CN106845412B - Obstacle identification method and device, computer equipment and readable medium - Google Patents
Obstacle identification method and device, computer equipment and readable medium Download PDFInfo
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- CN106845412B CN106845412B CN201710049464.5A CN201710049464A CN106845412B CN 106845412 B CN106845412 B CN 106845412B CN 201710049464 A CN201710049464 A CN 201710049464A CN 106845412 B CN106845412 B CN 106845412B
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
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Abstract
The invention provides an obstacle identification method and device, computer equipment and a readable medium. The method comprises the following steps: dividing the point cloud of the obstacle to be identified into layers with the specified number of layers in height; acquiring the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each layer and the number of points included in the point cloud; generating a hierarchical feature vector of the obstacle to be identified according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each hierarchy and the included point number; and identifying the category of the obstacle to be identified according to the pre-trained classifier model and the hierarchical feature vector of the obstacle to be identified. According to the technical scheme, the point cloud of the obstacle to be recognized is analyzed, so that the layered feature vector of the obstacle to be recognized contains more abundant information of the obstacle to be recognized, the recognition accuracy of the obstacle to be recognized can be effectively improved, and the recognition efficiency of the obstacle to be recognized can be effectively improved.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of automatic driving, in particular to an obstacle identification method and device, computer equipment and a readable medium.
[ background of the invention ]
In the existing automatic driving technology, the information output by the obstacle recognition to be recognized is used as the input of the control and planning information, so that the accurate and fast recognition of the obstacle to be recognized is a very critical technology.
In the prior art, a camera and a laser radar are generally adopted to identify an obstacle to be identified. The camera scheme can be applied to the scene with sufficient illumination and relatively stable environment. However, under the conditions of bad weather and disordered road environment, the vision of the camera scheme is always unstable, so that the acquired information of the obstacle to be identified is inaccurate. While lidar is very expensive, lidar solutions are very stable and safe in identifying obstacles to be identified. In the prior art, when a laser radar is used for identifying an obstacle to be identified, the type of the obstacle to be identified is judged according to the point cloud size and the local features of the obstacle to be identified, which are acquired by scanning the obstacle to be identified by the laser radar. For example, whether the obstacle to be recognized is a person may be determined according to whether the local feature of the point cloud of the obstacle to be recognized is a head portrait of the person; and judging whether the obstacle to be identified is a bicycle or not according to whether the local characteristic of the point cloud of the obstacle to be identified is the bicycle head characteristic or not.
However, in the prior art, local features of a point cloud of an obstacle to be identified in a point cloud scanned by a laser radar are usually not so obvious, so that the obstacle to be identified is poor in identification accuracy and low in identification efficiency.
[ summary of the invention ]
The invention provides an obstacle identification method and device, computer equipment and a readable medium, which are used for improving the identification accuracy and identification efficiency of an obstacle to be identified in automatic driving.
The invention provides an obstacle identification method, which comprises the following steps:
dividing the point cloud of the obstacle to be identified into layers with the specified number of layers in height; the designated number of layers comprises at least two layers;
acquiring the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each layer and the included points;
generating a hierarchical feature vector of the obstacle to be identified according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each hierarchical layer and the number of points included in the point cloud;
and identifying the category of the obstacle to be identified according to a pre-trained classifier model and the hierarchical feature vector of the obstacle to be identified.
Further optionally, in the method as described above, generating a hierarchical feature vector of the obstacle to be identified according to a maximum value, a minimum value, and a number of points included in the point cloud of the obstacle to be identified in each of the hierarchical layers, specifically includes:
and generating a hierarchical feature vector of the obstacle to be identified according to the maximum value, the minimum value and the included points of the point cloud of the obstacle to be identified in each direction in each hierarchy, and by referring to at least one of the coordinate information of the centroid of the point cloud of the obstacle to be identified and the total points included in the point cloud of the obstacle to be identified.
Further optionally, in the method as described above, further comprising:
and determining the position of the obstacle to be recognized relative to the current vehicle according to the coordinate information of the mass center of the point cloud of the obstacle to be recognized.
Further optionally, in the method as described above, generating a hierarchical feature vector of the obstacle to be identified according to a maximum value, a minimum value, and a number of points included in the point cloud of the obstacle to be identified in each of the hierarchical layers, specifically includes:
acquiring length information of the point cloud of the obstacle to be identified in each layer according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in the length direction of each layer;
acquiring width information of the point cloud of the obstacle to be identified in each layer according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in the width direction of each layer;
and generating a hierarchical feature vector of the obstacle to be identified according to the length information, the width information and the number of points included in the point cloud of the obstacle to be identified in each hierarchical layer.
Further optionally, in the method as described above, before the identifying the category of the obstacle to be identified according to a pre-trained classifier model and the hierarchical feature vector of the obstacle to be identified, the method further includes:
collecting point cloud information of a plurality of classes of marked preset obstacles to generate an obstacle training set;
and training the classifier model according to the point cloud information of the preset obstacles in the obstacle training set.
Further optionally, in the method as described above, training the classifier model according to the point cloud information of the plurality of preset obstacles in the obstacle training set specifically includes:
dividing the point cloud of each preset obstacle into the layers of the specified number of layers in height; acquiring the maximum value and the minimum value of the point cloud of each preset obstacle in each direction in each layer and the number of points included in the point cloud;
generating a layering characteristic vector of each preset obstacle according to the maximum value and the minimum value of the point cloud of each preset obstacle in each direction in each layering and the number of points included in the point cloud;
and training a classifier model according to the hierarchical feature vector of each preset obstacle and the category of each preset obstacle, so as to determine the classifier model.
The present invention also provides an obstacle recognition apparatus, the apparatus including:
the system comprises a dividing module, a detecting module and a judging module, wherein the dividing module is used for dividing the point cloud of the obstacle to be identified into layers with the specified number of layers in height; the designated number of layers comprises at least two layers;
the acquisition module is used for acquiring the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each layer and the included points;
the feature vector generation module is used for generating a hierarchical feature vector of the obstacle to be identified according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each hierarchical layer and the included points;
and the identification module is used for identifying the category of the obstacle to be identified according to a pre-trained classifier model and the hierarchical feature vector of the obstacle to be identified.
Further optionally, in the apparatus as described above, the feature vector generation module is specifically configured to generate the hierarchical feature vector of the obstacle to be recognized, according to a maximum value, a minimum value, and a number of points included in each direction of the point cloud of the obstacle to be recognized in each hierarchical layer, and referring to at least one of coordinate information of a centroid of the point cloud of the obstacle to be recognized and a total number of points included in the point cloud of the obstacle to be recognized.
Further optionally, in the apparatus as described above, further comprising:
and the determining module is used for determining the position of the obstacle to be recognized relative to the current vehicle according to the coordinate information of the mass center of the point cloud of the obstacle to be recognized.
Further optionally, in the apparatus as described above, the feature vector generation module is specifically configured to:
acquiring length information of the point cloud of the obstacle to be identified in each layer according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in the length direction of each layer;
acquiring width information of the point cloud of the obstacle to be identified in each layer according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in the width direction of each layer;
and generating a hierarchical feature vector of the obstacle to be identified according to the length information, the width information and the number of points included in the point cloud of the obstacle to be identified in each hierarchical layer.
Further optionally, in the apparatus as described above, further comprising:
the system comprises an acquisition module, a recognition module and a recognition module, wherein the acquisition module is used for acquiring point cloud information of a plurality of preset obstacles marked with obstacle categories to be recognized and generating an obstacle training set;
and the training module is used for training the classifier model according to the point cloud information of the preset obstacles in the obstacle training set.
Further optionally, in the apparatus as described above, the training module is specifically configured to:
dividing the point cloud of each preset obstacle into the layers of the specified number of layers in height; acquiring the maximum value and the minimum value of the point cloud of each preset obstacle in each direction in each layer and the number of points included in the point cloud;
generating a layering characteristic vector of each preset obstacle according to the maximum value and the minimum value of the point cloud of each preset obstacle in each direction in each layering and the number of points included in the point cloud; and training a classifier model according to the hierarchical feature vector of each preset obstacle and the category of each preset obstacle, so as to determine the classifier model.
The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the obstacle identification method as described above when executing the program.
The invention also provides a computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the obstacle identification method as described above.
The obstacle identification method and device, the computer equipment and the readable medium divide the point cloud of the obstacle to be identified into layers with the specified number of layers in height; acquiring the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each layer and the number of points included in the point cloud; generating a hierarchical feature vector of the obstacle to be identified according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each hierarchy and the included point number; and identifying the category of the obstacle to be identified according to the pre-trained classifier model and the hierarchical feature vector of the obstacle to be identified. Compared with the identification method for identifying the obstacle to be identified according to the size and the local characteristics of the point cloud of the obstacle to be identified in the prior art, the technical scheme of the invention analyzes the point cloud of the obstacle to be identified, so that the layered characteristic vector of the obstacle to be identified contains more abundant information of the obstacle to be identified, and identifies the characteristic vector of the obstacle to be identified according to the pre-trained classifier model to determine the category of the obstacle to be identified, so that the identification accuracy of the obstacle to be identified can be effectively improved, and the identification efficiency of the obstacle to be identified can be effectively improved.
[ description of the drawings ]
Fig. 1 is a flowchart of an obstacle identification method according to an embodiment of the present invention.
Fig. 2 is a structural diagram of a first obstacle recognition device according to an embodiment of the present invention.
Fig. 3 is a structural diagram of a second obstacle recognition device according to an embodiment of the present invention.
Fig. 4 is a block diagram of a computer apparatus of the present invention.
[ detailed description ] embodiments
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a flowchart of an obstacle identification method according to an embodiment of the present invention. As shown in fig. 1, the obstacle identification method of this embodiment may specifically include the following steps:
100. dividing the point cloud of the obstacle to be identified into layers with the specified number of layers in height;
the obstacle recognition method of the embodiment is applied to the technical field of automatic driving. In automatic driving, a vehicle is required to be capable of automatically identifying obstacles in a road so as to make a decision and control in time during vehicle driving, and the vehicle can safely drive conveniently. The execution subject of the obstacle recognition method of the embodiment may be an obstacle recognition device, which may be integrated by a plurality of modules, and the obstacle recognition device may be specifically provided in an autonomous vehicle to control the autonomous vehicle.
The point cloud of the obstacle to be identified in this embodiment may be obtained by scanning with a laser radar. The specifications of the laser radar may be 16-wire, 32-wire, 64-wire, etc. Wherein a higher number of lines indicates a higher specific energy density of the lidar. The designated number of layers in this embodiment includes at least two layers, and may be specifically classified according to the height of the point cloud of the obstacle, and the higher the height of the point cloud of the obstacle is, the more the number of layers that may be appropriately classified is, and the lower the height of the point cloud of the obstacle is, the fewer the number of layers that are classified may be relatively. Since the point cloud of the obstacle to be recognized is divided into the layers with the specified number of layers in height in the embodiment, the point cloud of the obstacle is divided in the height direction by adopting a plurality of xy planes parallel to the horizontal plane. Preferably, in this embodiment, the point clouds of the obstacles to be recognized are divided in a uniform manner in height, that is, the heights of the point clouds in the divided layers are equal.
101. Acquiring the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each layer and the number of points included in the point cloud;
each point in the point cloud of the obstacle to be recognized in the present embodiment has coordinates. The origin of the corresponding coordinate system used is the centroid position of the vehicle currently carrying the lidar. Therefore, each point in the point cloud of the obstacle to be recognized, which is obtained through detection, can correspond to one coordinate point of the identification by taking the center of mass position of the vehicle as an origin. According to the coordinates of all points in the point cloud of the obstacle, the maximum value ymax and the minimum value ymin of the point cloud of the obstacle in the length direction, the maximum value xmax and the minimum value xmin in of the width direction and the maximum value zmax and the minimum value zmin of the height direction can be determined, then the length of the obstacle can be taken to be equal to the maximum length minus the minimum length ymax-ymin of the point cloud, the width of the obstacle is equal to the maximum width minus the minimum width xmax-xmin of the point cloud, and the height of the obstacle is higher than the maximum height minus the minimum height zmax-zmin of the point cloud. In the same manner, after the point cloud of the obstacle is layered in the step 100, the maximum length value and the minimum length value in the length direction, and the maximum width value and the minimum width value in the width direction in each layer of the point cloud of the obstacle can be obtained. Meanwhile, the number of points included in each layered point cloud can be acquired.
102. Generating a hierarchical feature vector of the obstacle to be identified according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each hierarchy and the included point number;
in this embodiment, the hierarchical feature vector of the obstacle to be identified may be generated directly according to the maximum value and the minimum value in each direction in each hierarchy and the number of points included. For example, when the obstacle point cloud to be identified includes 10 hierarchies, each hierarchy includes a maximum value in a length direction, a minimum value, a maximum value in a width direction, a minimum value, and the number of points included in the hierarchy point cloud, which are 5 features in total; then 10 tiers may include 50 features in total, and the 50 features may be arranged in a row to collectively form a tiered feature vector for the obstacle to be identified.
In addition, optionally, in this embodiment, the step 102 may further include the following steps:
(a1) acquiring length information of the point cloud of the obstacle to be identified in each layer according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in the length direction of each layer;
specifically, the length of the point cloud of the obstacle to be identified in each layer is equal to the maximum value minus the minimum value of the point cloud of the obstacle to be identified in the length direction of the layer.
(a2) Acquiring width information of the point cloud of the obstacle to be identified in each layer according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in the width direction of each layer;
specifically, the width of the point cloud of the obstacle to be identified in each layer is equal to the maximum value minus the minimum value of the point cloud of the obstacle to be identified in the width direction in the layer.
(a3) And generating a hierarchical feature vector of the obstacle to be identified according to the length information, the width information and the included points of the point cloud of the obstacle to be identified in each hierarchy.
At this time, a hierarchical feature vector of the obstacle to be recognized may also be generated according to the length, width, and number of points included in the point cloud of the obstacle to be recognized in each hierarchy. Similarly, when the obstacle point cloud to be identified includes 10 tiers, each tier includes only 3 features. Then 10 hierarchies may include 30 features in total, and these 30 features may be arranged in a row, which together constitute the hierarchical feature vector of the obstacle to be identified.
Further optionally, step 102 in this embodiment may specifically be: and generating a hierarchical feature vector of the obstacle to be identified according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each hierarchy and the included point number, and by referring to at least one of the coordinate information of the centroid of the point cloud of the obstacle to be identified and the total point number included in the point cloud of the obstacle to be identified.
That is to say, on the basis of the above technical solution, when the hierarchical feature vector of the obstacle to be recognized is generated, at least one of the coordinate information of the centroid of the point cloud of the obstacle to be recognized and the total number of points included in the point cloud of the obstacle to be recognized may be added. In this way, before generating the hierarchical feature vector of the obstacle to be recognized by referring to at least one of the coordinate information of the centroid of the point cloud of the obstacle to be recognized and the total number of points included in the point cloud of the obstacle to be recognized according to the maximum value, the minimum value, and the number of points included in each direction of the point cloud of the obstacle to be recognized in each hierarchy, it is necessary to acquire the coordinate information of the centroid of the point cloud of the obstacle to be recognized and the total number of points included in the point cloud of the obstacle to be recognized. Specifically, when the laser radar detects a point cloud of an obstacle to be recognized, a centroid coordinate of the point cloud of the obstacle to be recognized may be acquired, and the centroid coordinate may include values of a length, a width, and a height of the centroid of the obstacle to be recognized in a current coordinate system. In addition, the total point number included in the point cloud of the obstacle to be recognized is the total point number of the point cloud of the whole obstacle to be recognized before the obstacle to be recognized is layered. The total number of points included in the point clouds of the obstacle to be identified should be equal to the sum of the number of point clouds included in each hierarchy. When the laser radar detects the point cloud of the obstacle to be identified, the total point number included in the point cloud of the obstacle to be identified can be acquired.
For example, when the hierarchical feature vector of the obstacle to be recognized is generated directly according to the maximum value, the minimum value and the included point number in each direction in each hierarchy, the hierarchical feature vector of the obstacle to be recognized may be generated, and in addition to the maximum value, the minimum value and the maximum value in the length direction and the minimum value in the width direction of each hierarchy and the 5 point numbers included in the hierarchical point cloud, the length, the width and the height of the centroid of the obstacle to be recognized may be included, so that when the point cloud of the obstacle to be recognized includes 10 hierarchies in total, the generated hierarchical feature vector of the obstacle to be recognized may include the above 5 × 10+3 — 53 features in total. Or generating a hierarchical feature vector of the obstacle to be recognized, and besides 5 features of each hierarchy, including the total point number included in the point cloud of the obstacle to be recognized. In this way, when the point cloud of the obstacle to be recognized includes 10 hierarchies in total, the generated hierarchical feature vector of the obstacle to be recognized may include 5 × 10+1 ═ 51 features in total. Or, generating a hierarchical feature vector of the obstacle to be recognized, including, in addition to 5 features of each hierarchy, 3 features of the length, width and height of the centroid of the obstacle to be recognized, and the total number of points included in the point cloud of the obstacle to be recognized. In this way, when the point cloud of the obstacle to be recognized includes 10 hierarchies in total, the generated hierarchical feature vector of the obstacle to be recognized may include 54 features in total by 5 × 10+3+1 above.
In addition, when the hierarchical feature vector of the obstacle to be recognized may be generated according to the length, the width, and the included number of points of the point cloud of the obstacle to be recognized in each hierarchical layer through the above steps (a1), (a2), and (a3), the hierarchical feature vector of the obstacle to be recognized may be generated to include 3 features of the length, the width, and the number of points included in the hierarchical point cloud, in addition to the length, the width, and the number of points included in each hierarchical layer, of the centroid of the obstacle to be recognized, so that, when the point cloud of the obstacle to be recognized includes 10 hierarchical layers in total, the generated hierarchical feature vector of the obstacle to be recognized may include 3+ 10+3+ 33 features in total. Or generating a hierarchical feature vector of the obstacle to be recognized, and besides 3 features of each hierarchy, including the total point number included in the point cloud of the obstacle to be recognized. In this way, when the point cloud of the obstacle to be recognized includes 10 hierarchies in total, the generated hierarchical feature vector of the obstacle to be recognized may include 3 × 10+1 ═ 31 features in total. Or, generating a hierarchical feature vector of the obstacle to be recognized, including 3 features of the length, width and height of the centroid of the obstacle to be recognized and the total point number included in the point cloud of the obstacle to be recognized, in addition to 3 features of each hierarchy. In this way, when the point cloud of the obstacle to be recognized includes 10 hierarchies in total, the generated hierarchical feature vector of the obstacle to be recognized may include 3 × 10+3+1 ═ 34 features in total.
In actual use, besides the above manner, the generation of the hierarchical feature vector of the obstacle to be recognized may further expand the number of features included in the hierarchical feature vector of the obstacle to be recognized according to other feature information of the obstacle to be recognized, such as a distance from the current vehicle, and the like, and may also realize the recognition of the obstacle to be recognized.
Further optionally, in this embodiment, after the coordinate information of the centroid of the point cloud of the obstacle to be recognized is acquired, the distance from the obstacle to be recognized to the origin of the coordinate system, that is, the distance from the centroid position of the vehicle, may also be calculated according to the coordinate information of the centroid of the obstacle to be recognized, so that the position of the obstacle to be recognized relative to the current vehicle, that is, the distance from which position the obstacle to be recognized is located in which direction of the current vehicle, may be determined, and the vehicle that is driven by the vehicle may control in time according to the position of the obstacle to be recognized relative to the current vehicle, thereby ensuring the driving safety of the vehicle.
103. And identifying the category of the obstacle to be identified according to the pre-trained classifier model and the hierarchical feature vector of the obstacle to be identified.
In this embodiment, a classifier model is trained in advance, the input of the classifier model may be a hierarchical feature vector of the obstacle, and the output may be a category of the obstacle. In this way, the hierarchical feature vector of the obstacle to be recognized acquired in the above embodiment is input to the classifier model, and the class output by the classifier model is the class of the obstacle to be recognized. Alternatively, in the present embodiment, the category of the obstacle to be recognized may be classified into a pedestrian, a bicycle, a car, or other categories. When an obstacle is identified, the category of the uncertain obstacle is identified as another category. And according to the practical application, the types of the obstacles can be increased step by step according to new vehicles appearing in the road, and the classifier model is updated and trained according to the point cloud information of countless obstacles of the types, so that the updated classifier model can also identify the obstacles of the newly increased types.
During the pre-training of the classifier model, some point cloud information of obstacles with classes already labeled may be used for training, for example, before the step 103, the method may specifically include the following steps:
(b1) collecting point cloud information of a plurality of classes of marked preset obstacles to generate an obstacle training set;
(b2) and training a classifier model according to the point cloud information of a plurality of preset obstacles in the obstacle training set.
In this embodiment, the number of the point cloud information of the preset obstacle included in the obstacle training set may be many, for example, more than 5000 or more than ten thousand or more, and the more the number of the point cloud information of the preset obstacle included in the obstacle training set is, the more accurate the parameters of the determined classifier model are when the classifier model is trained, and the more accurate the classification of the obstacle to be recognized according to the classifier model is subsequently recognized.
For example, the step (b2) may specifically include the following steps:
(c1) dividing the point cloud of each preset obstacle into layers with the specified number of layers in height;
(c2) acquiring the maximum value and the minimum value of the point cloud of each preset obstacle in each direction in each layer and the number of points included in the point cloud;
(c3) generating a layering characteristic vector of each preset obstacle according to the maximum value and the minimum value of the point cloud of each preset obstacle in each direction in each layer and the number of points included in the point cloud;
(c4) and training a classifier model according to the hierarchical characteristic vector of each preset obstacle and the category of each preset obstacle, so as to determine the classifier model.
In this embodiment, the implementation processes of the steps (c1) - (c3) can refer to the implementation process of the steps 100 and 102. That is, when the classifier model is trained, the generation method of the hierarchical feature vector of each preset obstacle is the same as the generation method of the hierarchical feature vector of the obstacle to be recognized generated in the process of recognizing the obstacle category to be recognized by using the classifier model; the number of the features contained in the obtained hierarchical feature vector of the preset obstacle is the same as the number of the features contained in the hierarchical feature vector of the obstacle to be recognized. In practical application, the more the number of the features contained in the hierarchical feature vector of the preset barrier is, the more the corresponding features of the preset barrier are rich, the more accurate the obtained classifier model is, and when the barrier to be recognized is recognized, the acquired hierarchical feature vector of the barrier to be recognized also needs to include the same number of features, so that the features of the barrier to be recognized are also rich, and the recognition of the barrier to be recognized by using the classifier model is also accurate. It should be noted that, when the obstacle to be recognized is recognized, the number of designated layers of the point cloud of the obstacle to be recognized divided in height is the same as the number of designated layers of the point cloud of each preset obstacle divided in height when the classifier model is trained. When the obstacle to be recognized is recognized, the specification of the laser radar adopted for obtaining the point cloud of the obstacle to be recognized is required to be the same as that of the laser radar adopted for obtaining the point cloud of each preset obstacle during training, otherwise, the number of points included in the point cloud is not in one level, and the obstacle to be recognized cannot be recognized accurately.
And finally, training the classifier model according to the layered feature vectors of the preset obstacles and the classes of the preset obstacles, so that the classifier model can output the classes of the preset obstacles when inputting the layered feature vectors of the preset obstacles. During training, because the type of the preset obstacle is known, if the hierarchical feature vector of the preset obstacle is input, the output type of the preset obstacle does not conform to the type of the preset obstacle known in advance, and parameters of the classifier model can be adjusted, so that the type of the preset obstacle output by the classifier model conforms to the type of the preset obstacle known in advance. By training the classifier model using the point cloud information of the countless preset obstacles in the obstacle training set through the above steps (c1) - (c4), the parameters of the classifier model can be determined, thereby determining the classifier model. At this time, if the hierarchical feature vector of the obstacle to be recognized is input into the determined classifier model, the classifier model can accurately input the category of the obstacle to be recognized.
The classifier model of this embodiment may be any one of a random forest model, a decision tree model, a logistic regression model, a Support Vector Machine (SVM) model, and a neural network model, which is not limited herein.
By adopting the obstacle identification method of the embodiment, after the automatic driving vehicle scans the point cloud of the obstacle to be identified through the laser radar, the obstacle to be identified can be identified according to the obstacle identification method, and the driving of the vehicle can be further controlled according to the type of the obstacle, for example, the vehicle is controlled to avoid the obstacle, so that the driving safety of the automatic driving vehicle is effectively improved.
In the obstacle identification method of the embodiment, the point cloud of the obstacle to be identified is divided into layers with the specified number of layers in height; acquiring the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each layer and the number of points included in the point cloud; generating a hierarchical feature vector of the obstacle to be identified according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each hierarchy and the included point number; and identifying the category of the obstacle to be identified according to the pre-trained classifier model and the hierarchical feature vector of the obstacle to be identified. Compared with the identification method for identifying the obstacle to be identified according to the size and the local characteristics of the point cloud of the obstacle to be identified in the prior art, according to the technical scheme of the embodiment, the point cloud of the obstacle to be identified is analyzed, so that the layered characteristic vector of the obstacle to be identified contains richer information of the obstacle to be identified, the characteristic vector of the obstacle to be identified is identified according to the pre-trained classifier model, the category of the obstacle to be identified is determined, the identification accuracy of the obstacle to be identified can be effectively improved, and the identification efficiency of the obstacle to be identified can be effectively improved.
Fig. 2 is a structural diagram of a first obstacle recognition device according to an embodiment of the present invention. As shown in fig. 2, the obstacle identification device of the present embodiment may specifically include: the device comprises a dividing module 10, an obtaining module 11, a feature vector generating module 12 and a recognition module 13.
The dividing module 10 is used for dividing the point cloud of the obstacle to be identified into layers with a specified number of layers in height; the specified number of layers comprises at least two layers; the acquisition module 11 is configured to acquire the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each layer and the number of points included in the point cloud after being divided by the division module 10; the feature vector generation module 12 is configured to generate a hierarchical feature vector of the obstacle to be identified according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each hierarchical layer, which are acquired by the acquisition module 11, and the number of points included in the point cloud; the recognition module 13 is configured to recognize the category of the obstacle to be recognized according to the pre-trained classifier model and the hierarchical feature vector of the obstacle to be recognized generated by the feature vector generation module 12.
The obstacle identification device of this embodiment identifies the obstacle to be identified by using the module, and the implementation principle and the technical effect of the related method embodiment are the same, so that reference may be made to the description of the related method embodiment in detail, and details are not repeated here.
Fig. 3 is a structural diagram of a second obstacle recognition device according to an embodiment of the present invention. As shown in fig. 3, the obstacle recognition device of the present embodiment further describes the technical solution of the present invention in more detail on the basis of the technical solution of the embodiment shown in fig. 2.
In the obstacle recognition apparatus of this embodiment, the feature vector generation module 12 is specifically configured to generate the hierarchical feature vector of the obstacle to be recognized, according to the maximum value, the minimum value, and the included point number of the point cloud of the obstacle to be recognized in each hierarchical layer, which are acquired by the acquisition module 11, and by referring to at least one of the coordinate information of the centroid of the point cloud of the obstacle to be recognized and the total point number included in the point cloud of the obstacle to be recognized.
Further optionally, the obstacle recognition device of this embodiment further includes: a module 14 is determined.
The acquisition module 11 is further configured to acquire coordinate information of a centroid of a point cloud of an obstacle to be identified and a total number of points included in the point cloud of the obstacle to be identified;
correspondingly, the feature vector generation module 12 is specifically configured to generate a hierarchical feature vector of the obstacle to be identified, according to the maximum value, the minimum value, and the included point number of the point cloud of the obstacle to be identified in each hierarchical layer, which are acquired by the acquisition module 11, and by referring to at least one of the coordinate information of the centroid of the point cloud of the obstacle to be identified, which is acquired by the acquisition module 11, and the total point number included in the point cloud of the obstacle to be identified.
The determining module 14 is configured to determine a position of the obstacle to be recognized relative to the current vehicle according to the coordinate information of the centroid of the point cloud of the obstacle to be recognized, which is acquired by the acquiring module 11.
Further optionally, the feature vector generation module 12 in the obstacle identification device of this embodiment is specifically configured to:
acquiring length information of the point cloud of the obstacle to be identified in each layer according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in the length direction of each layer;
acquiring width information of the point cloud of the obstacle to be identified in each layer according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in the width direction of each layer;
and generating a hierarchical feature vector of the obstacle to be identified according to the length information, the width information and the included points of the point cloud of the obstacle to be identified in each hierarchy.
Further optionally, in the obstacle recognition device of this embodiment, the method further includes:
the acquisition module 15 is configured to acquire point cloud information of a plurality of preset obstacles labeled with obstacle categories to be identified, and generate an obstacle training set;
the training module 16 is configured to train a classifier model according to the point cloud information of a plurality of preset obstacles in the obstacle training set acquired by the acquisition module 15.
Correspondingly, the recognition module 13 is configured to recognize the category of the obstacle to be recognized according to the classifier model trained in advance by the training module 16 and the hierarchical feature vector of the obstacle to be recognized generated by the feature vector generation module 12.
Further optionally, in the obstacle recognition device of this embodiment, the training module 16 is specifically configured to:
dividing the point cloud of each preset obstacle into layers with the specified number of layers in height; acquiring the maximum value and the minimum value of the point cloud of each preset obstacle in each direction in each layer and the number of points included in the point cloud;
generating a layering characteristic vector of each preset obstacle according to the maximum value and the minimum value of the point cloud of each preset obstacle in each direction in each layer and the number of points included in the point cloud; and training a classifier model according to the hierarchical characteristic vector of each preset obstacle and the category of each preset obstacle, so as to determine the classifier model.
The obstacle identification device of this embodiment identifies the obstacle to be identified by using the module, and the implementation principle and the technical effect of the related method embodiment are the same, so that reference may be made to the description of the related method embodiment in detail, and details are not repeated here.
The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the obstacle identification method as shown in the above embodiments.
For example, fig. 4 is a block diagram of a computer device provided in the present invention. FIG. 4 illustrates a block diagram of an exemplary computer device 12a suitable for use in implementing embodiments of the present invention. The computer device 12a shown in FIG. 4 is only an example and should not bring any limitations to the functionality or scope of use of embodiments of the present invention.
As shown in FIG. 4, computer device 12a is in the form of a general purpose computing device. The components of computer device 12a may include, but are not limited to: one or more processors or processors 16a, a system memory 28a, and a bus 18a that connects the various system components (including the system memory 28a and the processors 16 a).
Bus 18a represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
The system memory 28a may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30a and/or cache memory 32 a. Computer device 12a may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34a may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, and commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18a by one or more data media interfaces. Memory 28a may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of the various embodiments of the invention described above in fig. 1-3.
A program/utility 40a having a set (at least one) of program modules 42a may be stored, for example, in memory 28a, such program modules 42a including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may include an implementation of a network environment. Program modules 42a generally perform the functions and/or methodologies described above in connection with the various embodiments of fig. 1-3 of the present invention.
The processor 16a executes various functional applications and data processing by executing programs stored in the system memory 28a, for example, to implement the obstacle recognition method shown in the above-described embodiment.
The present invention also provides a computer-readable medium on which a computer program is stored, which when executed by a processor implements the obstacle identifying method as shown in the above embodiments.
The computer-readable media of this embodiment may include RAM30a, and/or cache memory 32a, and/or storage system 34a in system memory 28a in the embodiment illustrated in fig. 4 described above.
With the development of technology, the propagation path of computer programs is no longer limited to tangible media, and the computer programs can be directly downloaded from a network or acquired by other methods. Accordingly, the computer-readable medium in the present embodiment may include not only tangible media but also intangible media.
The computer-readable medium of the present embodiments may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including AN object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (14)
1. An obstacle identification method, characterized in that the method comprises:
dividing the point cloud of the obstacle to be identified into layers with the specified number of layers in height; the designated number of layers comprises at least two layers;
acquiring the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each layer and the included points;
generating a hierarchical feature vector of the obstacle to be identified according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each hierarchical layer and the number of points included in the point cloud;
and identifying the category of the obstacle to be identified according to a pre-trained classifier model and the hierarchical feature vector of the obstacle to be identified.
2. The method according to claim 1, wherein generating a hierarchical feature vector of the obstacle to be identified according to a maximum value, a minimum value and a number of points included in the point cloud of the obstacle to be identified in each of the hierarchical layers comprises:
and generating a hierarchical feature vector of the obstacle to be identified according to the maximum value, the minimum value and the included points of the point cloud of the obstacle to be identified in each direction in each hierarchy, and by referring to at least one of the coordinate information of the centroid of the point cloud of the obstacle to be identified and the total points included in the point cloud of the obstacle to be identified.
3. The method of claim 2, further comprising:
and determining the position of the obstacle to be recognized relative to the current vehicle according to the coordinate information of the mass center of the point cloud of the obstacle to be recognized.
4. The method according to claim 1, wherein generating a hierarchical feature vector of the obstacle to be identified according to a maximum value, a minimum value and a number of points included in the point cloud of the obstacle to be identified in each of the hierarchical layers comprises:
acquiring length information of the point cloud of the obstacle to be identified in each layer according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in the length direction of each layer;
acquiring width information of the point cloud of the obstacle to be identified in each layer according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in the width direction of each layer;
and generating a hierarchical feature vector of the obstacle to be identified according to the length information, the width information and the number of points included in the point cloud of the obstacle to be identified in each hierarchical layer.
5. The method according to any one of claims 1-4, wherein before identifying the category of the obstacle to be identified based on a pre-trained classifier model and the hierarchical feature vector of the obstacle to be identified, the method further comprises:
collecting point cloud information of a plurality of classes of marked preset obstacles to generate an obstacle training set;
and training the classifier model according to the point cloud information of the preset obstacles in the obstacle training set.
6. The method according to claim 5, wherein training the classifier model according to the point cloud information of the plurality of preset obstacles in the obstacle training set specifically comprises:
dividing the point cloud of each preset obstacle into the layers of the specified number of layers in height; acquiring the maximum value and the minimum value of the point cloud of each preset obstacle in each direction in each layer and the number of points included in the point cloud;
generating a layering characteristic vector of each preset obstacle according to the maximum value and the minimum value of the point cloud of each preset obstacle in each direction in each layering and the number of points included in the point cloud;
and training a classifier model according to the hierarchical feature vector of each preset obstacle and the category of each preset obstacle, so as to determine the classifier model.
7. An obstacle recognition apparatus, characterized in that the apparatus comprises:
the system comprises a dividing module, a detecting module and a judging module, wherein the dividing module is used for dividing the point cloud of the obstacle to be identified into layers with the specified number of layers in height; the designated number of layers comprises at least two layers;
the acquisition module is used for acquiring the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each layer and the included points;
the feature vector generation module is used for generating a hierarchical feature vector of the obstacle to be identified according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in each direction in each hierarchical layer and the included points;
and the identification module is used for identifying the category of the obstacle to be identified according to a pre-trained classifier model and the hierarchical feature vector of the obstacle to be identified.
8. The apparatus according to claim 7, wherein the feature vector generation module is specifically configured to generate the hierarchical feature vector of the obstacle to be identified according to a maximum value, a minimum value, and a number of points included in each direction of the point cloud of the obstacle to be identified in each hierarchical layer, and with reference to at least one of coordinate information of a centroid of the point cloud of the obstacle to be identified and a total number of points included in the point cloud of the obstacle to be identified.
9. The apparatus of claim 8, further comprising:
and the determining module is used for determining the position of the obstacle to be recognized relative to the current vehicle according to the coordinate information of the mass center of the point cloud of the obstacle to be recognized.
10. The apparatus of claim 7, wherein the feature vector generation module is specifically configured to:
acquiring length information of the point cloud of the obstacle to be identified in each layer according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in the length direction of each layer;
acquiring width information of the point cloud of the obstacle to be identified in each layer according to the maximum value and the minimum value of the point cloud of the obstacle to be identified in the width direction of each layer;
and generating a hierarchical feature vector of the obstacle to be identified according to the length information, the width information and the number of points included in the point cloud of the obstacle to be identified in each hierarchical layer.
11. The apparatus of any of claims 7-10, further comprising:
the system comprises an acquisition module, a recognition module and a recognition module, wherein the acquisition module is used for acquiring point cloud information of a plurality of preset obstacles marked with obstacle categories to be recognized and generating an obstacle training set;
and the training module is used for training the classifier model according to the point cloud information of the preset obstacles in the obstacle training set.
12. The apparatus of claim 11, wherein the training module is specifically configured to:
dividing the point cloud of each preset obstacle into the layers of the specified number of layers in height; acquiring the maximum value and the minimum value of the point cloud of each preset obstacle in each direction in each layer and the number of points included in the point cloud;
generating a layering characteristic vector of each preset obstacle according to the maximum value and the minimum value of the point cloud of each preset obstacle in each direction in each layering and the number of points included in the point cloud; and training a classifier model according to the hierarchical feature vector of each preset obstacle and the category of each preset obstacle, so as to determine the classifier model.
13. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1-6 when executing the program.
14. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.
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| CN109901567B (en) * | 2017-12-08 | 2020-06-23 | 百度在线网络技术（北京）有限公司 | Method and apparatus for outputting obstacle information |
| CN108501954A (en) * | 2018-04-03 | 2018-09-07 | 北京瑞特森传感科技有限公司 | A kind of gesture identification method, device, automobile and storage medium |
| CN109215135B (en) * | 2018-09-07 | 2019-11-08 | 百度在线网络技术（北京）有限公司 | A kind of Obstacle Position emulation mode, device and terminal based on statistics |
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