CN112083441B - Obstacle detection method and system for depth fusion of laser radar and millimeter wave radar - Google Patents
Obstacle detection method and system for depth fusion of laser radar and millimeter wave radar Download PDFInfo
- Publication number
- CN112083441B CN112083441B CN202010944090.5A CN202010944090A CN112083441B CN 112083441 B CN112083441 B CN 112083441B CN 202010944090 A CN202010944090 A CN 202010944090A CN 112083441 B CN112083441 B CN 112083441B
- Authority
- CN
- China
- Prior art keywords
- millimeter wave
- laser radar
- radar
- target
- wave radar
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 42
- 230000004927 fusion Effects 0.000 title claims abstract description 21
- 239000000428 dust Substances 0.000 claims abstract description 27
- 230000008447 perception Effects 0.000 claims abstract description 8
- 238000007781 pre-processing Methods 0.000 claims abstract description 6
- 238000000034 method Methods 0.000 claims description 16
- 238000002592 echocardiography Methods 0.000 claims description 8
- 230000009466 transformation Effects 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 3
- 238000009434 installation Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 230000004888 barrier function Effects 0.000 claims 1
- 238000005516 engineering process Methods 0.000 description 5
- 230000007547 defect Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000007499 fusion processing Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
Images
Classifications
-
- 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
- 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
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
- G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
-
- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/87—Combinations of radar systems, e.g. primary radar and secondary radar
-
- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
-
- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/411—Identification of targets based on measurements of radar reflectivity
-
- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
-
- 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 discloses an obstacle detection method based on laser radar and millimeter wave radar depth fusion, which comprises the following steps: step 1, preprocessing original point cloud data of a laser radar and extracting targets, and then carrying out joint online calibration between the original point cloud data and the targets; step 2, eliminating a millimeter wave radar false alarm perception model; step 3, comparing the distance information of the obstacle returned by the millimeter wave with the point distance information returned by the laser radar, so as to filter out the interference of rain, fog and dust; and step 4, removing false detection information of the millimeter wave radar and the laser radar based on the step 3 and the step 4 respectively. According to the obstacle detection method based on the depth fusion of the laser radar and the millimeter wave radar, the high-precision detection based on the depth fusion of the laser radar and the millimeter wave radar can be effectively realized through the arrangement of the steps 1 to 4.
Description
Technical Field
The invention relates to an obstacle detection method based on depth fusion of a laser radar and a millimeter wave radar, in particular to an obstacle detection method and system based on depth fusion of the laser radar and the millimeter wave radar.
Background
Realizing the intellectualization of automobiles is an important trend of the development of automobile industry, and the environment perception technology is one of core technologies of intelligent vehicle technologies. The environment sensing technology provides environment information for technologies such as decision making and control by acquiring and analyzing sensor data. Among them, obstacle detection is one of important functions of the sensing system, and accuracy of detection results has an important influence on performance of the sensing system, and the detection results are mainly dependent on accuracy of detection results of the sensor. The millimeter wave radar easily generates false alarms on unstructured bumpy roads, the laser radar generates multi-echo phenomenon when encountering rain, fog and dust, and the inherent defects of the sensors can reduce the detection precision of a sensing system, so that the two are deeply fused according to the advantages of the laser radar and the millimeter wave radar, and the influence caused by the defects is reduced.
The patent with application number 201810070156.5 proposes a method for fusion detection of laser radar and millimeter wave radar based on lossless Kalman filtering, which comprises the steps of firstly carrying out data combination and marking data from different sensors, and then completing fusion of received data by using lossless Kalman filtering.
The patent with application number 201910190515.5 proposes a method for calibrating target distance information detected by a laser radar by utilizing target distance information detected by a millimeter wave radar, so as to improve the distance detection precision of the laser radar. According to the method, target information of the laser radar and target coordinate information of the millimeter wave radar are simultaneously converted into a coordinate system where a calibration device is located, and target distance detected by the laser radar is calibrated through calculation processing.
The patent with the application number of 201910840854.3 proposes to obtain the contour information of a target by using a single-line laser radar, obtain the transverse and longitudinal speed information of the target by using a millimeter wave radar, and improve the detection precision of obstacle information by integrating the laser radar and the millimeter wave target information.
The method for integrating the laser radar and the millimeter wave radar has the following main problems: the fusion is to fuse the detected data, and the influence caused by inherent defects of the sensor is not eliminated. The false alarms of the millimeter wave radar are not removed in the above patent, so that millimeter wave detection information used in the fusion process may be derived from the false alarms, thereby causing false detection; the laser radar generates multiple echoes when encountering rain, fog and dust, and the method does not distinguish the echoes of the target and the interference echoes generated by the rain, fog and dust, so that the detection accuracy is reduced to a certain extent.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to provide an obstacle detection method and system based on depth fusion of a laser radar and a millimeter wave radar.
In order to achieve the above purpose, the present invention provides the following technical solutions: an obstacle detection method based on depth fusion of a laser radar and a millimeter wave radar comprises the following steps:
step 4, based on the step 3 and the step 4, respectively removing false detection information of the millimeter wave radar and the laser radar, and then carrying out existence fusion on the detected targets, wherein the existence of the targets is larger than E 0 And fusing the states thereof.
As a further improvement of the present invention, the multi-level self-calibration procedure in step 1 is as follows:
step 11, preprocessing an original point cloud and extracting a target, and then unifying the millimeter wave detected target information under a laser radar coordinate system through registration;
step 12, setting the target set detected by the laser radar as
The target set detected by the millimeter wave radar is +.>
The method comprises the following steps:
wherein ,
indicating the target state of the ith lidar measurement at time t,/>
The j millimeter wave radar measuring target state at the moment t is shown;
the transformation matrix from the target of the millimeter wave radar to the laser radar coordinate system is T R2L The transformation formula is:
as a further improvement of the invention, the specific steps for eliminating the millimeter wave radar false alarm perception model in the step 2 are as follows:
step 21, according to the installation positions of the laser radar and the millimeter wave radar and the angle relation between the laser radar beams, the distribution of the corresponding points of the laser radar can be deduced through the target return value detected by the millimeter wave radar;
step 22, taking plane OXY as projection plane of laser radar point, projecting target point detected by laser radar to the plane, setting the set of projection points as omega L ＝{l 1 ,l 2 ,…,l n A set of distribution points presumed by millimeter waves is Ω R ＝{r 1 ,r 2 ,…,r m The distance error of the point projected to the depth map is:
wherein ,αi Defined as a distance distribution coefficient, satisfies Gaussian distribution, and sets an error determination threshold as d 0 When actually d<d 0 When the millimeter wave radar detection target is not a false alarm; if d>d 0 The target detected by the millimeter wave radar is a false alarm.
In another aspect, the present invention provides a system, including a target detection and tracking module, configured to execute a program that carries out the above method.
The method has the beneficial effects that through the arrangement of the steps 1 to 4, the multi-level sensor external parameter calibration can be effectively realized, the interference of false alarms of the millimeter wave radar is eliminated, and the interference of rain, fog and dust is filtered, so that the precision of the detection method based on the laser radar and the millimeter wave radar can be effectively improved.
Drawings
FIG. 1 is a schematic flow chart of the detection of a depth fusion target of a laser radar and a millimeter wave radar;
FIG. 2 is a flow chart of the multi-level sensor joint calibration in accordance with the present invention;
FIG. 3 is a schematic diagram of a false alarm fusion perception model excluding millimeter wave radars;
fig. 4 is a schematic diagram of a laser radar perception model in a rain, fog and dust scene.
Detailed Description
The invention will be further described in detail with reference to examples of embodiments shown in the drawings.
As shown in fig. 1, the flow chart of the obstacle detection method of the depth fusion of the laser radar and the millimeter wave radar according to the invention mainly comprises the following steps:
1) The multi-level sensor is calibrated in a combined and online manner. The levels related to the invention mainly comprise a data level and a target level, and the data of different levels are converted through processing. For the homogeneous sensor, the data combination can be directly calibrated on line, and the heterogeneous sensor needs to be preprocessed firstly because the original data are different. The heterogeneous sensor related to the patent refers to joint calibration between a laser radar and a millimeter wave radar, because the millimeter wave radar outputs target information, at the moment, the original point cloud data of the laser radar is required to be preprocessed and target extraction is required, and then joint online calibration between the two is carried out. And the methods of pretreatment, target extraction and joint online calibration are not limited herein.
2) And eliminating the millimeter wave radar false alarm perception model. And (3) transforming the target detected by the millimeter wave radar into a laser radar coordinate system through the space synchronization in the step 1). According to the position relation between the laser radar and the millimeter wave radar and the wire harness characteristics of the laser radar, the possible point cloud distribution of the target can be estimated through the target information returned by the millimeter wave radar, and the points are projected to the depth map of the laser radar; if an obstacle exists in the current environment, the point cloud information detected by the laser radar is projected to the depth map, and whether the target is a false alarm or not is judged by comparing the distance errors among the projected points of different sensors.
3) False detection scene of laser radar. When the laser radar detects obstacles, if the laser radar encounters interference of rain, fog and dust, multiple echoes can be generated, and erroneous judgment is caused; the millimeter wave is not affected by the rain dust, and can directly pass through the rain dust, and the returned information is from the obstacle. According to the characteristic that millimeter wave energy directly penetrates through rain, fog and dust, distance information of obstacles returned by millimeter waves is compared with point distance information returned by a laser radar, and therefore interference of the rain, fog and dust is filtered.
4) Based on the steps 3) and 4), respectively removing false detection information of the millimeter wave radar and the laser radar, and then carrying out presence fusion on the detected targets, wherein the presence of the targets is larger than E 0 And fusing the states thereof.
The multi-level self-calibration flow in step 1) is shown in fig. 2, and because the data levels of the laser point cloud data and the millimeter wave radar are different, the point cloud data needs to be processed to be converted into information of a target level, and the processing mainly comprises: preprocessing the original point cloud, extracting the target, and then unifying the millimeter wave detected target information under a laser radar coordinate system through registration. Let the target set detected by the laser radar be
The target set detected by the millimeter wave radar is +.>
The method comprises the following steps:
wherein ,
indicating the target state of the ith lidar measurement at time t,/>
And the target state of the jth millimeter wave radar measurement at the moment t is represented.
The transformation matrix from the target of the millimeter wave radar to the laser radar coordinate system is T R2L The transformation formula is:
the virtual alarm perception model excluding the millimeter wave radar in the step 2) is shown in fig. 3: according to the installation positions of the laser radar and the millimeter wave radar and the angle relation between the laser radar beams, the distribution of the corresponding points of the laser radar can be deduced through the target return value detected by the millimeter wave radar. As shown in fig. 3, after calibration, the coordinate of the target a detected by the millimeter wave radar in the laser radar coordinate system is s 0 (x 0 ,y 0 ,z 0 ) Then the point cloud distribution of target A can be extrapolated, e.g., we can extrapolate s 1 ,s 2 And deducing the coordinates s 'of the corresponding neighboring points according to the horizontal angular resolution of the lidar' 1 ,s′ 2 . In this step at point s 2 、s′ 2 An example is described.
Assuming that after the calibration in the step 1), the coordinate of the target detected by the millimeter wave radar under the laser radar coordinate system is s 0 (x 0 ,y 0 ,z 0 ) The horizontal angle resolution of the laser radar is
The inferred point s 2 The coordinates are (x) 2 ,y 2 ,z 2 ) According to the geometrical relationship:
h 2 ＝H-L·tanβ 2 (2)
wherein ：
therefore, it is
z 2 ＝z 0 +h 2 (4)
And, in addition, the method comprises the steps of,
x 2 ＝x 0 (5)
y 2 ＝y 0 (6)
so s is 2 Is s in the coordinate of 2 ＝(x 0 ,y 0 ,z 0 +h 2 )。
From the horizontal angular resolution of the lidar, it is possible to:
thus can obtain s' 2 ＝(x 0 ,y 0 +s′ 2 s 2 ,z 0 +h 2 ) The distribution of other points belonging to object a can be obtained in the same way.
As shown in fig. 3), a plane OXY is taken as a projection plane of the laser radar point, the target point detected by the laser radar is projected onto the plane, and the set of the projection points is set as Ω L ＝{l 1 ,l 2 ,…,l n A set of distribution points presumed by millimeter waves is Ω R ＝{r 1 ,r 2 ,…,r m The distance error of the point projected to the depth map is:
wherein ,αi Defined as the distance distribution coefficient, satisfying the gaussian distribution.
Let the error determination threshold be d 0 When actually d<d 0 When the millimeter wave radar detection target is not a false alarm; if d>d 0 The target detected by the millimeter wave radar is a false alarm.
As shown in fig. 4, in the false detection scenario of the laser radar in the step 3), under the condition that dust or rain and fog exist on an unstructured road, the scanning line of the laser radar also generates echoes when encountering the particles, returns to the laser receiver, and the truly existing target also generates echoes, so that a multi-echo phenomenon is caused, and serious interference is caused to the detection of the laser radar; however, millimeter wave radar detection is not affected by these environmental factors, millimeter waves can directly penetrate through these interferences, targets are detected, and interference of rain, fog and dust can be eliminated by comparing distance information of returned points.
The points returned by the first echo and the second echo are respectively recorded as a set L 1 ,L 2 The detection of millimeter wave radar is denoted as R. Wherein:
L 1 ＝{l 1,1 ,l 1,2 ,…,l 1,p } (9)
L 2 ＝{l 2,1 ,l 2,2 ,…,l 2,q } (10)
R＝{r 1 ,r 2 …,r k } (11)
by comparing the target distance information of the millimeter wave radar with the distance information of different echoes of the laser radar:
if d R1 <d R2 The probability of the second echo being dust or rain, fog and dust is larger; if d R1 >d R2 The probability that the first echo is dust or rain, fog and dust is high, and the dust or rain, fog and dust can be filtered according to the comparison result.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the present invention may occur to one skilled in the art without departing from the principles of the present invention and are intended to be within the scope of the present invention.
Claims (4)
1. A barrier detection method based on laser radar and millimeter wave radar depth fusion is characterized in that: the method comprises the following steps:
step 1, preprocessing original point cloud data of a laser radar and extracting targets, and then carrying out joint online calibration between the original point cloud data and the targets;
step 2, according to the position relation between the laser radar and the millimeter wave radar and the wiring harness characteristics of the laser radar, the possible point cloud distribution of the target can be estimated through the target information returned by the millimeter wave radar, and the points are projected to the depth map of the laser radar; if an obstacle exists in the current environment, the point cloud information detected by the laser radar is projected to a depth map, and whether the target is a false alarm or not is judged by comparing the distance errors among projection points of different sensors;
step 3, comparing the distance information of the obstacle returned by the millimeter wave with the point distance information returned by the laser radar, so as to filter the interference of rain, fog and dust, wherein the specific filtering steps are as follows;
the points returned by the first echo and the second echo are respectively recorded as a set L 1 ，L 2 The detection of a millimeter wave radar is denoted R, where:
L 1 ＝{l 1，1 ，l 1，2 ，...，l 1，p }
L 2 ＝{l 2，1 ，l 2，2 ，…，l 2，q }
R＝{r 1 ，r 2 …，r k }
by comparing the target distance information of the millimeter wave radar with the distance information of different echoes of the laser radar:
if d R1 ＜d R2 The probability of the second echo being dust or rain, fog and dust is larger; if d R1 ＞d R2 The probability of the first echo being dust or rain, fog and dust is high according toThe comparison result can filter out dust or rain, fog and dust; step 4, based on the step 3 and the step 4, respectively removing false detection information of the millimeter wave radar and the laser radar, and then carrying out existence fusion on the detected targets, wherein the existence of the targets is larger than E 0 And fusing the states thereof.
2. The obstacle detection method based on the depth fusion of the laser radar and the millimeter wave radar according to claim 1, wherein the obstacle detection method comprises the following steps: the multi-level self-calibration flow in the step 1 is as follows:
step 11, preprocessing an original point cloud and extracting a target, and then unifying the millimeter wave detected target information under a laser radar coordinate system through registration;
step 12, setting the target set detected by the laser radar as
The target set detected by the millimeter wave radar is +.>
The method comprises the following steps:
wherein ,
indicating the target state of the ith lidar measurement at time t,/>
The j millimeter wave radar measuring target state at the moment t is shown;
the transformation matrix from the target of the millimeter wave radar to the laser radar coordinate system is T R2L The transformation formula is:
3. the obstacle detection method based on the depth fusion of the laser radar and the millimeter wave radar according to claim 2, wherein: the specific steps for eliminating the millimeter wave radar false alarm perception model in the step 2 are as follows:
step 21, according to the installation positions of the laser radar and the millimeter wave radar and the angle relation between the laser radar beams, the distribution of the corresponding points of the laser radar can be deduced through the target return value detected by the millimeter wave radar;
step 22, taking plane OXY as projection plane of laser radar point, projecting target point detected by laser radar to the plane, setting the set of projection points as omega L ＝{l 1 ，l 2 ，...，l n A set of distribution points presumed by millimeter waves is Ω R ＝{r 1 ，r 2 ，...，r m The distance error of the point projected to the depth map is:
wherein ,αi Defined as a distance distribution coefficient, satisfies Gaussian distribution, and sets an error determination threshold as d 0 When the actual d is less than d 0 When the millimeter wave radar detection target is not a false alarm; if d > d 0 The target detected by the millimeter wave radar is a false alarm.
4. A system for applying the method of any one of claims 1 to 3, characterized in that: the system comprises a target detection and tracking module for executing a program carrying the method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010944090.5A CN112083441B (en) | 2020-09-10 | 2020-09-10 | Obstacle detection method and system for depth fusion of laser radar and millimeter wave radar |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010944090.5A CN112083441B (en) | 2020-09-10 | 2020-09-10 | Obstacle detection method and system for depth fusion of laser radar and millimeter wave radar |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112083441A CN112083441A (en) | 2020-12-15 |
| CN112083441B true CN112083441B (en) | 2023-04-21 |
Family
ID=73732273
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010944090.5A Active CN112083441B (en) | 2020-09-10 | 2020-09-10 | Obstacle detection method and system for depth fusion of laser radar and millimeter wave radar |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112083441B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113158763B (en) | 2021-02-23 | 2021-12-07 | 清华大学 | Three-dimensional target detection method based on multi-view feature fusion of 4D millimeter waves and laser point clouds |
| CN112986982B (en) * | 2021-05-12 | 2021-07-30 | 长沙万为机器人有限公司 | Environment map reference positioning method and device and mobile robot |
| CN113296120B (en) * | 2021-05-24 | 2023-05-12 | 福建盛海智能科技有限公司 | Obstacle detection method and terminal |
| CN113701577B (en) * | 2021-08-23 | 2023-04-28 | 中国北方工业有限公司 | Layout method of active laser and active millimeter wave common-caliber composite detection device |
| CN113687349A (en) * | 2021-09-23 | 2021-11-23 | 上海大学 | Unmanned ship sea surface target tracking method and device based on multi-sensor fusion |
| CN114814826B (en) * | 2022-04-08 | 2023-06-16 | 苏州大学 | Radar orbit area environment sensing method based on target grid |
| CN114994684B (en) * | 2022-06-01 | 2023-05-12 | 湖南大学无锡智能控制研究院 | Method and system for detecting obstacle in dust scene of multi-radar data fusion |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106405555A (en) * | 2016-09-23 | 2017-02-15 | 百度在线网络技术（北京）有限公司 | Obstacle detecting method and device used for vehicle-mounted radar system |
| CN106908783A (en) * | 2017-02-23 | 2017-06-30 | 苏州大学 | Obstacle detection method based on multi-sensor information fusion |
| CN108226883A (en) * | 2017-11-28 | 2018-06-29 | 深圳市易成自动驾驶技术有限公司 | Test the method, apparatus and computer readable storage medium of millimetre-wave radar performance |
| CN108226906A (en) * | 2017-11-29 | 2018-06-29 | 深圳市易成自动驾驶技术有限公司 | A kind of scaling method, device and computer readable storage medium |
| CN108509972A (en) * | 2018-01-16 | 2018-09-07 | 天津大学 | A kind of barrier feature extracting method based on millimeter wave and laser radar |
| CN110726993A (en) * | 2019-09-06 | 2020-01-24 | 武汉光庭科技有限公司 | Obstacle detection method using single line laser radar and millimeter wave radar |
| CN111025250A (en) * | 2020-01-07 | 2020-04-17 | 湖南大学 | On-line calibration method for vehicle-mounted millimeter wave radar |
| CN111060881A (en) * | 2020-01-10 | 2020-04-24 | 湖南大学 | Millimeter wave radar external parameter online calibration method |
| CN111352112A (en) * | 2020-05-08 | 2020-06-30 | 泉州装备制造研究所 | Target detection method based on vision, laser radar and millimeter wave radar |
-
2020
- 2020-09-10 CN CN202010944090.5A patent/CN112083441B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106405555A (en) * | 2016-09-23 | 2017-02-15 | 百度在线网络技术（北京）有限公司 | Obstacle detecting method and device used for vehicle-mounted radar system |
| CN106908783A (en) * | 2017-02-23 | 2017-06-30 | 苏州大学 | Obstacle detection method based on multi-sensor information fusion |
| CN108226883A (en) * | 2017-11-28 | 2018-06-29 | 深圳市易成自动驾驶技术有限公司 | Test the method, apparatus and computer readable storage medium of millimetre-wave radar performance |
| CN108226906A (en) * | 2017-11-29 | 2018-06-29 | 深圳市易成自动驾驶技术有限公司 | A kind of scaling method, device and computer readable storage medium |
| CN108509972A (en) * | 2018-01-16 | 2018-09-07 | 天津大学 | A kind of barrier feature extracting method based on millimeter wave and laser radar |
| CN110726993A (en) * | 2019-09-06 | 2020-01-24 | 武汉光庭科技有限公司 | Obstacle detection method using single line laser radar and millimeter wave radar |
| CN111025250A (en) * | 2020-01-07 | 2020-04-17 | 湖南大学 | On-line calibration method for vehicle-mounted millimeter wave radar |
| CN111060881A (en) * | 2020-01-10 | 2020-04-24 | 湖南大学 | Millimeter wave radar external parameter online calibration method |
| CN111352112A (en) * | 2020-05-08 | 2020-06-30 | 泉州装备制造研究所 | Target detection method based on vision, laser radar and millimeter wave radar |
Non-Patent Citations (1)
| Title |
|---|
| 毫米波雷达与激光雷达在无人船上的应用;庄加兴等;《船舶工程》;20191125(第11期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112083441A (en) | 2020-12-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112083441B (en) | Obstacle detection method and system for depth fusion of laser radar and millimeter wave radar | |
| WO2022022694A1 (en) | Method and system for sensing automated driving environment | |
| CN109143207B (en) | Laser radar internal reference precision verification method, device, equipment and medium | |
| CN107632308B (en) | Method for detecting contour of obstacle in front of vehicle based on recursive superposition algorithm | |
| CN108509972A (en) | A kind of barrier feature extracting method based on millimeter wave and laser radar | |
| CN110836983B (en) | Method for determining an uncertainty estimate of an estimated velocity | |
| CN111025250B (en) | On-line calibration method for vehicle-mounted millimeter wave radar | |
| CN112513679B (en) | Target identification method and device | |
| CN113391305B (en) | False target suppression method and device for multi-radar fusion and terminal equipment | |
| US20220075074A1 (en) | Obstacle detection device and obstacle detection method | |
| EP3819663A1 (en) | Method for determining a position of a vehicle | |
| CN112949782A (en) | Target detection method, device, equipment and storage medium | |
| CN112462368B (en) | Obstacle detection method and device, vehicle and storage medium | |
| CN113296120B (en) | Obstacle detection method and terminal | |
| CN115018879A (en) | Target detection method, computer-readable storage medium, and driving apparatus | |
| CN114821526A (en) | Obstacle three-dimensional frame detection method based on 4D millimeter wave radar point cloud | |
| CN108693517B (en) | Vehicle positioning method and device and radar | |
| US20230094836A1 (en) | Method for Detecting Moving Objects in the Surroundings of a Vehicle, and Motor Vehicle | |
| CN114296095A (en) | Method, device, vehicle and medium for extracting effective target of automatic driving vehicle | |
| CN111959515B (en) | Forward target selection method, device and system based on visual detection | |
| CN113391270A (en) | False target suppression method and device for multi-radar point cloud fusion and terminal equipment | |
| CN112835029A (en) | Unmanned-vehicle-oriented multi-sensor obstacle detection data fusion method and system | |
| CN114740448A (en) | Target state estimation method and device for vehicle-mounted radar and storage medium | |
| CN110967040B (en) | Method and system for identifying horizontal deviation angle of sensor | |
| CN115792891A (en) | Target track tracking method based on fusion of multi-millimeter-wave radar and laser radar |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |