A luggage surrounding box processing method, electronic equipment and storage medium

Abstract

The application provides a luggage surrounding box processing method, an electronic device and a storage medium, relates to the technical field of luggage surrounding box processing, and the method comprises the following steps: obtaining each first surrounding box corresponding to a luggage area point cloud image to obtain a first surrounding box list EA, obtaining each second surrounding box corresponding to the luggage area point cloud image to obtain a second surrounding box list EB; the overlapping part and the non-overlapping part of the first surrounding box in EA and the second surrounding box in EB are split into third surrounding boxes to obtain a third surrounding box list BN; the Z-axis coordinate of each third surrounding box in the third surrounding box list BN is adjusted to the maximum Z-axis coordinate of the inner point cloud, so that the height of the third surrounding box is the same as the height of the corresponding luggage, and when the luggage is subsequently stored, the space of the luggage cart can be fully utilized, and the utilization rate of the space of the luggage cart is improved.

Description

Technical Field

[0001] This invention relates to the field of luggage wrapper processing technology, and in particular to a luggage wrapper processing method, electronic device, and storage medium. Background Technology

[0002] In the civil aviation sector, baggage handling is a major challenge for the modern aviation industry. As airport passenger traffic continues to increase, the amount of passenger baggage is also constantly rising. To improve baggage handling efficiency, some airports use automated baggage handling equipment to move baggage from conveyor belts to baggage carts, which are then transported to the aircraft for loading and check-in. Before the automated handling equipment moves baggage to the baggage carts, the space occupied by the baggage on the carts needs to be quantified. This quantification often uses a bounding box method to represent the baggage. Bounding boxes are typically rectangular in shape. Due to varying baggage heights, the height represented by the bounding box often differs significantly from the actual height of the baggage, resulting in low space utilization during subsequent baggage stacking. Summary of the Invention

[0003] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows:

[0004] According to a first aspect of this application, a method for processing a luggage wrap box is provided, the method comprising the following steps:

[0005] T100, obtain each first bounding box corresponding to the point cloud image of the luggage area, to obtain the first bounding box list EA = (EA1, EA2, ..., EA...). g , ..., EA h ), g = 1, 2, ..., h; where EA g Let h be the g-th first bounding box corresponding to the point cloud image of the baggage area, and h be the number of first bounding boxes corresponding to the point cloud image of the baggage area; the length of the first bounding box is along the Y-axis.

[0006] T200, obtain each second bounding box corresponding to the point cloud image of the baggage area, to obtain a list of second bounding boxes EB = (EB1, EB2, ..., EB...). u , ..., EB v ), u = 1, 2, ..., v; where EB u Let v be the u-th second bounding box corresponding to the point cloud image of the baggage area, and v be the number of second bounding boxes corresponding to the point cloud image of the baggage area; the length of the second bounding box is along the x-axis.

[0007] T300, split the overlapping and non-overlapping portions of the first bounding box in EA and the second bounding box in EB into third bounding boxes, to obtain a list of third bounding boxes BN = (BN1, BN2, ..., BN...).α , ..., BN β ), α=1, 2,...,β; among them, BN α Let α be the αth third bounding box obtained from the split, and β be the number of third bounding boxes obtained from the split.

[0008] T400, obtain the maximum Z-axis coordinate of the point cloud within each third bounding box in BN, to obtain the list of maximum Z-axis coordinates PA in BN. z =(PA) z，1 PA z，2 , ..., PA z，α , ..., PA z，β ); where PA z，α BN α Maximum Z-axis coordinate of the interior point cloud;

[0009] T500, BN α The Z-axis coordinate is adjusted to PA. z，α .

[0010] According to another aspect of this application, a non-transitory computer-readable storage medium is also provided, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or at least one program is loaded and executed by a processor to implement the above-described method for processing the luggage wrap box.

[0011] According to another aspect of this application, an electronic device is also provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0012] The present invention has at least the following beneficial effects:

[0013] The method for processing luggage bounding boxes of the present invention involves acquiring each first bounding box corresponding to a point cloud image of the luggage area to obtain a first bounding box list EA, acquiring each second bounding box corresponding to a point cloud image of the luggage area to obtain a second bounding box list EB; splitting the overlapping and non-overlapping portions of the first bounding boxes in EA and the second bounding boxes in EB into third bounding boxes to obtain a third bounding box list BN; adjusting the Z-axis coordinate of each third bounding box in the third bounding box list BN to obtain the maximum Z-axis coordinate of its internal point cloud; thereby making the height of the third bounding box the same as the height of the corresponding luggage, so that the space of the luggage cart can be fully utilized when the luggage is stacked, thereby improving the utilization rate of the luggage cart space. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating a method for processing a luggage wrap box according to an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] It should be noted that, based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Furthermore, this device and / or practice the method can be implemented using other structures and / or functionalities besides one or more of the aspects set forth herein.

[0018] Example 1:

[0019] Before generating the luggage wrap box, the luggage area needs to be identified. The following steps can be used to identify the luggage area on the luggage cart:

[0020] S100, obtain the initial point cloud coordinates of each initial point in the point cloud image corresponding to the luggage cart parking area, to obtain the initial point coordinate list A = (A1, A2, ..., A...). i A n ), i=1, 2,..., n; among them, A i Let be the initial coordinates of the i-th initial point corresponding to the luggage cart parking area, and n be the number of initial points in the point cloud image corresponding to the luggage cart parking area.

[0021] In this embodiment, the luggage carried by the robotic arm from the luggage conveyor needs to be stacked on the luggage cart; the luggage cart has a preset luggage cart parking area, and a point cloud image acquisition device, such as a lidar or depth camera, is installed above the luggage cart parking area; it can acquire point cloud images of the luggage cart parking area; it should be noted that the area occupied by the luggage cart parking area is larger than the actual area occupied by the luggage cart, so as to ensure that the complete image of the luggage cart can be captured.

[0022] S200, based on the preset coordinates of the luggage cart area, determine each intermediate point from all the initial points to obtain a list of intermediate point coordinates B = (B1, B2, ..., B...). j B m ), j = 1, 2, ..., m; where, B j To determine the j-th intermediate point, m is the number of intermediate points determined; B j = (B j，x B j，y B j，z ); B j，x Let B be the X-axis coordinate of the j-th intermediate point. j，y Let B be the y-coordinate of the j-th intermediate point. j，z Let be the Z-axis coordinate of the j-th intermediate point.

[0023] In this embodiment, step S200 may include the following steps:

[0024] S210, Obtain the maximum X-axis coordinate PB of the preset luggage cart area. x，max Minimum X-axis coordinate PB x，min Maximum Y-axis coordinate PB y，max and minimum Y-axis coordinate PB y，min .

[0025] After the luggage cart is parked in the preset position, the maximum X-axis coordinate, minimum X-axis coordinate, maximum Y-axis coordinate, and minimum Y-axis coordinate of the luggage cart are PB respectively. x，max PB x，min PB y，max and PB y，min .

[0026] S220, if PB x，min ≤B j，x ≤PB x，max And PB y，min ≤B j，y ≤PB y，max Then B j The midpoint was determined.

[0027] In this embodiment, the luggage cart parking area is larger than the actual area occupied by the luggage cart. Therefore, it is necessary to delete the point cloud outside the luggage cart in the point cloud image corresponding to the luggage cart parking area, and only retain the point cloud corresponding to the luggage cart, so as to reduce the amount of calculation for subsequent luggage area recognition and improve the calculation efficiency.

[0028] S300, iterate through B, if YZ min <|B j，z -PB z |<YZ max Then B j Identify the target points to obtain a list of target points C = (C1, C2, ..., C...). p C q ), p = 1, 2, ..., q; where C p Let p be the number of target points identified, and q be the number of target points identified; PB z YZ is the preset height of the luggage cart floor. min YZ is the preset first height difference threshold. max This is the preset second height difference threshold.

[0029] In this embodiment, PB z This can be determined through the following steps:

[0030] S310, obtain the initial height of each luggage cart to get a list of initial heights for the luggage carts: CA = (CA1, CA2, ..., CA2). c CA d ), c = 1, 2, ..., d; where, CA c Let d be the initial height of the c-th luggage cart, and d be the number of luggage carts.

[0031] Each baggage cart in the airport is designed with a preset height. However, the baggage carts will wear out during use, and their height will change to some extent. The height of each known baggage cart can be obtained by traversing through the carts to get the CA (Cost Assurance).

[0032] S320, Based on CA, determine the initial height volatility μ corresponding to CA = (1 / d) × ∑ d c＝1 (CA c -((1 / d)×∑ d c＝1 CA c )) 2 .

[0033] S330, if μ < μ', then determine PB. z = (1 / d)×∑ d c＝1 CA c.

[0034] In this embodiment, if μ is the variance corresponding to CA, the larger μ is, the higher the data variability within CA; the smaller μ is, the smaller the data variability within CA. If μ < μ', then PB can be directly determined using the mean of the initial height of the luggage cart in CA. z .

[0035] Furthermore, after step S330, the method further includes the following steps:

[0036] S340, if μ≥μ', then delete the largest and smallest initial heights of several luggage carts in CA.

[0037] S350, the average initial height of the remaining luggage carts in CA is determined as PB. z .

[0038] If μ ≥ μ', it indicates that the data in CA has significant differences. In this case, using the average initial height of the luggage carts in CA is no longer appropriate. Therefore, the larger and smaller initial heights of the luggage carts in CA are deleted, and the data with smaller differences are retained. Then, the average of the remaining initial heights of the luggage carts in CA is determined as PB. z To improve PB z The established rationale further improves the accuracy of subsequent baggage area identification.

[0039] S400, according to C, determine the baggage area.

[0040] In this embodiment, step S400 may include the following steps:

[0041] S410, using a preset clustering algorithm, cluster the target points in C to obtain a cluster list D = (D1, D2, ..., D...). a D b ), a = 1, 2, ..., b; where, D a Let b be the a-th cluster obtained by clustering the target points in C, and let b be the number of clusters obtained by clustering the target points in C.

[0042] In this embodiment, the preset clustering algorithm can be the DBSCAN clustering algorithm. During the clustering process, clustering can be performed using the clustering between target points, which can cluster target points with adjacent distribution locations in C into a single cluster.

[0043] S420, obtain the volume of the point cloud corresponding to all target points in each cluster of D, so as to obtain the point cloud volume list DA = (DA1, DA2, ..., DA2). a , ..., DA b ); where DA a Da The volume of the point cloud corresponding to all target points within the area.

[0044] In this embodiment, the volume of the point cloud corresponding to all target points in each cluster can be represented by the minimum bounding box. It should be noted that those skilled in the art can use existing minimum bounding box determination methods to determine the minimum bounding box of the point cloud corresponding to all target points in each cluster according to actual needs, thereby obtaining the corresponding volume. This will not be elaborated here.

[0045] S430, if DA a <QA, then D a All target points within the scope are deleted; where QA is the preset minimum luggage volume threshold.

[0046] In this embodiment, under normal circumstances, luggage has a minimum volume; for the civil aviation industry, a user's luggage cannot be too small. Therefore, if DA a If QA is not met, then D can be determined. a The target points within are noise points, and D needs to be removed. a Target points within the area are deleted to avoid misidentifying noise as luggage areas, thereby improving the accuracy of subsequent luggage area identification.

[0047] S440, the point cloud region corresponding to the remaining target points in C is determined as the baggage area.

[0048] In this embodiment, the initial point cloud coordinates of each initial point in the point cloud image corresponding to the luggage cart parking area are obtained; each intermediate point is determined from all the initial points according to the preset coordinates of the luggage cart area; each target point corresponding to the luggage is determined according to the height difference between the intermediate point and the luggage cart floor, and then the luggage area is further determined according to each target point; the method in this invention is based on the point cloud image corresponding to the luggage cart parking area, and the amount of computation is small in the entire luggage area recognition process, so the recognition efficiency of the luggage area is high.

[0049] In addition, the baggage area recognition method of the present invention can obtain the point cloud coordinates of each initial point in the point cloud image of the baggage cart parking area through LiDAR, and the accuracy of the point cloud coordinates is very high; therefore, the accuracy of the baggage area subsequently identified is also high.

[0050] Example 2:

[0051] After determining the baggage area, you can quantify the baggage area using the following steps to facilitate the subsequent stacking of baggage:

[0052] Q100: Obtain the point cloud image of the luggage area corresponding to the luggage area on the luggage cart; wherein, the point cloud image of the luggage area includes several points, and each point corresponds to coordinate information.

[0053] In this embodiment, the point cloud image of the luggage area corresponding to the luggage area on the luggage cart can be obtained through the method steps in Embodiment 1, which will not be repeated here.

[0054] Q200, perform average cropping on the point cloud image of the luggage area along the X-axis to obtain the first sub-bounding box list WA = (WA1, WA2, ..., WA2). e , ..., WA f ), e=1, 2,..., f; among them, WA e Let f be the e-th first sub-bounding box obtained by averaging the point cloud image of the baggage area along the X-axis, and let f be the number of first sub-bounding boxes obtained by averaging the point cloud image of the baggage area along the X-axis; the first sub-bounding boxes in WA are sequentially adjacent.

[0055] Furthermore, step Q200 may include the following steps:

[0056] Q210, Obtain the maximum X-axis coordinate of a point in the point cloud image of the luggage area. x，max Minimum X-axis coordinate QR x，min Maximum Y-axis coordinate QR y，max and minimum Y-axis coordinate QR y，min .

[0057] In this embodiment, the coordinates of every point in the baggage area point cloud image can be obtained. Therefore, the maximum X-axis coordinate QR of the point in the baggage area point cloud image can be obtained. x，max Minimum X-axis coordinate QR x，min Maximum Y-axis coordinate QR y，max and minimum Y-axis coordinate QR y，min .

[0058] Q220, according to QR y，max and QR y，min Determine the initial length of the first sub-boundary box along the Y-axis: LR = QR y，max -QR y，min and according to QR x，max and QR x，min Determine the initial width DR of the first sub-boundary box along the X-axis as DR = (QR) x，max -QR x，min ) / f.

[0059] In this embodiment, when cutting along the X-axis, it is necessary to determine the length of the first sub-boundary box in the Y-axis direction. Using LR as the length in the Y-axis direction, the point cloud image of the luggage area is cut, which can cut the entire area of ​​the point cloud image of the luggage area in the Y-axis direction.

[0060] Q230, using the initial first sub-boundary box from QRx，min The point cloud image of the luggage area is sequentially segmented along the X-axis to obtain an initial first sub-bounding box list WA' = (WA'1, WA'2, ..., WA'). e , ...,WA' f ); among them, WA' e Let e ​​be the initial first sub-bounding box obtained.

[0061] It can be understood that if the luggage is not arranged in a regular pattern, for example, in an L-shape, then during the cutting process, there may be a large space without point clouds in the Y-axis direction.

[0062] Q240, Get WA' e Maximum Y-axis coordinate GH of the interior point cloud e y，max Minimum Y-axis coordinate GH e y，min and the maximum Z-axis coordinate GH e z，max .

[0063] Q250, WA' e The maximum Y-axis coordinate is adjusted to GH e y，max The minimum Y-axis coordinate is adjusted to GH. e y，min Adjust the maximum Z-axis coordinate to GH e z，max In order to obtain WA e .

[0064] In this embodiment, steps Q240 and Q250 enable the first sub-bounding box to be closely attached to the corresponding point cloud, avoiding the situation where the generated first sub-bounding box is too large, resulting in wasted space in the luggage cart, thereby improving the space utilization rate of the luggage cart.

[0065] Q300, merge several adjacent first sub-bounding boxes in WA that meet the preset merging conditions into a first bounding box list EA = (EA1, EA2, ..., EA300) to obtain the first bounding box list corresponding to the baggage area. g , ..., EA h ), g = 1, 2, ..., h; where EA g Let h be the g-th first bounding box obtained by merging the first sub-bounding boxes, and h be the number of first bounding boxes obtained by merging the first sub-bounding boxes.

[0066] Furthermore, step Q300 may include the following steps:

[0067] Q310, obtain the first preset value BH=1 and the second preset value HU=1.

[0068] Q320, if |LE BH -LE BH+1 |≤LE'1、|KD BH -KD BH+1 |≤LE'2 and|HD BH -HD BH+1 |≤LE'3, then WA BH and WA BH+1 If the first sub-bounding box to be merged is identified, and BH = BH + 1 is obtained, proceed to Q320; otherwise, proceed to Q330; where LE BH for WA BH Length, KD BH for WA BH Width, HD BH for WA BH The height; LE'1, LE'2 and LE'3 are the preset length difference threshold, width difference threshold and height difference threshold, respectively.

[0069] Q330 merges each first sub-boundary box to be merged into EA. HU ; and obtain HU = HU + 1; enter Q320.

[0070] In this embodiment, LE'1, LE'2 and LE'3 are empirical values ​​that can be obtained based on a large amount of experimental data in actual application. Through steps Q310-Q330, several first sub-enclosing boxes of similar size in the first sub-enclosing box can be merged into one first enclosing box. When stacking luggage in the future, the number of calculations of the enclosing box can be reduced and the stacking efficiency can be improved.

[0071] Furthermore, EA HU The length is the maximum length of the corresponding first sub-bounding box to be merged, EA HU The height is the maximum height of the corresponding first sub-boundary box to be merged, EA HU The width of the first bounding box to be merged is the sum of the widths of the corresponding first bounding boxes; thus, the merged first bounding box can completely enclose the point cloud of the corresponding luggage area, preventing the point cloud from appearing outside the first bounding box.

[0072] Q400, perform average cropping on the point cloud image of the luggage area along the Y-axis to obtain the second sub-bounding box list WB = (WB1, WB2, ..., WB...). k WB r ), k = 1, 2, ..., r; where WB k The second sub-bounded box is the kth sub-bounded box obtained by averaging the point cloud image of the baggage area along the Y-axis, and r is the number of second sub-bounded boxes obtained by averaging the point cloud image of the baggage area along the Y-axis; the second sub-bounded boxes in WB are sequentially adjacent.

[0073] Furthermore, step Q400 may include the following steps:

[0074] Q410, Obtain the maximum X-axis coordinate of a point in the point cloud image of the luggage area. x，max Minimum X-axis coordinate QR x，min Maximum Y-axis coordinate QR y，max and minimum Y-axis coordinate QR y，min .

[0075] Q420, according to QR x，max and QR x，min Determine the length of the initial second sub-boundary box along the X-axis: LT = QR x，max -QR x，min and according to QR y，max and QR y，min Determine the initial width DR of the second sub-boundary box along the Y-axis: DR = (QR) y，max -QR y，min ) / r.

[0076] Q430, using the initial second sub-boundary box from QR y，min The point cloud image of the luggage area is sequentially segmented along the Y-axis to obtain an initial second sub-bounding box list WB' = (WB'1, WB'2, ..., WB'). k , ..., WB' r ); where WB' k This is the kth initial second sub-bounding box obtained.

[0077] Q440, get WB' k The maximum Y-axis coordinate EH of the interior point cloud e y，max Minimum Y-axis coordinate EH e y，min and the maximum Z-axis coordinate EH e z，max .

[0078] Q450, WB' k The maximum Y-axis coordinate is adjusted to EH e y，max The minimum Y-axis coordinate is adjusted to EH. e y，min And the maximum Z-axis coordinate is adjusted to EH e z，max To obtain WB k .

[0079] In this embodiment, the segmentation of the point cloud image of the luggage area along the Y-axis is the same as the segmentation method of the point cloud image of the luggage area along the X-axis described above, and will not be repeated here.

[0080] Q500, merge several adjacent second sub-bounding boxes in WB that meet the preset merging conditions into a second bounding box to obtain the second bounding box list EB = (EB1, EB2, ..., EB500) corresponding to the baggage area. u , ..., EB v ), u = 1, 2, ..., v; where EB u Let v be the u-th second bounding box obtained by merging the second sub-bounding boxes, and v be the number of second bounding boxes obtained by merging the second sub-bounding boxes.

[0081] Furthermore, step Q500 may include the following steps:

[0082] Q510, obtain the third preset value TY=1 and the fourth preset value RU=1.

[0083] Q520, if |ME TY -ME TY+1 |≤LE'1、|ND TY -ND TY+1 |≤LE'2 and|FD TY -FD TY+1 If |≤LE'3, then WB TY and WB TY+1 If the second sub-bounding box is identified as to be merged, and TY = TY + 1 is obtained, proceed to Q520; otherwise, proceed to Q530; where ME TY For WB TY Length, ND TY For WB TY Width, FD TY For WB TY The height; LE'1, LE'2 and LE'3 are the preset length difference threshold, width difference threshold and height difference threshold, respectively.

[0084] Q530 merges each second sub-boundary box to be merged into EB. RU ; and obtain RU = RU + 1; enter Q520.

[0085] Through steps Q510-Q530, several second sub-enclosing boxes of similar size can be merged into one second enclosing box. This reduces the number of enclosing box calculations and improves stacking efficiency when packing luggage.

[0086] Furthermore, EB RU The length is the maximum length of the corresponding second sub-bounding box to be merged, EB RU The height is the maximum height of the corresponding second sub-boundary box to be merged, EB RUThe width of the bounding box is the sum of the widths of the corresponding second bounding boxes to be merged; thus, the merged second bounding box can completely enclose the point cloud of the corresponding luggage area, preventing the point cloud from appearing outside the second bounding box.

[0087] In this embodiment, a point cloud image of the luggage area corresponding to the luggage area on the luggage cart is acquired; the point cloud image of the luggage area corresponding to the luggage area is segmented along the X-axis and Y-axis respectively to obtain a first sub-bounding box list WA and a second sub-bounding box list WB; several adjacent first sub-bounding boxes in WA that meet the preset merging conditions are merged into a first bounding box to obtain a first bounding box list EA corresponding to the luggage area; several adjacent second sub-bounding boxes in WB that meet the preset merging conditions are merged into a second bounding box to obtain a second bounding box list EB corresponding to the luggage area; both the first bounding box and the second bounding box correspond to specific position and size information, thereby achieving accurate quantization of luggage on the X-axis and Y-axis.

[0088] In addition, the method of this embodiment can not only obtain the bounding box of the luggage area on the X-axis, but also the bounding box of the luggage area on the Y-axis. When stacking luggage, the bounding box on the X-axis or the bounding box on the Y-axis can be used according to the position and orientation of the luggage.

[0089] Example 3:

[0090] In Example 2, the height of the luggage cart's floor was not considered when generating the luggage enclosure; to improve the space utilization of the luggage cart, it can be achieved through methods such as... Figure 1 The method shown for handling the luggage compartment is to remove the luggage cart floor panel:

[0091] T100, obtain each first bounding box corresponding to the point cloud image of the luggage area, to obtain the first bounding box list EA = (EA1, EA2, ..., EA...). g , ..., EA h ), g = 1, 2, ..., h; where EA g Let h be the g-th first bounding box corresponding to the point cloud image of the baggage area, and h be the number of first bounding boxes corresponding to the point cloud image of the baggage area; the length of the first bounding box is along the Y-axis.

[0092] Furthermore, EA can be obtained through the following steps:

[0093] T110 acquires the point cloud image of the luggage area corresponding to the luggage area on the luggage cart; wherein, the luggage area point cloud image includes several points, each of which has coordinate information.

[0094] In this embodiment, the luggage area on the luggage cart can be obtained through the method and steps in Embodiment 1, which will not be repeated here.

[0095] T120, the point cloud image of the luggage area is averaged along the X-axis to obtain the first sub-bounding box list WA = (WA1, WA2, ..., WA...). e , ..., WA f ), e=1, 2,..., f; among them, WA e Let f be the e-th first sub-bounding box obtained by averaging the point cloud image of the baggage area along the X-axis, and let f be the number of first sub-bounding boxes obtained by averaging the point cloud image of the baggage area along the X-axis; the first sub-bounding boxes in WA are sequentially adjacent.

[0096] In this embodiment, T120 may include the following steps:

[0097] T121, Obtain the maximum X-axis coordinate QR of the point cloud image of the luggage area. x，max Minimum X-axis coordinate QR x，min Maximum Y-axis coordinate QR y，max and minimum Y-axis coordinate QR y，min .

[0098] In this embodiment, the coordinates of every point in the baggage area point cloud image can be obtained. Therefore, the maximum X-axis coordinate QR of the point in the baggage area point cloud image can be obtained. x，max Minimum X-axis coordinate QR x，min Maximum Y-axis coordinate QR y，max and minimum Y-axis coordinate QR y，min .

[0099] T122, according to QR y，max and QR y，min Determine the initial length of the first sub-boundary box along the Y-axis: LR = QR y，max -QR y，min and according to QR x，max and QR x，min Determine the initial width DR of the first sub-boundary box along the X-axis as DR = (QR) x，max -QR x，min ) / f.

[0100] In this embodiment, when cutting along the X-axis, it is necessary to determine the length of the first sub-boundary box in the Y-axis direction. Using LR as the length in the Y-axis direction, the point cloud image of the luggage area is cut, which can cut the entire area of ​​the point cloud image of the luggage area in the Y-axis direction.

[0101] T123, using the initial first sub-boundary box from QR x，min The point cloud image of the luggage area is sequentially segmented along the X-axis to obtain an initial first sub-bounding box list WA' = (WA'1, WA'2, ..., WA'). e, ...,WA' f ); among them, WA' e Let e ​​be the initial first sub-bounding box obtained.

[0102] It can be understood that if the luggage is not arranged in a regular pattern, for example, in an L-shape, then during the cutting process, there may be a large space without point clouds in the Y-axis direction.

[0103] T124, Get WA' e Maximum Y-axis coordinate GH of the interior point cloud e y，max Minimum Y-axis coordinate GH e y，min and the maximum Z-axis coordinate GH e z，max .

[0104] T125, will WA' e The maximum Y-axis coordinate is adjusted to GH e y，max The minimum Y-axis coordinate is adjusted to GH. e y，min Adjust the maximum Z-axis coordinate to GH e z，max In order to obtain WA e .

[0105] In this embodiment, steps Q240 and Q250 enable the first sub-bounding box to be closely attached to the corresponding point cloud, avoiding the situation where the generated first sub-bounding box is too large, resulting in wasted space in the luggage cart, thereby improving the space utilization rate of the luggage cart.

[0106] T130 merges several adjacent first sub-bounding boxes in WA that meet the preset merging conditions into a first bounding box to obtain the first bounding box list EA corresponding to the baggage area.

[0107] Furthermore, step T130 may include the following steps:

[0108] T131, obtain the first preset value BH=1 and the second preset value HU=1.

[0109] T132, if |LE BH -LE BH+1 |≤LE'1、|KD BH -KD BH+1 |≤LE'2 and|HD BH -HD BH+1 |≤LE'3, then WA BH and WA BH+1If the first sub-bounding box to be merged is identified, and BH = BH + 1 is obtained, proceed to T132; otherwise, proceed to T133; where LE BH for WA BH Length, KD BH for WA BH Width, HD BH for WA BH The height; LE'1, LE'2 and LE'3 are the preset length difference threshold, width difference threshold and height difference threshold, respectively.

[0110] T133 merges each first sub-boundary box to be merged into EA. HU ; and obtain HU = HU + 1; enter T132.

[0111] In this embodiment, LE'1, LE'2 and LE'3 are empirical values ​​that can be obtained based on a large amount of experimental data in actual application. Through steps T131-T133, several first sub-enclosing boxes of similar size in the first sub-enclosing box can be merged into one first enclosing box. When stacking luggage in the future, the number of calculations of the enclosing box can be reduced and the stacking efficiency can be improved.

[0112] Furthermore, EA HU The length is the maximum length of the corresponding first sub-bounding box to be merged, EA HU The height is the maximum height of the corresponding first sub-boundary box to be merged, EA HU The width of the first bounding box to be merged is the sum of the widths of the corresponding first bounding boxes; thus, the merged first bounding box can completely enclose the point cloud of the corresponding luggage area, preventing the point cloud from appearing outside the first bounding box.

[0113] T200, obtain each second bounding box corresponding to the point cloud image of the baggage area, to obtain a list of second bounding boxes EB = (EB1, EB2, ..., EB...). u , ..., EB v ), u = 1, 2, ..., v; where EB u Let v be the u-th second bounding box corresponding to the point cloud image of the baggage area, and v be the number of second bounding boxes corresponding to the point cloud image of the baggage area; the length of the second bounding box is along the x-axis.

[0114] EB is obtained through the following steps:

[0115] T210, the point cloud image of the luggage area is averaged along the Y-axis to obtain the second sub-bounding box list WB = (WB1, WB2, ..., WB...). k WB r ), k = 1, 2, ..., r; where WB kThe second sub-bounded box is the kth sub-bounded box obtained by averaging the point cloud image of the baggage area along the Y-axis, and r is the number of second sub-bounded boxes obtained by averaging the point cloud image of the baggage area along the Y-axis; the second sub-bounded boxes in WB are sequentially adjacent.

[0116] T220 merges several adjacent second sub-bounding boxes in WB that meet the preset merging conditions into a second bounding box to obtain the second bounding box list EB corresponding to the baggage area.

[0117] In this embodiment, the method for obtaining EB is the same as that for obtaining EA, and will not be described in detail here.

[0118] T300, split the overlapping and non-overlapping portions of the first bounding box in EA and the second bounding box in EB into third bounding boxes, to obtain a list of third bounding boxes BN = (BN1, BN2, ..., BN...). α , ..., BN β ), α=1, 2,...,β; among them, BN α Let α be the αth third bounding box obtained from the split, and β be the number of third bounding boxes obtained from the split.

[0119] In this embodiment, it can be understood that the first bounding box is obtained by cutting along the X-axis direction and the second bounding box is obtained by cutting along the Y-axis direction. When luggage is stacked, it may not be stacked into a rectangle, but may be L-shaped, and the height of the luggage may be different. Therefore, there may be spaces in the first bounding box and the second bounding box that do not have point clouds. It is necessary to separate the first bounding box and the second bounding box to further remove the bounding box without point clouds.

[0120] Furthermore, step T300 may include the following steps:

[0121] T310, obtain the fifth preset value WF = 1.

[0122] T311, obtain the sixth preset value WK = 1.

[0123] T312, if EA WF With EB WF+WK-1 If there is overlap, then EA will be... WF With EB WF+WK-1 The overlapping and non-overlapping parts are defined as the third bounding box; otherwise, EA is defined as... WF With EB WF+WK-1 It has been identified as the third enclosing box.

[0124] T313, if WK < v, then obtain WK = WK + 1; proceed to T312; otherwise, proceed to T314.

[0125] In T314, if WF < h, then obtain WF = WF + 1; proceed to T311; otherwise, exit the current processing.

[0126] Through the above steps T310-T314, the overlapping part of the first enclosing box and the non-overlapping part can be separated into a third enclosing box.

[0127] Following step T314, the method further includes the following steps:

[0128] T315, traverse all third bounding boxes, delete the third bounding boxes that do not contain point clouds inside, to obtain BN.

[0129] In this embodiment, the third bounding box may not contain point clouds at all. The third bounding box without point clouds is an empty bounding box, which does not contain luggage. Therefore, it needs to be deleted to free up unused space in the luggage cart and improve space utilization.

[0130] T400, obtain the maximum Z-axis coordinate of the point cloud within each third bounding box in BN, to obtain the list of maximum Z-axis coordinates PA in BN. z =(PA) z，1 PA z，2 , ..., PA z，α , ..., PA z，β ); where PA z，α BN α Maximum Z-axis coordinate of the interior point cloud;

[0131] T500, BN α The Z-axis coordinate is adjusted to PA. z，α .

[0132] In this embodiment, both the first and second sub-enclosing boxes are rectangular parallelepipeds. The first and second enclosing boxes are relatively large, and their height is determined based on the maximum height of the corresponding luggage. If the upper surface of the corresponding luggage is not flat but convex, then the corresponding first or second enclosing box will also have a large space without luggage. Therefore, in order to make the third enclosing box fit more closely to the point cloud corresponding to the luggage, the Z-axis coordinate of the third enclosing box is adjusted to the maximum Z-axis coordinate of its internal point cloud, so that the third enclosing box can fit closely to the corresponding point cloud and improve the space utilization of the luggage cart.

[0133] The luggage bounding box processing method of this embodiment involves obtaining each first bounding box corresponding to the luggage area point cloud image to obtain a first bounding box list EA, and obtaining each second bounding box corresponding to the luggage area point cloud image to obtain a second bounding box list EB. The overlapping and non-overlapping parts of the first bounding boxes in EA and the second bounding boxes in EB are split into third bounding boxes to obtain a third bounding box list BN. The Z-axis coordinate of each third bounding box in the third bounding box list BN is adjusted to the maximum Z-axis coordinate obtained from its internal point cloud. This makes the height of the third bounding box the same as the height of the corresponding luggage, so that the space of the luggage cart can be fully utilized when the luggage is stacked, thereby improving the utilization rate of the luggage cart space.

[0134] Example 4:

[0135] When there is no luggage stored on the luggage cart, the following steps can be used to determine the luggage area, thereby improving the efficiency of luggage area determination:

[0136] H100 determines whether there is luggage on the luggage cart.

[0137] In this embodiment, when the luggage cart arrives at the luggage cart parking area, there may or may not be luggage on the luggage cart. First, it is determined whether there is luggage on the luggage cart. Specifically, this can be determined through the following steps:

[0138] H111, obtain the Z-axis coordinate of each initial pixel corresponding to the initial image of the luggage cart.

[0139] H112, if the volatility of the Z-axis coordinates of all initial pixels is less than the preset volatility threshold, then it is determined that there is no luggage on the luggage cart; otherwise, it is determined that there is luggage on the luggage cart.

[0140] In this embodiment, the volatility of the Z-axis coordinates of all initial pixels can be the variance of the Z-axis coordinates of all initial pixels. It can be understood that when there is no luggage on the luggage cart, the bottom of the luggage cart is flat, and the Z-axis coordinates of the pixels corresponding to the bottom are equal or have very small differences. When the volatility of the Z-axis coordinates of all initial pixels is greater than the preset volatility threshold, it means that the Z-axis coordinates of all initial pixels fluctuate greatly, indicating that there is luggage on the luggage cart.

[0141] H200: If there is no luggage on the luggage cart, then obtain the first luggage cart image corresponding to the luggage cart.

[0142] In this embodiment, the first luggage cart image can be acquired by an industrial camera. It should be noted that the industrial camera has a camera coordinate system and the luggage cart has a luggage cart coordinate system. The two can be transformed to determine the coordinate information of the pixels in the first luggage cart image.

[0143] Furthermore, if there is luggage on the luggage cart, the spatial area occupied by the luggage on the luggage cart is determined by the point cloud image corresponding to the luggage cart.

[0144] In this embodiment, the method and steps in Embodiment 1 can be used to determine the space occupied by the current luggage on the luggage cart; this will not be elaborated here.

[0145] H300 moves the current baggage code to the origin position of the baggage cart and acquires a second baggage cart image; wherein, both the first baggage cart image and the second baggage cart image are two-dimensional images.

[0146] In this embodiment, when there is no luggage on the luggage cart, the luggage handling system will place the current luggage at the origin position of the luggage cart, for example, the center position of the luggage cart.

[0147] H400, obtain the coordinates of each pixel corresponding to the first luggage cart image and the coordinates of each pixel corresponding to the second luggage cart image.

[0148] In this embodiment, a camera is installed at a preset position above the luggage cart. The industrial camera corresponds to a known camera coordinate system, and the luggage cart corresponds to a known luggage cart coordinate system. The two can be transformed to determine the coordinate information of the pixels in the first luggage cart image and the second luggage cart image. It should be noted that those skilled in the art can use existing coordinate transformation methods to obtain the coordinates of each pixel in the first luggage cart image and the coordinates of each pixel in the second luggage cart image according to actual needs, which will not be elaborated here.

[0149] H500 determines the space occupied by the current luggage on the luggage cart based on the coordinates of each pixel corresponding to the first luggage cart image and the coordinates of each pixel corresponding to the second luggage cart image.

[0150] Furthermore, step H500 may include the following steps:

[0151] H510, the first luggage cart image is divided into several adjacent image regions to be compared, so as to obtain a list of the first image regions to be compared UA = (UA1, UA2, ..., UA2). θ , ..., UA ω ), θ=1, 2,...,ω; among them, UA θ Let θ be the first image region to be compared obtained by dividing the first baggage image, and ω be the number of first image regions to be compared obtained by dividing the first baggage image.

[0152] H520, the second luggage cart image is divided into several adjacent image regions to be compared, to obtain a list of second image regions to be compared UB = (UB1, UB2, ..., UB2).θ , ..., UB ω ); where UB θ The θ-th second image region to be compared is obtained by dividing the second baggage image; UA θ and UB θ The same area corresponding to the luggage cart.

[0153] In this embodiment, the first luggage cart image and the second luggage cart image can be divided into several rectangular image regions to be compared, and the size of each image region to be compared can be determined according to actual needs.

[0154] H530, obtain the first target height coordinates of each first image region to be compared in UA, to obtain the first target height coordinate list UC = (UC1, UC2, ..., UC...). θ , ..., UC ω ); among them, UC θ For UA θ First target altitude coordinates; UC θ via UA θ The Z-axis coordinates of several pixels within the range are obtained.

[0155] Furthermore, UC θ Determined through the following steps:

[0156] H531, Get User Agent θ The Z-axis coordinates of each preset pixel position within the array are used to obtain the UA. θ The corresponding preset position pixel coordinate list ZP θ =(ZP θ，1 ZP θ，2 ..., ZP θ，γ ..., ZP θ，δ ), γ=1, 2,..., δ; where, ZP θ，γ For UA θ The Z-axis coordinate of the γth preset position pixel, where δ is UA. θ The number of pixels at preset internal positions.

[0157] H532, according to ZP θ , determine UC θ = (1 / δ)×∑ δ γ＝1 ZP θ，γ .

[0158] In this embodiment, the preset position can be UA. θ The four vertices, the center point, and the pixel corresponding to the midpoint of each side; able to obtain the UA θ The Z-axis coordinate of each preset pixel position within the range.

[0159] H540, obtain the second target height coordinates for each second image region to be compared in UB, to obtain a list of second target height coordinates UD = (UD1, UD2, ..., UD40). θ , ..., UD ω ); where UD θ For UB θ The second target's altitude coordinates; UD θ via UB θ The Z-axis coordinates of several pixels within the range are obtained.

[0160] In this embodiment, UD θ The determination method and UC θ The method for determining UA is the same; it should be noted that UA θ Each preset position pixel and UB θ Each preset position pixel is identical.

[0161] H550, based on UC and UD, determines the space area currently occupied by the luggage on the luggage cart.

[0162] Furthermore, step H550 may include the following steps:

[0163] H551, iterate through UC and UD, if |UC θ -UD θ |>SG, then UC θ The first image region to be compared was identified as the target, and the UD was... θ The second image region to be compared is identified as the target; where SG is the preset target height difference threshold.

[0164] In this embodiment, if |UC θ -UD θ |>SG, representing UA θ With UB θ If the Z-axis coordinate of a pixel at a preset position within the area changes significantly, the UC can be determined. θ The corresponding luggage cart area contains newly stacked luggage; thus, areas where no new luggage has been stacked can be filtered out. When filtering most areas where no new luggage has been stacked, only a portion of the pixels are used, resulting in minimal computation and significantly improving image processing efficiency.

[0165] H552, obtain each pixel within the first image region to be compared for each target, to obtain a list of first pixels JC = (JC1, JC2, ..., JC...). ε JC σ ), ε=1, 2,...,σ; among them, JC εLet ε be the ε-th pixel within the overall region corresponding to the first image region to be compared for all targets, and σ be the number of pixels within the overall region corresponding to the first image region to be compared for all targets.

[0166] H553, obtain each pixel within the second image region to be compared for each target, to obtain a second pixel list JD = (JD1, JD2, ..., JD...). ε ..., JD σ ); among them, JD ε JC represents the ε-th pixel within the overall region corresponding to the second image region to be compared for all targets; ε With JD ε The corresponding luggage carts have the same location.

[0167] H554, if |JC ε _Z-JD ε If _Z|>UZ, then JD will be... ε The target pixel is identified; where JC ε _Z is JC ε Z-axis coordinate, JD ε _Z is JD ε The Z-axis coordinate; UZ is the preset threshold for the difference in Z-axis coordinates of pixels.

[0168] By following the steps above, all pixels whose Z-coordinates have changed significantly can be identified.

[0169] H555 defines the spatial region formed by all target pixels as the current space occupied by the luggage on the luggage cart.

[0170] In this embodiment, it is determined whether there is luggage on the luggage cart; if there is no luggage on the luggage cart, a first luggage cart image corresponding to the luggage cart is obtained; the current luggage code is moved to the origin position of the luggage cart, and a second luggage cart image is obtained; the coordinates of each pixel corresponding to the first luggage cart image and the coordinates of each pixel corresponding to the second luggage cart image are obtained; based on the coordinates of each pixel corresponding to the first luggage cart image and the coordinates of each pixel corresponding to the second luggage cart image, the spatial area occupied by the current luggage on the luggage cart is determined; in this invention, when identifying the spatial area of ​​the luggage, a two-dimensional image corresponding to the luggage cart is used, and only the coordinates of the pixels in the two-dimensional image are processed, resulting in a small amount of computation and high efficiency and accuracy in luggage area recognition.

[0171] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0172] Embodiments of the present invention also provide a non-transitory computer-readable storage medium that can be disposed in an electronic device to store at least one instruction or at least one program related to implementing a method in the method embodiments, wherein the at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiments.

[0173] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0174] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0175] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0176] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0177] Embodiments of the present invention also provide an electronic device, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0178] The electronic device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments in this application.

[0179] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and a bus connecting different system components (including memory and processor).

[0180] The memory stores program code that can be executed by the processor, causing the processor to perform the steps in the various embodiments described in this specification.

[0181] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0182] The memory may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0183] A bus can represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus structures.

[0184] The electronic device can also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be performed via input / output (I / O) interfaces. Furthermore, the electronic device can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0185] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0186] Embodiments of the present invention also provide a computer program product including program code, which, when the program product is run on an electronic device, causes the electronic device to perform the steps of the methods described above in various exemplary embodiments of the present invention.

[0187] While specific embodiments of the invention have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the invention.

Claims

1. A method for processing a luggage wrap box, characterized in that, The method includes the following steps: S100, obtain the initial point cloud coordinates of each initial point in the point cloud image corresponding to the luggage cart parking area, to obtain the initial point coordinate list A = (A1, A2, ..., A...). i A n ), i=1, 2,...,n; among them, A i Let be the initial coordinates of the i-th initial point corresponding to the luggage cart parking area, and n be the number of initial points in the point cloud image corresponding to the luggage cart parking area. S200, based on the preset coordinates of the luggage cart area, determine each intermediate point from all the initial points to obtain a list of intermediate point coordinates B = (B1, B2, ..., B...). j B m ), j=1,2,…,m; where, B j To determine the j-th intermediate point, m is the number of intermediate points determined; B j = (B j，x B j，y B j，z ); B j，x Let B be the X-axis coordinate of the j-th intermediate point. j，y Let B be the y-coordinate of the j-th intermediate point. j，z Let j be the Z-axis coordinate of the j-th intermediate point; S300, iterate through B, if YZ min <|B j，z -PB z |<YZ max Then B j The target points are identified to obtain a list of target points C = (C1, C2, ..., C...). p C q ), p=1,2,…,q; where C p Let PB be the number of target points determined, where q represents the p-th target point. z YZ is the preset height of the luggage cart floor. min YZ is the preset first height difference threshold. max The second height difference threshold is preset; S400, according to C, determine the baggage area; PB z Determined through the following steps: S310, obtain the initial height of each luggage cart to get a list of initial heights for the luggage carts: CA = (CA1, CA2, ..., CA2). c CA d ), c=1,2,…,d; where, CA c Let d be the initial height of the c-th luggage cart, and d be the number of luggage carts. S320, Based on CA, determine the initial height volatility μ corresponding to CA = (1 / d) × ∑ d c=1 (CA) c -(1 / d)×∑ d c=1 CA c )) 2 ; S330, if μ < μ', then determine PB. z =(1 / d)×∑ d c=1 CA c ; T100, obtain each first bounding box corresponding to the point cloud image of the luggage area, to obtain the first bounding box list EA = (EA1, EA2, ..., EA...). g , ..., EA h ), g = 1, 2, ..., h; where EA g Let h be the g-th first bounding box corresponding to the point cloud image of the baggage area, and h be the number of first bounding boxes corresponding to the point cloud image of the baggage area; the length of the first bounding box is along the Y-axis. T200, obtain each second bounding box corresponding to the point cloud image of the baggage area, to obtain a list of second bounding boxes EB = (EB1, EB2, ..., EB...). u , ..., EB v ), u=1,2,…,v; where EB u Let v be the u-th second bounding box corresponding to the point cloud image of the baggage area, and v be the number of second bounding boxes corresponding to the point cloud image of the baggage area; the length of the second bounding box is along the x-axis. T300, split the overlapping and non-overlapping portions of the first bounding box in EA and the second bounding box in EB into third bounding boxes, to obtain a list of third bounding boxes BN = (BN1, BN2, ..., BN...). α , ..., BN β ), α=1, 2,...,β; among them, BN α α is the αth third bounding box obtained by splitting, and β is the number of third bounding boxes obtained by splitting; T400, obtain the maximum Z-axis coordinate of the point cloud within each third bounding box in BN, to obtain the list of maximum Z-axis coordinates PA in BN. z = (PA) z，1 PA z，2 , ..., PA z，α , ..., PA z，β ); where PA z，α BN α Maximum Z-axis coordinate of the interior point cloud; T500, BN α The Z-axis coordinate is adjusted to PA. z，α .

2. The method for processing a luggage wrapper according to claim 1, characterized in that, Step T300 includes the following steps: T310, obtain the fifth preset value WF=1; T311, obtain the sixth preset value WK=1; T312, if EA WF With EB WF+WK-1 If there is overlap, then EA will be... WF With EB WF+WK-1 The overlapping and non-overlapping parts are defined as the third bounding box; otherwise, EA is defined as... WF With EB WF+WK-1 It has been identified as the third enclosing box; T313, if WK < v, then obtain WK = WK + 1; proceed to T312; otherwise, proceed to T314; T314, if WF < h, then get WF = WF + 1; proceed to T311; otherwise, exit the current processing.

3. The method for processing a luggage wrapper according to claim 1, characterized in that, Following step T314, the method further includes the following steps: T315, traverse all third bounding boxes, delete the third bounding boxes that do not contain point clouds inside, to obtain BN.

4. The method for processing a luggage wrapper according to claim 1, characterized in that, EA is obtained through the following steps: T110 acquires the point cloud image of the luggage area on the luggage cart; the point cloud image of the luggage area includes several points, each of which has coordinate information. T120, average the point cloud image of the luggage area along the X-axis to obtain the first sub-bounding box list WA = (WA1, WA2, ..., WA2). e , ..., WA f ), e=1, 2,...,f; among them, WA e Let f be the e-th first sub-bounding box obtained by averaging the point cloud image of the baggage area along the X-axis, and let f be the number of first sub-bounding boxes obtained by averaging the point cloud image of the baggage area along the X-axis; the first sub-bounding boxes in WA are sequentially adjacent. T130 merges several adjacent first sub-bounding boxes in WA that meet the preset merging conditions into a first bounding box to obtain the first bounding box list EA corresponding to the baggage area.

5. The method for processing a luggage wrapper according to claim 4, characterized in that, EB is obtained through the following steps: T210, the point cloud image of the luggage area is averaged along the Y-axis to obtain the second sub-bounding box list WB = (WB1, WB2, ..., WB...). k WB r ), k=1,2,…,r; where WB k The second sub-bounding box is the kth sub-bounding box obtained by averaging the point cloud image of the baggage area along the Y-axis, and r is the number of second sub-bounding boxes obtained by averaging the point cloud image of the baggage area along the Y-axis; the second sub-bounding boxes in WB are sequentially adjacent; T220 merges several adjacent second sub-bounding boxes in WB that meet the preset merging conditions into a second bounding box to obtain the second bounding box list EB corresponding to the baggage area.

6. The method for processing a luggage wrapper according to claim 1, characterized in that, The point cloud image of the luggage area is acquired using LiDAR or a depth camera.

7. The method for processing a luggage wrapper according to claim 5, characterized in that, Both the first sub-boundary box and the second sub-boundary box are cuboid in shape.

8. A non-transitory computer-readable storage medium, wherein the storage medium stores at least one instruction or at least one program segment, characterized in that, The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the method for processing the luggage wrap box as described in any one of claims 1-7.

9. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 8.

Images