CN113819912A - High-precision point cloud map generation method based on multi-sensor data - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/265—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network constructional aspects of navigation devices, e.g. housings, mountings, displays
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
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Abstract
The invention relates to the technical field of automatic driving, in particular to a high-precision point cloud map generation method based on multi-sensor data, which comprises the following steps: s1, the data acquisition vehicle runs in the area to be driven of the automatic driving vehicle, and original geographic data in the area to be driven are acquired by utilizing a vehicle-mounted sensor, a GNSS global positioning system and an inertial navigation system which are arranged on the data acquisition vehicle; s2, performing off-line processing on data acquired by a vehicle-mounted sensor, a GNSS global positioning system and an inertial navigation system, performing data frame time alignment, and performing point cloud data processing through a laser radar; s3, extracting key frames of the laser radar data by using a point cloud matching algorithm, and calculating the transformation relation of adjacent key frames by using self-adaptive parameters; … … S6, constructing a pose graph by using the extracted key frames, and optimizing to obtain a global optimal pose; and S7, splicing and storing the point clouds to generate a high-precision point cloud map.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to a high-precision point cloud map generation method based on multi-sensor data.
Background
The research on the automatic driving technology has attracted much attention in recent years, and automobile manufacturers, IT enterprises, universities and research institutions, and the like, both at home and abroad, are invested in and actively explore in the field. The automatic driving technology is considered as a feasible solution to the traffic efficiency and safety problems.
In the current classical technical framework of autopilot, high-precision positioning is the basic but most important factor, the most fundamental guarantee for determining the accuracy of autopilot perception, the reliability of decision planning and the accuracy of control execution. According to different positioning realization technologies in automatic driving, high-precision positioning can be divided into three types: signal-based positioning, typically represented by GNSS positioning, i.e. global satellite navigation system; 2, a dead reckoning-IMU inertial measurement unit which infers the current position and orientation according to the position and orientation at the previous moment; and 3, matching environmental features, namely matching the observed features with the features stored in the database based on the positioning of the laser radar/stereoscopic vision to obtain the position and the posture of the vehicle at the current moment. The high-precision point cloud map is used as a static prior, and the position of the automatic driving vehicle can be obtained in real time according to the point cloud data of the laser radar.
The collection and production of the existing high-precision point cloud map can be roughly divided into two types, namely a laser radar-based point cloud and an image-based method. The high-precision map making scheme based on the visual image is low in cost and convenient to operate, but has higher requirements on picture quality, acquisition environment and the like under the prior art. The manufacturing scheme based on the laser radar point cloud is less affected by working conditions and the like, data acquisition can be carried out under various conditions, post-processing is convenient, and a high-precision point cloud map can be conveniently and quickly generated.
Chinese patent publication No. CN106441319A provides a system and method for generating a lane-level navigation map of an unmanned vehicle. The system comprises an offline global map and an online local map, wherein an offline module acquires original data in an unmanned vehicle form area by using a satellite photo, a vehicle-mounted sensor and high-precision combined positioning, then extracts road information through data processing, and finally fuses road information extraction results to generate the global map. And the online module extracts road data in the offline global map according to the real-time positioning information during the driving of the unmanned vehicle, and draws an online local map which takes the vehicle as the center and is within a fixed distance range.
Chinese patent publication No. CN104573733A provides a high-precision map generation system and method based on high-definition orthographic maps. In the system, a vehicle-mounted image acquisition system is used for acquiring road data, and an algorithm is used for processing to obtain a global map. Based on this map, labeling of road signs is performed.
When the method is operated in a large range, the accumulated error is increased under the influence of the change of geographic features, the problem of loop detection in the generation of a high-precision point cloud map is not considered, and the closed loop, the integrity and the consistency of the map are ensured.
Disclosure of Invention
The invention aims to provide a high-precision point cloud map generation method based on multi-sensor data, and solves the problem that the conventional positioning system of an automatic driving vehicle is easily influenced by geographical feature changes, so that accumulated errors are increased, and the driving precision is influenced.
In order to solve the technical problems, the invention adopts the following technical scheme:
a high-precision point cloud map generation method based on multi-sensor data comprises the following steps: s1, the data acquisition vehicle runs in the area to be driven of the automatic driving vehicle, and original geographic data in the area to be driven are acquired by utilizing a vehicle-mounted sensor, a GNSS global positioning system and an inertial navigation system which are arranged on the data acquisition vehicle; s2, performing off-line processing on data acquired by a vehicle-mounted sensor, a GNSS global positioning system and an inertial navigation system, performing data frame time alignment, and performing point cloud data processing through a laser radar; s3, extracting key frames of the laser radar data by using a point cloud matching algorithm, and calculating the transformation relation of adjacent key frames by using self-adaptive parameters; s4, performing multiple scanning rounds on the key frame, and adding a robust loop constraint condition by using different parameters; s5, adding GNSS positioning coordinate constraint to the key frame; s6, constructing a pose graph by using the extracted key frames, and optimizing to obtain a global optimal pose; and S7, splicing and storing the point clouds to generate a high-precision point cloud map.
The technical scheme is that the vehicle-mounted sensor is mounted on the roof of the data acquisition vehicle through a protective cover, a mounting cavity is formed in the protective cover, and the vehicle-mounted sensor is mounted in the mounting cavity.
According to a further technical scheme, the protective cover comprises a shell and a base which are detachably connected.
A further technical scheme is that a vertical groove and a transverse groove which are connected in an L shape are arranged on the side face of the base, the upper end of the vertical groove is communicated with the upper side of the base, the lower end of the vertical groove is communicated with the transverse groove, a locking rod is connected to the inner side of the shell, the locking rod penetrates into the vertical groove from the upper end of the vertical groove, and the lower end of the vertical groove horizontally moves into the transverse groove to enable the shell to be connected with the base buckle.
The technical scheme is that the upper groove wall and the lower groove wall of the transverse groove are provided with a plurality of elastic particles which are uniformly arranged along the length direction of the transverse groove.
The technical scheme is that the base is provided with an air exhaust hole and an air inlet hole which are through up and down, an exhaust fan is arranged above the air exhaust hole on the upper side of the base, and an air inlet fan is arranged above the air inlet hole.
The further technical scheme is that a plurality of grids are rotatably arranged in the air inlet hole and are rotatably connected with the hole wall of the air inlet hole through a rotating shaft.
According to a further technical scheme, a containing groove is formed in the hole wall of the air inlet hole, a motor is installed in the containing groove, a rotating wheel is connected to an output shaft of the motor, a pulling rope is sleeved on the rotating wheel in a transmission mode, the upper end of the pulling rope is sequentially connected with a plurality of grids on the upper side of a rotating shaft, and the lower end of the pulling rope is sequentially connected with the grids on the lower side of the rotating shaft.
A further technical scheme is that a raindrop sensor is arranged at the top of the shell and connected with the motor and the air inlet fan through a control module.
Compared with the prior art, the invention has the beneficial effects that: by arranging the vehicle-mounted sensor, the GNSS global positioning system and the inertial navigation system on the collection vehicle, the original geographic data in the area to be driven can be collected in various modes, the diversity of the original geographic data is improved, and the single geographic data is prevented from being easy to generate errors. The accuracy of the original geographic data is further improved by carrying out data frame time alignment, and the accuracy and the stability of subsequent data processing are improved by the self-adaptive matching of the point cloud data of the laser radar and the robust loop detection. The problem that the conventional positioning system of the automatic driving vehicle is easily influenced by geographical feature changes, so that accumulated errors are increased, and the driving precision is influenced is solved through the high-precision point cloud map generated in the steps S1-S7.
Drawings
FIG. 1 is a schematic step diagram of a high-precision point cloud map generation method based on multi-sensor data according to the present invention.
Fig. 2 is a schematic connection diagram of a shell and a base of the high-precision point cloud map generation method based on multi-sensor data.
Fig. 3 is a schematic connection diagram of a locking rod and a transverse groove of the high-precision point cloud map generation method based on multi-sensor data.
Fig. 4 is a schematic cross-sectional view of an air inlet 204 of a high-precision point cloud map generation method based on multi-sensor data according to the present invention.
Invention icon: 1-shell, 101-locking rod, 2-base, 201-vertical groove, 202-horizontal groove, 203-exhaust hole, 204-intake hole, 205-exhaust fan, 206-intake fan, 207-grille, 208-rotating shaft, 209-containing groove, 210-motor, 211-rotating wheel, 212-pull rope and 3-working chamber.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example (b):
a high-precision point cloud map generation method based on multi-sensor data comprises the following steps: s1, the data acquisition vehicle runs in the area to be driven of the automatic driving vehicle, and original geographic data in the area to be driven are acquired by utilizing a vehicle-mounted sensor, a GNSS global positioning system and an inertial navigation system which are arranged on the data acquisition vehicle; s2, performing off-line processing on data acquired by a vehicle-mounted sensor, a GNSS global positioning system and an inertial navigation system, performing data frame time alignment, and performing point cloud data processing through a laser radar; s3, extracting key frames of the laser radar data by using a point cloud matching algorithm, and calculating the transformation relation of adjacent key frames by using self-adaptive parameters; s4, performing multiple scanning rounds on the key frame, and adding a robust loop constraint condition by using different parameters; s5, adding GNSS positioning coordinate constraint to the key frame; s6, constructing a pose graph by using the extracted key frames, and optimizing to obtain a global optimal pose; and S7, splicing and storing the point clouds to generate a high-precision point cloud map. By arranging the vehicle-mounted sensor, the GNSS global positioning system and the inertial navigation system on the collection vehicle, the original geographic data in the area to be driven can be collected in various modes, the diversity of the original geographic data is improved, and the single geographic data is prevented from being easy to generate errors. The accuracy of the original geographic data is further improved by carrying out data frame time alignment, and the accuracy and the stability of subsequent data processing are improved by the self-adaptive matching of the point cloud data of the laser radar and the robust loop detection. The problem that the conventional positioning system of the automatic driving vehicle is easily influenced by geographical feature changes, so that accumulated errors are increased, and the driving precision is influenced is solved through the high-precision point cloud map generated in the steps S1-S7.
The vehicle-mounted sensor is installed on the roof of the data acquisition vehicle through the protective cover, a mounting cavity 3 is formed in the protective cover, and the vehicle-mounted sensor is installed in the mounting cavity 3. Through setting up vehicle sensor in the roof, the vehicle sensor of being convenient for gathers raw geographic data, through setting up the safety cover, can effectually protect vehicle sensor, avoids the rainwater to drench vehicle sensor and leads to vehicle sensor trouble.
The protective cover comprises a shell 1 and a base 2 which are detachably connected. With such an arrangement, it is convenient to repair or adjust the in-vehicle sensor by detaching the housing 1 and the base 2. The convenience of operation and maintenance has been promoted.
The side of base 2 is provided with and is "L" font continuous perpendicular groove 201 and transverse groove 202, and the upper end of perpendicular groove 201 is linked together with base 2's upside, and lower extreme and transverse groove 202 are linked together, and casing 1's inboard is connected with check lock lever 101, and check lock lever 101 is worn into in perpendicular groove 201 from the upper end of perpendicular groove 201 to in the lower extreme horizontal migration who erects groove 201 reaches transverse groove 202, so that casing 1 and base 2 buckle link to each other. Such setting, when the hand is waved to needs connection casing 1 and base 2, through the upper end that aligns vertical slot 201 with the check lock pole 101 of casing 1, then make check lock pole 101 slide to the lower extreme of vertical slot 201 from the upper end of vertical slot 201, then through rotatory casing 1, let check lock pole 101 slide to the horizontal slot 202 in from vertical slot 201, just so can make casing 1 and base 2 buckle continuous. The locking rod 101 on the housing 1 and the matched vertical groove 201 and horizontal groove 202 on the base 2 can be arranged in a plurality of numbers, so that the housing 1 and the base 2 can be connected more stably and firmly. When the housing 1 and the base 2 are separated, the housing 1 is rotated in the opposite direction, so that the locking rod 101 moves from the transverse groove 202 to the vertical groove 201, and then slides out from the upper end of the vertical groove 201.
The upper groove wall and the lower groove wall of the transverse groove 202 are provided with a plurality of elastic particles which are uniformly arranged along the length direction of the transverse groove 202. Through setting up the elastic particle, can increase connection stability between check lock lever 101 and the horizontal groove 202, through a plurality of align to grid's elastic particle, can be that the stable card of check lock lever 101 locates in the horizontal groove 202, like this at the in-process that the data acquisition vehicle went, casing 1 and base 2 can not drop, have promoted the stability of structure. The base 2 is provided with an air outlet 203 and an air inlet 204 which are vertically through, an air exhaust fan 205 is arranged above the air outlet 203 on the upper side of the base 2, and an air inlet fan 206 is arranged above the air inlet 204. Through setting up exhaust hole 203, inlet port 204, exhaust fan 205 and air inlet fan 206, can be when the hot temperature of weather is too high, can be with the high temperature air discharge in the installation cavity 3 through exhaust hole 203, inlet port 204, exhaust fan 205 and air inlet fan 206, then the lower air that shows of temperature of trading in, can make keep suitable temperature in the installation cavity 3 like this, avoid the hot and vehicle-mounted sensor self work to generate heat and lead to the installation cavity 3 interior temperature too high, influence vehicle-mounted sensor's precision.
A plurality of grilles (207) are rotatably arranged in the air inlet hole (204), and the grilles (207) are rotatably connected with the hole wall of the air inlet hole (204) through a rotating shaft (208). When the air inlet holes 204 are rotatably connected with the plurality of grills 207 through the rotating shaft 208, in this way, when the air inlet fan 206 is not needed to be used or rain falls, all the grills 207 are rotated to the horizontal state or the nearly horizontal state around the rotating shaft 208 to block the air inlet holes 204, and rain water or sundries are prevented from entering the installation cavity 3 from the air inlet holes 204.
The hole wall of the air inlet hole 204 is provided with a containing groove 209, a motor 210 is installed in the containing groove 209, an output shaft of the motor 210 is connected with a rotating wheel 211, a pulling rope 212 is sleeved on the rotating wheel 211 in a transmission mode, the upper end of the pulling rope 212 is sequentially connected with the plurality of grids 207 on the upper side of the rotating shaft 208, and the lower end of the pulling rope 212 is sequentially connected with the plurality of grids 207 on the lower side of the rotating shaft 208. With the arrangement, when the air inlet holes 204 need to be closed, the motor 210 is controlled to operate, so that the motor 210 drives the rotating wheel 211 to rotate, the rotating wheel 211 can drive the pull ropes 212 to move through rotation, when the rotating wheel 211 rotates clockwise, the pull ropes 212 on the upper side of the rotating wheel 211 (namely the upper ends of the pull ropes 212) move towards the rotating wheel, so that the upper ends of all the grids 207 can be driven to incline towards one side of the rotating wheel 211, and meanwhile, the pull ropes 212 on the lower side of the rotating wheel (namely the lower ends of the pull ropes 212) are far away from the rotating wheel 211 through the inclined grids 207. The motor 210 stops working until the pull rope 212 drives the grille 207 to rotate around the rotating shaft 208 to the horizontal state or the nearly horizontal state to block the air inlet hole 204. And the supply fan 206 is also deactivated. When the air supply fan 206 is needed, the motor 210 is only needed to drive the rotating wheel 211 to rotate reversely, so that all the grilles 207 are in a vertical state.
The top of the housing 1 is provided with a raindrop sensor, which is connected to the motor 210 and the air intake fan 206 via a control module. Through setting up raindrop sensor and control module, come the work of automatic control motor 210 and air inlet fan 206 through control module when can realizing raining, can open and seal inlet port 204 like this according to weather conditions automatic control. The exhaust hole 203 and the exhaust fan 205 may be disposed in the same manner as the intake hole 204 and the intake fan 206.
Although the invention has been described herein with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More specifically, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, other uses will also be apparent to those skilled in the art.
Claims (9)
1. A high-precision point cloud map generation method based on multi-sensor data is characterized by comprising the following steps:
s1, the data acquisition vehicle runs in the area to be driven of the automatic driving vehicle, and original geographic data in the area to be driven are acquired by utilizing a vehicle-mounted sensor, a GNSS global positioning system and an inertial navigation system which are arranged on the data acquisition vehicle;
s2, performing off-line processing on data acquired by a vehicle-mounted sensor, a GNSS global positioning system and an inertial navigation system, performing data frame time alignment, and performing point cloud data processing through a laser radar;
s3, extracting key frames of the laser radar data by using a point cloud matching algorithm, and calculating the transformation relation of adjacent key frames by using self-adaptive parameters;
s4, performing multiple scanning rounds on the key frame, and adding a robust loop constraint condition by using different parameters;
s5, adding GNSS positioning coordinate constraint to the key frame;
s6, constructing a pose graph by using the extracted key frames, and optimizing to obtain a global optimal pose;
and S7, splicing and storing the point clouds to generate a high-precision point cloud map.
2. The method for generating a high-precision point cloud map based on multi-sensor data as claimed in claim 1, wherein: the vehicle-mounted sensor is installed on the roof of the data acquisition vehicle through a protective cover, a mounting cavity (3) is formed in the protective cover, and the vehicle-mounted sensor is installed in the mounting cavity (3).
3. The method for generating a high-precision point cloud map based on multi-sensor data as claimed in claim 2, wherein: the protective cover comprises a shell (1) and a base (2) which are detachably connected.
4. The method for generating a high-precision point cloud map based on multi-sensor data as claimed in claim 3, wherein: the side of base (2) is provided with and is "L" font continuous perpendicular groove (201) and cross slot (202), the upper end of perpendicular groove (201) is linked together with the upside of base (2), and the lower extreme is linked together with cross slot (202), the inboard of casing (1) is connected with check lock lever (101), check lock lever (101) are worn into in perpendicular groove (201) from the upper end of perpendicular groove (201) to in lower extreme horizontal migration to cross slot (202) of perpendicular groove (201), so that casing (1) and base (2) buckle link to each other.
5. The method for generating a high-precision point cloud map based on multi-sensor data as claimed in claim 4, wherein: the upper groove wall and the lower groove wall of the transverse groove (202) are both provided with a plurality of elastic particles which are uniformly arranged along the length direction of the transverse groove (202).
6. The method for generating a high-precision point cloud map based on multi-sensor data as claimed in claim 3, wherein: the base (2) is provided with an air exhaust hole (203) and an air inlet hole (204) which are through up and down, the upper side of the base (2) is provided with an exhaust fan (205) above the air exhaust hole (203), and an air inlet fan (206) is arranged above the air inlet hole (204).
7. The method for generating a high-precision point cloud map based on multi-sensor data as claimed in claim 6, wherein: a plurality of grilles (207) are rotatably arranged in the air inlet hole (204), and the grilles (207) are rotatably connected with the hole wall of the air inlet hole (204) through a rotating shaft (208).
8. The method for generating a high-precision point cloud map based on multi-sensor data as claimed in claim 7, wherein: be provided with on the pore wall of inlet port (204) and accomodate groove (209), accomodate and install motor (210) in groove (209), be connected with on the output shaft of motor (210) and rotate wheel (211), it is equipped with stay cord (212) to rotate the transmission cover on wheel (211), the upper end of stay cord (212) links to each other with a plurality of grids (207) in proper order at the upside of axis of rotation (208), the lower extreme of stay cord (212) links to each other with a plurality of grids (207) in proper order at the downside of axis of rotation (208).
9. The method for generating a high-precision point cloud map based on multi-sensor data according to claim 8, wherein the method comprises the following steps: the top of casing (1) is provided with the raindrop sensor, the raindrop sensor passes through control module and links to each other with motor (210) and air inlet fan (206).
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| CN114495018A (en) * | 2022-04-14 | 2022-05-13 | 深圳宇通智联科技有限公司 | Automatic data cleaning method for automatic driving mine card |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN208572631U (en) * | 2018-06-21 | 2019-03-01 | 北京四维图新科技股份有限公司 | The device of intelligent perception system |
| CN208987238U (en) * | 2018-08-29 | 2019-06-14 | 广东惠德科技发展有限公司 | A kind of Mobile communication base station machine room radiator |
| CN110261870A (en) * | 2019-04-15 | 2019-09-20 | 浙江工业大学 | It is a kind of to synchronize positioning for vision-inertia-laser fusion and build drawing method |
| CN209786177U (en) * | 2019-05-23 | 2019-12-13 | 四川电科安研视讯科技有限公司 | Directional antenna |
| CN210556450U (en) * | 2019-09-12 | 2020-05-19 | 武汉谊盛新材料科技有限公司 | Compound storage tank is moulded to wicking scaling powder steel |
| CN111578957A (en) * | 2020-05-07 | 2020-08-25 | 泉州装备制造研究所 | Intelligent pure vehicle tracking and tracking method based on three-dimensional point cloud map positioning |
| CN212545220U (en) * | 2020-05-18 | 2021-02-19 | 海南欣源生物科技有限公司 | Domestic mosquito killing device |
| CN112525202A (en) * | 2020-12-21 | 2021-03-19 | 北京工商大学 | SLAM positioning and navigation method and system based on multi-sensor fusion |
| CN213188539U (en) * | 2020-06-09 | 2021-05-14 | 阮李英 | Prevent cracked lipstick strorage device of lipstick |
| CN213422340U (en) * | 2020-08-21 | 2021-06-11 | 天津市金凤科技发展有限公司 | Jig tray for automatic assembly line water meter production |
| CN112950781A (en) * | 2021-03-19 | 2021-06-11 | 中山大学 | Point cloud map construction method for multi-sensor dynamic weighting fusion of special scene |
| CN113219492A (en) * | 2021-03-30 | 2021-08-06 | 苏州市卫航智能技术有限公司 | Method and system for positioning and navigating river course ship driving |
| CN214047564U (en) * | 2020-11-07 | 2021-08-27 | 常州市派腾电子技术服务有限公司 | Atomizer and aerosol generating device |
| CN113359154A (en) * | 2021-05-24 | 2021-09-07 | 邓良波 | Indoor and outdoor universal high-precision real-time measurement method |
| CN113447014A (en) * | 2021-08-30 | 2021-09-28 | 深圳市大道智创科技有限公司 | Indoor mobile robot, mapping method, positioning method, and mapping positioning device |
-
2021
- 2021-09-30 CN CN202111156443.6A patent/CN113819912A/en active Pending
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN208572631U (en) * | 2018-06-21 | 2019-03-01 | 北京四维图新科技股份有限公司 | The device of intelligent perception system |
| CN208987238U (en) * | 2018-08-29 | 2019-06-14 | 广东惠德科技发展有限公司 | A kind of Mobile communication base station machine room radiator |
| CN110261870A (en) * | 2019-04-15 | 2019-09-20 | 浙江工业大学 | It is a kind of to synchronize positioning for vision-inertia-laser fusion and build drawing method |
| CN209786177U (en) * | 2019-05-23 | 2019-12-13 | 四川电科安研视讯科技有限公司 | Directional antenna |
| CN210556450U (en) * | 2019-09-12 | 2020-05-19 | 武汉谊盛新材料科技有限公司 | Compound storage tank is moulded to wicking scaling powder steel |
| CN111578957A (en) * | 2020-05-07 | 2020-08-25 | 泉州装备制造研究所 | Intelligent pure vehicle tracking and tracking method based on three-dimensional point cloud map positioning |
| CN212545220U (en) * | 2020-05-18 | 2021-02-19 | 海南欣源生物科技有限公司 | Domestic mosquito killing device |
| CN213188539U (en) * | 2020-06-09 | 2021-05-14 | 阮李英 | Prevent cracked lipstick strorage device of lipstick |
| CN213422340U (en) * | 2020-08-21 | 2021-06-11 | 天津市金凤科技发展有限公司 | Jig tray for automatic assembly line water meter production |
| CN214047564U (en) * | 2020-11-07 | 2021-08-27 | 常州市派腾电子技术服务有限公司 | Atomizer and aerosol generating device |
| CN112525202A (en) * | 2020-12-21 | 2021-03-19 | 北京工商大学 | SLAM positioning and navigation method and system based on multi-sensor fusion |
| CN112950781A (en) * | 2021-03-19 | 2021-06-11 | 中山大学 | Point cloud map construction method for multi-sensor dynamic weighting fusion of special scene |
| CN113219492A (en) * | 2021-03-30 | 2021-08-06 | 苏州市卫航智能技术有限公司 | Method and system for positioning and navigating river course ship driving |
| CN113359154A (en) * | 2021-05-24 | 2021-09-07 | 邓良波 | Indoor and outdoor universal high-precision real-time measurement method |
| CN113447014A (en) * | 2021-08-30 | 2021-09-28 | 深圳市大道智创科技有限公司 | Indoor mobile robot, mapping method, positioning method, and mapping positioning device |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114495018A (en) * | 2022-04-14 | 2022-05-13 | 深圳宇通智联科技有限公司 | Automatic data cleaning method for automatic driving mine card |
| CN114495018B (en) * | 2022-04-14 | 2022-07-01 | 深圳宇通智联科技有限公司 | Automatic data cleaning method for automatic driving mine card |
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