CN116605772A - Tower crane collision early warning method based on multiple integrated systems - Google Patents
Tower crane collision early warning method based on multiple integrated systems Download PDFInfo
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C15/00—Safety gear
- B66C15/04—Safety gear for preventing collisions, e.g. between cranes or trolleys operating on the same track
- B66C15/045—Safety gear for preventing collisions, e.g. between cranes or trolleys operating on the same track electrical
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C13/00—Other constructional features or details
- B66C13/16—Applications of indicating, registering, or weighing devices
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C13/00—Other constructional features or details
- B66C13/18—Control systems or devices
- B66C13/48—Automatic control of crane drives for producing a single or repeated working cycle; Programme control
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C15/00—Safety gear
- B66C15/06—Arrangements or use of warning devices
- B66C15/065—Arrangements or use of warning devices electrical
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C23/00—Cranes comprising essentially a beam, boom, or triangular structure acting as a cantilever and mounted for translatory of swinging movements in vertical or horizontal planes or a combination of such movements, e.g. jib-cranes, derricks, tower cranes
- B66C23/88—Safety gear
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention provides a tower crane collision early warning method based on a multi-integration system, which belongs to the technical field of building tower crane construction and comprises the following steps: acquiring high-precision pose information by adopting a dual-antenna combined positioning and pose determining technology; optimizing sensor placement parameters by combining laser point cloud and pose information, generating a construction site point cloud base map, and extracting an outer surrounding box of a building; dividing a lifting object laser point cloud by utilizing tower crane hook position information and encoder data to generate an optimal outer surrounding box of the lifting object, and performing collision detection on the base map surrounding box and the lifting object surrounding box to obtain lifting object collision early warning information; and constructing a collision information visualization module by combining the server side and the client side, and displaying the three-dimensional environment and collision early warning information of the tower crane operation in real time. According to the invention, by constructing the tower crane collision early warning system based on the integration of the double antennas, the encoder and the laser radar, the collision early warning problem in the tower crane operation is effectively solved, the tower crane operation efficiency is improved, and the assistance is provided for the remote operation of the tower crane.
Description
Technical Field
The invention relates to the technical field of construction of building tower cranes, in particular to a tower crane collision early warning method based on a multi-integration system.
Background
The tower crane is widely used hoisting equipment in the field of engineering construction, the operation activity in the use stage is a dynamic process of complex interaction of machinery, personnel and environment, various special operators such as a tower crane driver, a signal worker and the like are involved, the operation safety level is comprehensively influenced by various factors such as knowledge skills, physical and psychological conditions, communication coordination and the like of the special operators, and the high risk is realized. The tower crane using stage is a multiple stage of tower crane production safety accidents, and timely and accurate collision early warning information in hoisting operation is of great significance for controlling the safety risk level of the tower crane operation.
In the traditional tower crane operation safety management, by taking side station supervision and inspection of technical management personnel as main means, the safety technical requirements set forth by standard specifications are met, the management effect is often limited by experience and subjective judgment of the technical management personnel, and comprehensive, timely and reliable implementation is difficult. In order to reduce the occurrence of tower crane accidents and enable the tower crane to run safely, stably and effectively, sensors such as encoders, ultrasonic sensors, laser rangefinders, cameras, radio frequency tags, global positioning systems (Global Positioning System, GPS), ultra Wide Band (UWB) and the like have been used for tower crane collision accident pre-warning, but the sensors have some disadvantages in use. The encoder obtains the position information of the hoisted objects by measuring the rotation angle of the tower crane and the moving distance of the steel wire rope, but the encoder cannot sense the surrounding environment of the hoisted objects and cannot acquire the spatial relationship between the hoisted objects and the building, and the encoder is generally used for collision early warning among tower crane groups; distance measuring sensors such as ultrasonic sensors and laser distance measuring instruments are usually arranged on a lifting hook, collision accidents are early warned by directly measuring the distance between a lifting object and an obstacle of a construction site, and the measuring precision is high, but visual lifting environment information and auxiliary operation information with directivity cannot be provided; the cameras are arranged on the large arms and the small cars of the tower crane, the perception capability of a tower crane driver to the hoisting environment is enhanced through the images transmitted in real time, but the distance between the hoisted object and surrounding objects still needs to be judged manually, the shooting visual angle of the cameras is fixed, the stereoscopic impression of the images is poor, and the judgment of the collision distance is not facilitated; the radio frequency tag, the GPS, the UWB and the like are usually arranged on the lifting hook, so that the position information of the lifting object can be accurately acquired, but the surrounding environment information cannot be acquired, and the collision information is usually required to be early warned by combining technologies such as a building information model (Building Information Modeling, BIM) and the like.
Aiming at the defects of various sensor systems in tower crane collision early warning, the method needs to make up for the deficiencies in various technologies, and provides a novel tower crane collision early warning method.
Disclosure of Invention
The invention provides a tower crane collision early warning method based on a multi-integrated system, which is used for solving the defects that various sensors adopted for tower crane collision early warning in the prior art are poor in spatial environment perception, too dependent on the cooperation with peripheral systems and can not recognize and acquire more accurate collision early warning information.
In a first aspect, the present invention provides a tower crane collision early warning method based on a multi-integrated system, including:
acquiring laser radar point cloud data and combined navigation system observation data by a tower crane side industrial personal computer, and receiving encoder observation data and a hook receiver carrier phase differential positioning result;
performing dual-antenna integrated navigation positioning and attitude determination calculation by using the integrated navigation system observation data to obtain real-time pose information;
obtaining a construction site cloud base map by using the laser radar point cloud data and the real-time pose information through geographic orientation and point cloud registration, and extracting a building contour bounding box corresponding to the construction site cloud base map by adopting an upper and lower bottom surface contour combined bounding box extraction algorithm;
Dividing and extracting the laser radar point cloud data or calculating by utilizing the encoder observation data to obtain a hoisted object coordinate, and generating a hoisted object bounding box by combining the carrier phase difference positioning result of the hook receiver;
and performing collision detection on the building outline bounding box and the lifting object bounding box to obtain the spatial relationship and collision early warning information of the lifting object and the building.
In a second aspect, the present invention further provides a tower crane collision early warning system based on a multi-integrated system, including:
the acquisition and receiving module is used for acquiring laser radar point cloud data and combined navigation system observation data by the industrial personal computer at the tower crane side, and receiving encoder observation data and a hook receiver carrier phase differential positioning result;
the positioning calculation module is used for carrying out dual-antenna integrated navigation positioning and gesture determination calculation by utilizing the integrated navigation system observation data to obtain real-time gesture information;
the directional registration module is used for obtaining a construction site cloud base map by utilizing the laser radar point cloud data and the real-time pose information through geographic orientation and point cloud registration, and extracting a building contour bounding box corresponding to the construction site cloud base map by adopting an upper and lower bottom surface contour combined bounding box extraction algorithm;
The segmentation calculation module is used for carrying out segmentation extraction on the laser radar point cloud data or calculating by utilizing the encoder observation data to obtain hoisting object coordinates, and generating a hoisting object bounding box by combining the hook receiver carrier phase difference positioning result;
and the collision detection module is used for carrying out collision detection on the building outline bounding box and the hoisting object bounding box to obtain the spatial relationship and collision early warning information of the hoisting object and the building.
In a third aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements any one of the tower crane collision early warning methods based on the multiple integrated systems described above when executing the program.
In a fourth aspect, the present invention also provides a non-transitory computer readable storage medium, on which is stored a computer program, which when executed by a processor, implements a tower crane collision warning method based on a multi-integrated system as described in any one of the above.
According to the tower crane collision early warning method based on the multi-integration system, the tower crane collision early warning system based on the integration of the double antennas, the encoder and the laser radar is constructed, so that the problem of collision early warning in tower crane operation is effectively solved, the tower crane operation efficiency is improved, and assistance is provided for remote operation of the tower crane.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a tower crane collision early warning method based on a multi-integrated system;
FIG. 2 is a general structural diagram of the intelligent tower crane collision early warning system with multiple sensors integrated;
FIG. 3 is a schematic diagram of a combination navigation system and lidar arrangement provided by the present invention;
FIG. 4 is a flow chart of a dual-antenna GNSS/SINS combined high-precision autonomous positioning and attitude determination algorithm provided by the invention;
FIG. 5 is a flowchart of a laser point cloud base map and bounding box generation algorithm provided by the invention;
FIG. 6 is a flow chart of a real-time monitoring and collision information measuring algorithm for a hoisted object provided by the invention;
FIG. 7 is a flow chart of a visualization system data interaction provided by the present invention;
fig. 8 is a schematic structural diagram of a tower crane collision early warning system based on a multi-integrated system provided by the invention;
Fig. 9 is a schematic structural diagram of an electronic device provided by the present invention.
Reference numerals:
1: a primary GNSS antenna; 2: a slave GNSS antenna; 3: an integrated navigation system;
4: and (5) laser radar.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Aiming at the defects existing in the tower crane collision early warning technology in the prior art, the invention provides a tower crane collision intelligent early warning system which is integrated with a global navigation satellite system (Global Navigation Satellite System, GNSS), an inertial strapdown navigation system (Strap-down Inertial Navigation System, SINS), an encoder and a laser radar, and provides a tower crane collision early warning method of a multi-integrated system, which can accurately acquire the space geometric information of a construction environment and a hoisting object and has the characteristics of accurate early warning, high real-time performance and panoramic visualization of the hoisting environment.
Fig. 1 is a schematic flow chart of a tower crane collision early warning method based on a multi-integrated system according to an embodiment of the present invention, as shown in fig. 1, including:
step 100: acquiring laser radar point cloud data and combined navigation system observation data by a tower crane side industrial personal computer, and receiving encoder observation data and a hook receiver carrier phase differential positioning result;
step 200: performing dual-antenna integrated navigation positioning and attitude determination calculation by using the integrated navigation system observation data to obtain real-time pose information;
step 300: obtaining a construction site cloud base map by using the laser radar point cloud data and the real-time pose information through geographic orientation and point cloud registration, and extracting a building contour bounding box corresponding to the construction site cloud base map by adopting an upper and lower bottom surface contour combined bounding box extraction algorithm;
step 400: dividing and extracting the laser radar point cloud data or calculating by utilizing the encoder observation data to obtain a hoisted object coordinate, and generating a hoisted object bounding box by combining the carrier phase difference positioning result of the hook receiver;
step 500: and performing collision detection on the building outline bounding box and the lifting object bounding box to obtain the spatial relationship and collision early warning information of the lifting object and the building.
It should be noted that, the embodiment of the invention is a tower crane collision intelligent early warning system based on multi-sensor integration, the layout of sensors and the acquisition of data are the precondition for realizing the functions of the embodiment of the invention, the overall structure of the tower crane collision intelligent early warning system based on multi-sensor integration is shown in fig. 2, and a laser radar and a combined navigation system are arranged above a tower crane cockpit and are connected with an industrial personal computer in the cockpit through cables; the GNSS receiver is arranged above the tower crane lifting hook and used for determining the position of the lifting hook and transmitting the position information to the industrial personal computer through the local area network; the encoder is integrated in a winch of the tower crane, can measure the moving distance of the trolley on the large arm and the descending distance of the lifting hook, and transmits the measuring result to the industrial personal computer through the local area network. In the integrated navigation system above the cockpit, two GNSS antennas are connected and fixedly connected with the lidar, as in the integrated navigation system and lidar layout schematic shown in fig. 3, the master GNSS antenna 1 and the slave GNSS antenna 2 are respectively arranged at two sides of the tower crane boom, and the integrated navigation system 3 and the lidar 4 perform time synchronization by sending impulse number (Pulses Per Second, PPS) pulses per second.
Specifically, the tower crane side industrial personal computer receives laser radar point cloud data and GNSS observations and SINS observations of the integrated navigation system in Real Time through a robot operating system (Robot Operating System, ROS), and simultaneously receives encoder data and Real-Time Kinematic (RTK) positioning results of a GNSS receiver at a hook through a local area network.
And performing dual-antenna GNSS/SINS combined positioning and attitude determination calculation by using the GNSS observation value and the SINS observation value which are acquired in real time to obtain real-time pose information.
And respectively rotating the tower crane clockwise and anticlockwise for 1.5 circles, simultaneously storing received laser radar point cloud data and resolved pose information, obtaining a point cloud base map of a construction site by directly carrying out geographic orientation and point cloud registration and optimizing spatial arrangement parameters between a combined navigation sensor and the laser radar, and obtaining a bounding box closer to the real contour of a building by adopting an upper and lower bottom surface contour combined bounding box extraction method.
And performing preliminary segmentation and extraction on the point cloud received in real time, calculating coordinates of the hoisted object by using the observation data of the encoder, screening out laser point cloud of the hoisted object by combining with the position information of the RTK at the lifting hook, generating an optimal outer bounding box of the hoisted object, and performing collision detection with the acquired base map bounding box to obtain the spatial relationship between the hoisted object and the building and collision early warning information.
And transmitting the obtained point cloud base map and base map bounding box and the obtained hoisting object bounding box and collision early warning information to a ground client through a local area network by a tower base side service end, and displaying the hoisting operation three-dimensional environment, the collision distance information and the potential collision targets in real time under different visual angles by ground end visualization software based on a illusion Engine 4 (UE4).
According to the invention, by integrating the GNSS, the inertial navigation, the encoder and the laser radar, an accurate three-dimensional point cloud base map of the construction site is established, the three-dimensional environment and the early warning information of the hoisting operation can be displayed at multiple angles in real time, the display effect is more visual, and the risk of collision accidents of the tower crane is reduced; by adopting a dual-antenna GNSS/SINS combined positioning and attitude determination method, the position and attitude resolving result is more accurate, and the precision of the point cloud base map and collision early warning information is improved; in addition, the building bounding box and the optimal outer bounding box of the hoisted object are used for collision detection, so that the calculation efficiency is greatly improved while the collision early warning redundancy is ensured; and a visualization module is constructed based on a mode that the client and the server are separated, so that the data transmission quantity is reduced, the data blocking is avoided, and the instantaneity of the visualization module is improved.
Based on the embodiment, the embodiment of the invention constructs the three-dimensional point cloud base map of the construction site and determines the position of the hoisted object in the point cloud base map, so that high-precision laser radar pose information is required to be calculated.
As shown in fig. 3, 2 GNSS antennas fixed on the acquisition device may form a short baseline to implement an kinematic-to-kinematic RTK, determine an accurate baseline vector, and thus obtain an accurate pose. In the GNSS/SINS combination, dual-antenna GNSS attitude measurement can be used for initial alignment and external attitude information for a navigation stage, and especially, the observability of a course angle is improved. The dual-antenna GNSS attitude measurement is still affected by signal shielding, the result output is discontinuous, the noise is large, and the dual-antenna GNSS attitude measurement is usually combined with the SINS, on one hand, a gyroscope in the SINS can smooth the GNSS attitude measurement result to make up for interruption when the signal is out of lock, and on the other hand, an accelerometer can calculate a horizontal angle, so that the dual-antenna GNSS/SINS combination can provide continuous, smooth and reliable three-dimensional attitude information. According to the invention, a misalignment angle equation in a GNSS/SINS combined basic model is used as a state equation, and a course angle provided by a double-antenna GNSS and a pitch angle and a roll angle provided by a sum are used as observation values to jointly form a basic element of Kalman filtering. The specific flow of the high-precision autonomous positioning and attitude determination of the dual-antenna GNSS/SINS combination is shown in fig. 4, and specifically comprises the following steps:
After the ambiguity is correctly fixed by the dual-antenna GNSS, an accurate baseline vector can be obtained, so that a course angle and a pitch angle are calculated:
in the formula ,representing the projection of the baseline vector in the eastern direction, +.>Representing the projection of the baseline vector in the north direction,representing a projection of the baseline vector in a vertical direction;
and (3) deriving a pitch angle and a roll angle according to the output of inertial navigation to obtain an observation equation with updated posture:
the inertial navigation sum output can be represented by a specific force equation:
in the formula ,is the sum output value,/->For line of sight acceleration in the l-system (local horizontal coordinate system), +.>For coriolis force, ->For centripetal acceleration to ground->For gravity, in obtaining-> and />After that, it is possible to compose a composition about +.>In which course angle is in turn defined by +.>Providing, therefore, the additively derived pitch and roll angles can be calculated while using the Butterworth filter pair +.>The original observations are smoothed and denoised. />Representation ofeIs relative toiThe angular velocity of the system is atlProjection under the system, ++>Representation oflIs relative toiThe angular velocity of the system is atlProjection under the system, ++>Representation oflUnder the system add-count output value, +.>Representation ofbIs tied tolAnd (3) a coordinate transformation matrix of the system.
After the attitude observation value is obtained, the relation between the attitude observation value and the state to be estimated is established by the following formula, namely an observation equation:
in the formula ,representation ofeIs tied tolCoordinate transformation matrix of system,/>Representation ofeIs tied tolCoordinate transformation matrix of system,/>Representing a unit array->Representation->An antisymmetric matrix of angles>Representing the current time of mechanical arrangementbIs tied toeAnd (3) a coordinate transformation matrix of the system.
Since the attitude angle is expressed inlUnder the system, the observed value is contained inIn the above, the state to be estimated->The expression under e is that the attitude angle is +.>After representation, the two-sided differentiation can be used to obtain the observation equation:
in the formula ,residual vector representing observations,/>Representing the design matrix->Representing parameters to be estimated->Representing observation noise->、/> and />Residual vectors representing heading angle, pitch angle and roll angle, respectively, +.>、/> and />Observation values respectively representing attitude angles, +.>、/> and />Euler angle obtained by updating inertial navigation attitude, < >>Middle->、、/>Observation coefficient representing heading angle, +.>、/>、/>Observation coefficient representing pitch angle, +.>、/>、/>Observation coefficient representing roll angle, +.>Representing the state to be estimated.
The main antenna obtains an observation equation with updated position through RTK:
in the formula ,representation ofeTethered position updateObservation residual vector, ">Representing mechanical arrangementePosition of down inertial navigation->Indicating that the lever arm is ateProjection under the system, ++ >Representation ofeTying down the position of the center of the GNSS antenna, +.>Error state indicating position +.>Indicating misalignment angle->Representing observed noise.
And then carrying out integrated navigation calculation to obtain high-precision pose information, and carrying out feedback correction on the zero offset of the acceleration and the gyroscope according to the result of the calculation.
The invention obtains the course angle of the acquisition equipment through the dynamic-to-dynamic RTK, constructs an observation equation for attitude update by combining inertial navigation addition and calculation output, and provides a space reference for subsequent point cloud data processing by combining the attitude information obtained by navigation calculation
Based on the above embodiment, as shown in fig. 5, a specific algorithm flow for generating a laser point cloud base map and a bounding box according to the embodiment of the present invention includes:
performing point cloud geographic orientation on the original point cloud, and converting the point cloud from a laser radar coordinate system to a local horizontal coordinate system;
performing inter-frame matching on the converted point cloud to obtain a preliminary laser point cloud base map;
space placement parameters of the laser radar and inertial navigation are optimized, and the accuracy of the point cloud base map is improved;
and the point cloud is divided into ground points and non-ground points by adopting a cloth simulation filtering algorithm, so that the extraction of the bounding box of the object in the monitoring range is convenient. The basic idea of the cloth simulation filtering algorithm is to invert three-dimensional point cloud data, assume that a piece of cloth is covered on the inverted point cloud, the cloth is under the action of gravity and is close to the ground, and the final position covered by the cloth is the position of the ground point;
And clustering discrete point clouds belonging to the same non-ground surface feature into a point cloud object by adopting an Euclidean clustering method, carrying out point cloud neighborhood query by using an octree structure or a KD tree structure so as to realize the Euclidean clustering of the non-ground point clouds, and clustering points with the point cloud distance smaller than a threshold value into the same object. For each object adoptThe shape algorithm calculates the out-of-plane boundary, and the bounding box of the object is obtained after the bottom surface point and the top surface point of the point cloud object are extracted.
The point cloud data collected by the laser radar take a laser radar coordinate system as a reference, and the laser radar continuously moves in the process of drawing data collection, so that an accurate spatial relationship between the laser radar and the SINS needs to be obtained when a laser point cloud base drawing is constructed, and the laser radar coordinate is projected to a local horizontal coordinate system.
Ground point measured by laser radarThe coordinates in the lidar coordinate system are +.>The relation between the laser radar and the inertial measurement unit is rigidly fixed, and the coordinates of the ground point under the inertial navigation coordinate system can be obtained according to the coordinate conversion principle:
according to the position and the gesture provided by the combination of the double-antenna GNSS/SINS, the coordinates in the inertial navigation coordinate system can be converted into a global coordinate system:
The method further comprises the following steps:
in the above-mentioned formulae, the first and second light-emitting elements,represents any ground point->Coordinates in the lidar coordinate system, +.>Representing ground pointsCoordinates under inertial navigation coordinate system, +.>Represents any ground point->Coordinates in the WGS84 geocentric geodetic coordinate system,>representing the position of the origin of the lidar coordinate system in the inertial navigation coordinate system, +>Rotation matrix representing the transformation of a lidar coordinate system into an inertial navigation coordinate system, +>Represents the position of the origin of the inertial navigation coordinate system in the WGS84 geocentric fixed space rectangular coordinate system,representing the transformation of the inertial navigation coordinate system into a rotation matrix of the geocentric, fixed space rectangular coordinate system at WGS 84.
The attitude angle output by the inertial navigation system is the attitude of the inertial navigation system in the local horizontal coordinate system, and the local horizontal coordinate system can obtain a rotation matrix from the local horizontal coordinate system to the WGS84 geocentric and geodetic fixed coordinate system through the geodetic coordinates of the origin of the local horizontal coordinate system, so that:
in the formula ,rotation matrix of inertial navigation coordinate system to local horizontal coordinate system calculated according to three attitude angles of inertial navigation output +.>Substituting a rotation matrix from a local horizontal coordinate system obtained by calculation according to an inertial navigation coordinate system origin geodetic coordinate to a WGS84 geodetic fixed space rectangular coordinate system to obtain:
The above equation is a positioning equation of the laser scanning of the tower crane, and coordinates of the ground feature points in the WGS84 space rectangular coordinate system can be directly calculated. The coordinates of the ground feature point in the local horizontal coordinate system are:
wherein ,for the coordinates of the origin of the local horizontal coordinate system under the WGS84 coordinate system, +.>Is that
WGS84 geocentric fixes the rotation matrix of the space rectangular coordinate system to the local horizontal coordinate system.
By projecting the point cloud from the lidar coordinates to the local horizontal coordinate system, the lidar coordinates are involved in the projection processPosition of point in inertial navigation coordinate systemAnd a rotation matrix for transformation of the lidar coordinate system to the inertial navigation coordinate systemTherefore, optimization +.> and />Assume that the laser scanning system is +.>Two scans were performed to obtain:
in the formula ,、/>、/> and />、/>、/>Is +.>The measured value in the lidar coordinate system is a known value,/->、/>、/> and />Is the first scan +.>A rotation matrix consisting of the position and the attitude of moment inertial navigation,>、/>、/> and />Is a second scan->A rotation matrix consisting of the position and the attitude of the moment inertial navigation, which are all known values, are only +.>And->Is unknown, i.e. only the lidar placement parameters that need to be solved are unknown. Subtracting the two formulas to obtain:
During the calibration processIn (3) minimizing the left side of the above equal sign,and->Unknown (I)>Representing three translation parameters, +.>Is a rotation matrix expressed by three angles of rotation around the X-axis, the Y-axis and the Z-axis respectively, and therefore, six unknowns are independent of each other:
further, a nonlinear optimization algorithm (Levenberg-Marquardt, LM) is used to solve for the six calibration parameters. The LM nonlinear algorithm iteratively obtains the least squares sum of a set of nonlinear equations, whose mathematical model is as follows:
is a set of nonlinear equations, LM algorithm looks for a set of +.>So that->Minimum, each point pair can constitute 3 equations, if anynA plurality of point pairs can form 3nEquation, calculate the optimal solution through LM algorithmThereby obtaining:
。
according to the invention, the original point cloud is projected to a local horizontal coordinate system by adopting direct geographic orientation, and a flexible and simple calibration mode without a three-dimensional control field is used, so that the placement parameters between inertial navigation and a laser radar are optimized, and the precision of a point cloud base map is improved.
Based on the above embodiment, as shown in fig. 6, a specific algorithm flow for real-time monitoring and collision information measurement of a hoisted object according to the embodiment of the present invention includes:
projecting the collected original laser point cloud to a local horizontal coordinate system, and converting the RTK positioning result of the lifting hook from the WGS84 coordinate system to the local horizontal coordinate system to be recorded as ；
Dividing an original point cloud into a ground point and a non-ground point by adopting point cloud filtering, and extracting a surface slice point cloud from the non-ground point cloud to serve as a target area extracted by a hoisted object;
carrying out connectivity analysis on non-ground patches, merging adjacent non-ground patches, taking the merged result as a target area to be determined, and then eliminating target areas which are obviously not buildings according to the geometric dimension of the buildings and certain elevation difference between the boundaries of the buildings and the ground;
using position information of hooksDividing the object closest to the lifting hook into a hoisted object, dividing other objects into large building objects, and calculating the position of the hook according to the moving distance of the trolley and the descending distance of the lifting hook, which are measured by an encoder, and pose information obtained by combining the double-antenna GNSS/SINS when GNSS signals at the lifting hook are shielded>Will beCarry in and put inA large screening threshold value can also be used for obtaining a hoisted object target;
generating a two-dimensional overlooking projection on an XY plane by using a three-dimensional external bounding box of the suspended object, rotating the overlooking projection around the center because the suspended object rotates around a z axis in the hoisting process, simulating a possible rotation range of the suspended object, and obtaining an external rectangle of the rotation range as an optimal external bounding box;
And (3) carrying out index construction on the base graphic primitives by adopting an AABB tree, then carrying out collision detection on the base graphic primitives and the suspended objects based on the AABB tree, traversing the AABB tree of the base graphic primitives and the suspended objects, and sequentially calculating the distance vector from the corner point of the suspended object bounding box to the nearest graphic primitive after judging the base graphic primitive closest to the outer bounding box of the suspended objects, thereby obtaining the real minimum collision distance.
It should be noted that, because the operation environment of the tower crane is complex, the laser point cloud scanned in real time includes not only the hoisted objects but also the buildings and other construction equipment. Therefore, the embodiment of the invention adopts a top-down strategy to identify the hoisted object area from the target area level, and then accurately extracts the hoisted object point cloud and the bounding box according to the characteristic difference of the target detail. In order to improve the speed of real-time monitoring of a hoisted object, the embodiment of the invention uses point cloud filtering to extract non-ground patch point clouds from the original point clouds, and the flow comprises the following steps:
in the point cloud filtering, the data quality of the point cloud has great influence on the result and the operation efficiency. For example: the density of the point cloud is too high, sometimes reaching hundreds of points per square, and great calculation amount is needed during the calculation and filtering processing of the local characteristics of the point cloud; the density of the point cloud is too low or the distribution is uneven, so that the accuracy of the calculation of the local characteristics of the point cloud is seriously affected, and the filtering result of the point cloud is affected; the data missing area may cause discontinuous distribution of the ground point clouds, so that the adjacent space relation between the point clouds is weakened, and the probability of filtering errors of the point clouds is increased. Therefore, the embodiment of the invention adopts the virtual grid technology to process the original point cloud, so that the point cloud distribution is as uniform as possible and the data holes are reduced. First, uniformly dividing a region by using a mesh of a certain size, wherein the mesh size is set as follows . Then, each grid is traversed, and the lowest point in the grid is taken as grid point +.>While the remaining point clouds in the grid are marked as other point clouds +.>. Finally, all grids without point clouds are detected, and a virtual point is interpolated for each grid without point clouds. The point is located at the center of the grid and the elevation of the point is obtained by nearest neighbor interpolation.
The point cloud filtering method adopted by the embodiment of the invention mainly comprises two steps: firstly, carrying out self-adaptive partitioning on grid point clouds by using a point cloud partitioning method, and dividing the grid point clouds into two types of a surface patch set and a discrete independent point cloud set; and then, respectively processing the two types of point clouds by a point cloud segmentation filtering method and a multi-scale morphological filtering method, and classifying the grid points into ground grid points and non-ground grid points. In the laser point cloud data, the gentle character of different areas is different, the geometric features of the adjacent point clouds of the gentle areas are similar, and the probability that the point clouds have the same category is very high; and the geometrical characteristics of the point cloud adjacent to the region with severe variation are greatly different, and the point and the adjacent point can come from different target surfaces. Therefore, different types of areas should be expressed by adopting different primitives, gentle areas are suitable to be expressed as area patch primitives, and severe areas should be expressed by adopting point primitives, so that the self characteristics and the adjacent relation of point clouds of different areas can be expressed better, and the difference between ground and non-ground point clouds can be described more robustly. In order to realize that different areas adopt different primitives, the point cloud is divided into smooth and gentle areas and areas with severe elevation changes by utilizing point cloud segmentation based on smoothness constraint. The point cloud segmentation based on the smoothness constraint is to merge adjacent point clouds with similar normal vectors and plane fitting residual errors into the same patch by using a region growing method. The segmentation process is as follows:
Method for estimating point cloud by principal component analysisVector and Gaussian curvature, extracting point cloud corresponding to minimum value of Gaussian curvature from undivided point cloud as seed point, and recording asSearch for seed points->And taking the points meeting the growing condition as new seed points, and replacing the new seed points until no points can meet the condition. The growing conditions are two, namely that the included angle between the normal vector of the neighborhood point and the normal vector of the seed point is smaller than a certain threshold value, and the residual error between the neighborhood point and the local fitting plane of the seed point is smaller than a certain threshold value. If all points are assigned to the corresponding patches, the segmentation ends; otherwise, extracting seed points from the undivided point cloud is continued. The value of 3 threshold values in the segmentation process is very important, and the point cloud segmentation result is directly affected. If the parameters are improper, the result is easy to have over-segmentation or sub-segmentation problems, so that the point cloud filtering result, particularly the sub-segmentation problem, is affected. The values of the 3 parameters have close relation with the point cloud density and the point cloud coordinate error. If the point density is high and the point cloud coordinate error is small, N can be properly increased, the anti-noise capability of normal vector calculation is improved, and the included angle and the residual error threshold are properly reduced; if the point density is low and the point cloud coordinate error is large, N is properly reduced, the included angle and the residual error threshold can be properly increased, and the gentle region can be ensured to have a better segmentation effect. After the point cloud filtering process, the point cloud is divided into a ground point cloud and a non-ground point cloud, wherein the non-ground point cloud is expressed as two types: non-ground patches and non-ground discrete independent point clouds.
According to the invention, the priori position of the hoisted object is obtained by adopting the RTK+ encoder, so that the extraction speed of the point cloud of the target area is improved, the interference of buildings and construction equipment on the rapid identification of the hoisted object is reduced, the collision detection method based on the AABB tree bottom graph primitive retrieval structure is suitable for the actual application scene of the tower crane, and the retrieval process of the bottom graph bounding box is accelerated.
Based on the embodiment, after the three-dimensional point cloud base map of the construction site and the collision early warning information of the hoisted objects are obtained, the information is subjected to visual processing, so that a tower crane operator can be assisted to perceive the three-dimensional environment of hoisting operation, and the efficiency of the hoisting operation is improved. The data interaction flow chart of the visualization system provided by the embodiment of the invention is shown in fig. 7, a structural design tower crane collision early warning visualization module of a 'server side plus a client side' is adopted, a tower side is taken as a server side, ground side visualization software is taken as a client side, functions of view angle switching, safety threshold setting, potential collision area lighting cloud highlighting and the like are realized, and the whole realization process comprises the following steps:
the client is connected with the server through a local area network, and is configured with parameters such as tower crane height, arm length and the like, and a tower crane model is loaded;
the client sends a drawing building instruction to the server, the tower crane rotates according to drawing building requirements, the server collects data required by drawing building, and a laser point cloud map and bounding box generating module is called to generate a point cloud base map and a bounding box;
The client downloads the point cloud base map and the base map bounding box from the server and loads the point cloud base map and the base map bounding box;
the client side configures parameters such as the length of the lifting rope and sends the parameters to the server side, the client side sends a collision detection instruction to the server side, and the server side calls a lifting object real-time monitoring and collision information measuring module to obtain collision early warning information: collision vector start point coordinates, collision vector end point coordinates, coordinates of 8 corner points of the bounding box;
after receiving the collision early warning information, the client visualizes the hoisted object bounding box and the collision vector, searches a potential collision area in the base map point cloud according to the set early warning range and highlights the potential collision area.
According to the invention, the data transmission efficiency is improved through the tower crane collision early warning visualization module based on the structural design of the server side and the client side, the real-time performance of the collision early warning information visualization is ensured, and effective assistance can be provided for remote operation of the tower crane.
The tower crane collision early warning system based on the multi-integrated system provided by the invention is described below, and the tower crane collision early warning system based on the multi-integrated system described below and the tower crane collision early warning method based on the multi-integrated system described above can be correspondingly referred to each other.
Fig. 8 is a schematic structural diagram of a tower crane collision early warning system based on a multi-integrated system according to an embodiment of the present invention, as shown in fig. 8, including: an acquisition and receiving module 81, a positioning and resolving module 82, an orientation registration module 83, a segmentation calculation module 84 and a collision detection module 85, wherein:
The acquisition and receiving module 81 is used for acquiring laser radar point cloud data and combined navigation system observation data by the tower crane side industrial personal computer, and the receiving encoder observation data and hook receiver carrier phase difference positioning result positioning calculation module 82 is used for carrying out double-antenna combined navigation positioning and gesture determination calculation by utilizing the combined navigation system observation data to obtain real-time gesture information; the directional registration module 83 is configured to obtain a construction site cloud bottom map by using the laser radar point cloud data and the real-time pose information and through geographic orientation and point cloud registration, and extract a building contour bounding box corresponding to the construction site cloud bottom map by adopting an upper and lower bottom surface contour combined bounding box extraction algorithm; the segmentation calculation module 84 is configured to perform segmentation extraction on the laser radar point cloud data or calculate by using the encoder observation data to obtain a hoisted object coordinate, and generate a hoisted object bounding box by combining the hook receiver carrier phase differential positioning result; the collision detection module 85 is configured to perform collision detection on the building outline bounding box and the lifting object bounding box, so as to obtain spatial relationship and collision early warning information of the lifting object and the building.
Fig. 9 illustrates a physical schematic diagram of an electronic device, as shown in fig. 9, which may include: processor 910, communication interface (Communications Interface), memory 930, and communication bus 940, wherein processor 910, communication interface 920, and memory 930 communicate with each other via communication bus 940. Processor 910 may invoke logic instructions in memory 930 to perform a multi-integrated system based tower crane collision warning method comprising: acquiring laser radar point cloud data and combined navigation system observation data by a tower crane side industrial personal computer, and receiving encoder observation data and a hook receiver carrier phase differential positioning result; performing dual-antenna integrated navigation positioning and attitude determination calculation by using the integrated navigation system observation data to obtain real-time pose information; obtaining a construction site cloud base map by using the laser radar point cloud data and the real-time pose information through geographic orientation and point cloud registration, and extracting a building contour bounding box corresponding to the construction site cloud base map by adopting an upper and lower bottom surface contour combined bounding box extraction algorithm; dividing and extracting the laser radar point cloud data or calculating by utilizing the encoder observation data to obtain a hoisted object coordinate, and generating a hoisted object bounding box by combining the carrier phase difference positioning result of the hook receiver; and performing collision detection on the building outline bounding box and the lifting object bounding box to obtain the spatial relationship and collision early warning information of the lifting object and the building.
Further, the logic instructions in the memory 930 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, is implemented to perform the tower crane collision pre-warning method based on a multi-integrated system provided by the above methods, the method comprising: acquiring laser radar point cloud data and combined navigation system observation data by a tower crane side industrial personal computer, and receiving encoder observation data and a hook receiver carrier phase differential positioning result; performing dual-antenna integrated navigation positioning and attitude determination calculation by using the integrated navigation system observation data to obtain real-time pose information; obtaining a construction site cloud base map by using the laser radar point cloud data and the real-time pose information through geographic orientation and point cloud registration, and extracting a building contour bounding box corresponding to the construction site cloud base map by adopting an upper and lower bottom surface contour combined bounding box extraction algorithm; dividing and extracting the laser radar point cloud data or calculating by utilizing the encoder observation data to obtain a hoisted object coordinate, and generating a hoisted object bounding box by combining the carrier phase difference positioning result of the hook receiver; and performing collision detection on the building outline bounding box and the lifting object bounding box to obtain the spatial relationship and collision early warning information of the lifting object and the building.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A tower crane collision early warning method based on a multi-integration system is characterized by comprising the following steps:
acquiring laser radar point cloud data and combined navigation system observation data by a tower crane side industrial personal computer, and receiving encoder observation data and a hook receiver carrier phase differential positioning result;
performing dual-antenna integrated navigation positioning and attitude determination calculation by using the integrated navigation system observation data to obtain real-time pose information;
obtaining a construction site cloud base map by using the laser radar point cloud data and the real-time pose information through geographic orientation and point cloud registration, and extracting a building contour bounding box corresponding to the construction site cloud base map by adopting an upper and lower bottom surface contour combined bounding box extraction algorithm;
Dividing and extracting the laser radar point cloud data or calculating by utilizing the encoder observation data to obtain a hoisted object coordinate, and generating a hoisted object bounding box by combining the carrier phase difference positioning result of the hook receiver;
and performing collision detection on the building outline bounding box and the lifting object bounding box to obtain the spatial relationship and collision early warning information of the lifting object and the building.
2. The tower crane collision early warning method based on the multiple integrated systems according to claim 1, wherein the method for performing dual-antenna integrated navigation positioning and attitude determination calculation to obtain real-time pose information by using the integrated navigation system observation data comprises the following steps:
performing motion vector phase difference RTK correction on a main antenna and a slave antenna in a GNSS dual antenna to obtain a base line vector, and obtaining a course angle and a pitch angle from the base line vector:
wherein ,indicating heading angle->Represents pitch angle, +.>Representing the projection of the baseline vector in the eastern direction, +.>Representing the projection of the baseline vector in the north direction, +.>Representing a projection of the baseline vector in a vertical direction;
deducing a pitch angle and a roll angle according to the output of the inertial strapdown navigation SINS, and obtaining an attitude updating observation equation according to the pitch angle and the roll angle;
Obtaining a position update observation equation by the main antenna through RTK:
wherein ,representation ofeAn observation residual vector with a lower position update,/->Representing mechanical arrangementeIs under inertial navigationPosition (S)>Indicating that the lever arm is ateProjection under the system, ++>Representation ofeTying down the position of the center of the GNSS antenna, +.>Error state indicating position +.>Indicating misalignment angle->Representing observed noise;
based on the course angle, the attitude updating observation equation and the position updating observation equation, carrying out integrated navigation settlement to obtain the real-time pose information;
and carrying out feedback correction on the zero offset of the acceleration and the gyroscope by utilizing the real-time pose information.
3. The tower crane collision early warning method based on the multi-integrated system according to claim 2, wherein the deriving of the pitch angle and the roll angle according to the output of the inertial strapdown navigation SINS, and the obtaining of the attitude update observation equation from the pitch angle and the roll angle, comprises:
and obtaining an attitude update observation equation according to the SINS, and expressing SINS accelerometer output by a specific force equation:
wherein ,is the sum output value,/->For the line of sight acceleration under the local horizontal coordinate system l>In order for the coriolis force to be a coriolis force,for centripetal acceleration to ground- >For gravity (I)>Representation ofeIs relative toiThe angular velocity of the system is atlProjection under the system, ++>Representation oflIs relative toiThe angular velocity of the system is atlProjection under the system, ++>Representation oflUnder the system add-count output value, +.>Representation ofbIs tied tolA coordinate transformation matrix of the system;
wherein ,representation ofeIs tied tolCoordinate transformation matrix of system,/>Representation ofeIs tied tolCoordinate transformation matrix of system,/>Representing a unit array->Representation->An antisymmetric matrix of angles>Representing the current time of mechanical arrangementbIs tied toeA coordinate transformation matrix of the system;
angle of attitudeAnd differentiating to obtain the attitude update observation equation:
wherein ,residual vector representing observations,/>Representing the design matrix->Representing parameters to be estimated->Representing observation noise->、/> and />Residual vectors representing heading angle, pitch angle and roll angle, respectively, +.>、/> and />Observation values respectively representing attitude angles, +.>、/> and />Euler angle obtained by updating inertial navigation attitude, < >>Middle->、、/>Observation coefficient representing heading angle, +.>、/>、/>Representing pitch angleObservation coefficient of>、/>、/>Observation coefficient representing roll angle, +.>Representing the state to be estimated.
4. The tower crane collision early warning method based on the multi-integration system according to claim 1, wherein the step of obtaining a construction site cloud base map by using the laser radar point cloud data and the real-time pose information through geographic orientation and point cloud registration, and extracting a building contour bounding box corresponding to the construction site cloud base map by adopting an upper and lower bottom surface contour combined bounding box extraction algorithm comprises the following steps:
Performing point cloud geographic orientation on the laser radar point cloud data, and converting the laser radar point cloud data from a laser radar coordinate system to a local horizontal coordinate system to obtain converted laser radar point cloud data;
performing inter-frame matching on the converted laser radar point cloud data to obtain an initial laser point cloud base map;
optimizing space placement parameters of the laser radar by adopting a nonlinear least square estimation LM algorithm to obtain a construction site cloud base map;
dividing the initial laser point cloud base map into ground points and non-ground points by adopting a cloth simulation filtering algorithm;
clustering non-ground points belonging to the same non-ground feature into a point cloud object by European clustering, adoptingThe shape algorithm calculates the out-of-plane edge of each point cloud objectAnd extracting the bottom surface point and the top surface point of each point cloud object to obtain the building outline bounding box.
5. The multi-integration system-based tower crane collision pre-warning method according to claim 4, wherein performing point cloud geographic orientation on the laser radar point cloud data, converting the laser radar point cloud data from a laser radar coordinate system to a local horizontal coordinate system, and obtaining converted laser radar point cloud data, comprises:
Acquiring any ground point in the laser radar point cloud dataCoordinates in the lidar coordinate system +.>The coordinates are->Conversion into the coordinate under the inertial navigation coordinate system +.>：
Based on the combined position and posture of the dual-antenna GNSS and SINS, the coordinates are obtainedConverting into global coordinate system coordinates:
wherein ,representing any ground point/>Coordinates in the lidar coordinate system, +.>Representing the ground point +.>Coordinates under inertial navigation coordinate system, +.>Represents any ground point->Coordinates in the WGS84 geocentric geodetic coordinate system,>representing the position of the origin of the lidar coordinate system in the inertial navigation coordinate system, +>Rotation matrix representing the transformation of a lidar coordinate system into an inertial navigation coordinate system, +>Representing the position of the origin of the inertial navigation system in the WGS84 geocentric and geostationary space rectangular coordinate system, +>A rotation matrix representing the transformation of the inertial navigation coordinate system to a geostationary space rectangular coordinate system at WGS 84;
wherein ,rotation matrix of inertial navigation coordinate system to local horizontal coordinate system calculated according to three attitude angles of inertial navigation output +.>The rotation matrix from the local horizontal coordinate system to the WGS84 geocentric and geocentric space rectangular coordinate system is calculated according to the origin geodetic coordinates of the inertial navigation coordinate system;
will beSubstituted into- >Obtaining a tower crane laser scanning positioning equation:
calculating according to the tower crane laser scanning positioning equation to obtain ground pointsCoordinates in the local horizontal coordinate system:
wherein ,for the coordinates of the origin of the local horizontal coordinate system under the WGS84 coordinate system, +.>Is that
WGS84 geocentric fixes the rotation matrix of the space rectangular coordinate system to the local horizontal coordinate system.
6. The tower crane collision early warning method based on the multi-integration system according to claim 5, wherein the optimizing the laser radar space placement parameter by using the LM algorithm to obtain the construction site cloud base map comprises the following steps:
determining the point of any groundPerforming laser scanning twice to obtain a two-time laser point cloud positioning equation:
wherein ,、/>、/> and />、/>、/>Is +.>The measured values in the lidar coordinate system,、/>、/> and />Is the first scan +.>A rotation matrix formed by the position and the gesture of moment inertial navigation,、/>、/> and />Is a second scan->A rotation matrix formed by the position and the posture of moment inertial navigation;
will be in the first scanMeasurement values in the lidar coordinate system minus +.>Measured values in a laser radar coordinate system are obtained by:
will beThree translation parameters of the representation +. >The three rotation matrices of the angular representations of the rotation around the X-axis, Y-axis and Z-axis, respectively, are converted into +.>, wherein />Respectively and->Three translation parameters of the representation correspond to +.>Respectively and->The three represented rotation matrices respectively correspond to rotation matrices represented by angles of rotation around an X axis, a Y axis and a Z axis;
by LM algorithm pairPerforming optimization solution and iteration to obtain a mathematical model, wherein />Is a set of nonlinear equations +.>Represents any group number,/->Representing the total group number based on a mathematical model +.>Solving the minimum value for the optimal solution>The method comprises the following steps:
。
7. the tower crane collision early warning method based on the multi-integration system according to claim 1, wherein the steps of dividing and extracting the laser radar point cloud data or calculating by using the encoder observation data to obtain a hoisted object coordinate, and generating a hoisted object bounding box by combining the hook receiver carrier phase difference positioning result include:
projecting the laser radar point cloud data to a local horizontal coordinate system, and converting the carrier phase difference positioning result of the hook receiver from a WGS84 coordinate system to the local horizontal coordinate system to obtain hook position information；
Dividing the laser radar point cloud data into a ground point cloud and a non-ground point cloud by adopting point cloud filtering, and extracting a non-ground patch point cloud from the non-ground point cloud to serve as a lifting object extraction target area;
Connectivity analysis is carried out on the non-ground surface patch point cloud, adjacent non-ground surface patches are combined to serve as target areas to be determined, and the non-building target areas are removed according to the geometric dimension of a building and the elevation difference between the boundary of the building and the ground;
using the hook position informationDetermining the distance from the->The area within the preset distance range is a hoisting object target, and the rest areas are building targets; or if it is confirmed that the GNSS signal at the hook is blocked, calculating the hook position according to the trolley moving distance and the hook descending distance obtained by the encoder measurement and pose information obtained by combining the dual-antenna GNSS and the SINS>According to->Screening to obtain the hoisted object target;
projecting the three-dimensional outer bounding box of the hoisted object target on a two-dimensional plane to generate a two-dimensional overlooking projection, rotating the two-dimensional overlooking projection around a projection center, and taking the circumscribed rectangle of the rotation range as the hoisted object bounding box.
8. The tower crane collision warning method based on the multi-integration system according to claim 7, wherein the dividing the laser radar point cloud data into a ground point cloud and a non-ground point cloud by using point cloud filtering, extracting a non-ground patch point cloud from the non-ground point cloud as a lifting object extraction target area, comprises:
Processing the laser radar point cloud data by adopting a virtual grid, determining the size of a preset grid, traversing each grid, determining the lowest point in each grid as each grid point, taking the rest point clouds in each grid as other point clouds, and supplementing the grids without the point clouds by adopting virtual point interpolation to obtain grid point clouds;
performing self-adaptive partitioning on the grid point cloud by utilizing point cloud partitioning to obtain a patch set and a discrete independent point cloud set;
respectively processing the surface patch set and the discrete independent point cloud set through point cloud segmentation filtering and multi-scale morphological filtering to obtain a ground grid point cloud and a non-ground grid point cloud;
and adopting smooth constraint point cloud segmentation to divide the ground point cloud and the non-ground point cloud into the ground point cloud and the non-ground point cloud respectively, wherein the non-ground point cloud comprises a non-ground surface patch and a non-ground discrete independent point cloud.
9. The tower crane collision pre-warning method based on the multi-integration system according to claim 1, wherein performing collision detection on the building outline bounding box and the hoisted object bounding box to obtain spatial relationship and collision pre-warning information of the hoisted object and the building, comprises:
Carrying out index construction on the building outline bounding box by adopting an axis parallel bounding box AABB tree;
performing collision detection on the building outline bounding box and the hoisted object bounding box based on an AABB tree, traversing the AABB tree of the building outline bounding box and the hoisted object bounding box, and obtaining a building outline bounding box bottom graphic primitive closest to the hoisted object bounding box;
and sequentially calculating the distance vector from each corner point in the hoisted object bounding box to the building outline bounding box bottom graphic element, and determining the minimum collision distance according to the distance vector.
10. The tower crane collision early warning method based on the multi-integration system according to claim 1, wherein after collision detection is performed on the building outline bounding box and the hoisting object optimal outer bounding box to obtain spatial relationship and collision early warning information of the hoisting object and the building, the method further comprises:
the tower foundation side service end sends the construction site cloud base map, the building outline bounding box, the hoisted object bounding box and the collision early warning information to a ground client;
and the ground client displays the hoisting operation three-dimensional environment, collision distance information and potential collision targets through visual software.
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