

S404：Institute in the current cluster q is a little projected to projection plane, the projection of the current cluster q is calculated Area, the projection plane is the plane vertical with the normal vector of the current cluster q；

S405：Judge whether the projected area is more than preset area, if so, then gathering the current cluster q as the big scale of construction Class；

S406：Judge whether each cluster that cluster segmentation is acquired is traversed to, if so, then execution step S407, no Then the next cluster currently clustered is clustered as current, and return to step S402；

S407：If the spacing distance between two big scale of construction cluster is less than pre-determined distance, the corresponding two big scale of construction is clustered A cluster is merged into, until the spacing distance between the big scale of construction cluster of any two is all higher than the pre-determined distance, described two Spacing distance between individual big scale of construction cluster is the distance between nearest two points in two clusters.

8. method as claimed in claim 7, it is characterised in that also include after step S407：

S408：Each cluster that traversal step S407 is acquired；

S409：Calculate the difference DELTA H between the current cluster r traversed height and ground level, the height of the current cluster r Spend for the current cluster r somewhat middle Z axis coordinate minimum value, the ground level of the current cluster r is described current Cluster the maximum of Z axis coordinate in first initial point of the corresponding row of each point in r；

S410：Judge whether the difference DELTA H is more than preset difference value, if so, then clustering the current cluster r as suspending；

S411：Whether each cluster that judgment step S407 is acquired is traversed to, if so, then execution step S412, no Then the next cluster currently clustered is clustered as current, and return to step S409；

S412：By each suspension Cluster merging to the cluster closest with it, until each cluster that suspends is incorporated into one In the non-cluster that suspends.

9. a kind of point cloud segmentation device, it is characterised in that described device includes：

Scan conversion module, for being scanned by column by depth transducer to tested scene, to obtain the tested scene Depth information, the depth information of the tested scene is subjected to Coordinate Conversion, to obtain the tested scene in local coordinate system Three-dimensional information under system；

Module is split on ground, for being partitioned into ground point cloud from the three-dimensional information；

Module is split on non-ground, and cluster segmentation is carried out to non-ground points cloud for the cluster segmentation mode by adaptive threshold, The non-ground points cloud is other clouds in the three-dimensional information in addition to the ground point cloud,

Wherein, the ground segmentation module, specifically for：

S201：To arrange the three-dimensional information is traveled through for unit, and using traverse one row as working as prostatitis；

S202：According to the direction traversal each point for working as prostatitis from bottom to up；

S203：Calculate the difference of the Z axis coordinate of the current point traversed and the ground level of the depth transducer position Absolute value, if the absolute value is less than height threshold, using the current point as first initial point Ps (1), performs step S204, otherwise return to step S 202；

S204：The cut off Pe (k) after initial point Ps (k) is detected, and the initial point Ps (k) and cut off Pe (k) are made For a pair；

S205：The initial point Ps (k+1) after cut off Pe (k) is detected, if detecting initial point Ps (k+1), k=k+1 is made, And return to step S204, otherwise by each pair initial point Ps (k) and cut off Pe (k) and each pair initial point Ps (k) and cut off Pe (k) point between performs step S206 as the ground point addition ground point cloud；

S206：Judge whether each row of the three-dimensional information are traversed to, if so, then performing the non-ground segmentation mould Block, otherwise using when the next column in prostatitis is as working as prostatitis, and return to step S202,

Wherein, the line and the angle of XOY plane formation of the cut off and next consecutive points are more than angle threshold.