CN113804183A - Real-time topographic surveying and mapping method and system - Google Patents
Real-time topographic surveying and mapping method and system Download PDFInfo
- Publication number
- CN113804183A CN113804183A CN202111095595.XA CN202111095595A CN113804183A CN 113804183 A CN113804183 A CN 113804183A CN 202111095595 A CN202111095595 A CN 202111095595A CN 113804183 A CN113804183 A CN 113804183A
- Authority
- CN
- China
- Prior art keywords
- map
- point cloud
- tsdf
- real
- node
- 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.)
- Granted
Links
- 238000013507 mapping Methods 0.000 title claims abstract description 48
- 238000000034 method Methods 0.000 title claims abstract description 47
- 239000010410 layer Substances 0.000 claims description 64
- 230000010354 integration Effects 0.000 claims description 14
- 239000011159 matrix material Substances 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 8
- 239000002344 surface layer Substances 0.000 claims description 8
- 238000009499 grossing Methods 0.000 claims description 7
- 230000000007 visual effect Effects 0.000 claims description 7
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical group C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 claims description 6
- 238000005315 distribution function Methods 0.000 claims description 3
- 238000009792 diffusion process Methods 0.000 claims description 2
- 230000036962 time dependent Effects 0.000 claims description 2
- 238000012876 topography Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 9
- 238000001514 detection method Methods 0.000 description 8
- 238000005259 measurement Methods 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000012805 post-processing Methods 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 239000004576 sand Substances 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000007429 general method Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3804—Creation or updating of map data
- G01C21/3807—Creation or updating of map data characterised by the type of data
- G01C21/3826—Terrain data
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3804—Creation or updating of map data
- G01C21/3833—Creation or updating of map data characterised by the source of data
- G01C21/3841—Data obtained from two or more sources, e.g. probe vehicles
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3863—Structures of map data
- G01C21/387—Organisation of map data, e.g. version management or database structures
- G01C21/3878—Hierarchical structures, e.g. layering
Abstract
The invention provides a real-time topographic mapping method, which comprises the following steps: acquiring a point cloud key frame and position and attitude information corresponding to the point cloud key frame in real time; updating a map layer of the TSDF map in an incremental manner by using the point cloud key frame and the corresponding position and posture information; and expanding the map layer of the updated TSDF map by a preset thickness to generate an elevation map containing a safety boundary. The invention also provides a real-time topographic mapping system. The method and the system provided by the invention can guide obstacle avoidance in real time and have small topographic data volume.
Description
Technical Field
The invention relates to the field of automatic driving, in particular to a real-time topographic mapping method and a real-time topographic mapping system.
Background
Mapping refers to the activities of measuring, collecting, representing, processing and providing acquired data, information and results of natural geographic factors or the shape, size, spatial position and attributes of surface artificial facilities. The real-time mapping refers to a process of receiving mapping information in real time and dynamically constructing a geographic information map by means of a real-time image transmission system.
The flying automobile is one of the development directions of future vehicles, and the automatic driving function of the intelligent flying automobile plays an important role. When the automatic driving function works, the terrain information in a certain distance range of the current position needs to be obtained in advance so as to avoid risks in advance, and meanwhile, the terrain data is also the prepositive information of flight trajectory planning. However, the requirement of the aerial flight on the terrain map is that the terrain area is the same, and the terrain area is represented by a smaller data volume under the condition of ensuring the safety, so that the aircraft can acquire the terrain information at high speed.
In some technologies, sensors such as laser radar, IMU, GPS and electronic compass and a processor module are included in the airplane onboard equipment, but the processor module only has the function of transmitting acquired data to a ground station system through wireless transmission for post-processing and displaying of the data. Most of the technologies firstly carry out real-time data acquisition, real-time transmission or storage and then carry out off-line post-processing, so that the aims of real-time mapping and guiding the aircraft to avoid obstacles cannot be achieved.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a real-time mapping method capable of guiding an aircraft to avoid obstacles in real time.
The invention firstly provides a real-time topographic mapping method, which comprises the following steps: acquiring a point cloud key frame and position and attitude information corresponding to the point cloud key frame in real time; updating a map layer of the TSDF map in an incremental manner by using the point cloud key frame and the corresponding position and posture information; and expanding the map layer of the updated TSDF map by a preset thickness to generate an elevation map containing a safety boundary.
Further, the incrementally updating a map layer of a TSDF map using the point cloud keyframes and the corresponding position and pose information includes: and fusing a plurality of layers of point cloud key frames which are scanned at different moments in the same view field area and corresponding position and attitude information into a single surface layer in an incremental mode to generate an updated TSDF map layer.
Further, expanding the map layer of the updated TSDF map by a preset thickness to generate an elevation map including a safety boundary, includes: and expanding a preset distance outwards along the surface normal direction to form a safety area with a preset thickness for the map layer of the updated TSDF map, and generating an elevation map containing a safety boundary.
Further, the acquiring the point cloud key frame after motion compensation and the position and posture information corresponding to the point cloud key frame specifically includes: acquiring point cloud information of a current working scene in a visual field range, corresponding high-frequency space pose information and corresponding low-frequency global space position information through a sensor; performing information pre-integration on the high-frequency space pose information; interpolating and calculating a pose matrix of each point relative to a first point in the point cloud frame through an information pre-integration result; correcting the point cloud frame according to the pose matrix and the low-frequency global spatial position information to obtain a corrected point cloud frame; and when the variation of the position or the angle of the carrier is larger than a preset threshold value, selecting the corrected point cloud frame corresponding to the moment as a point cloud key frame.
Further, fusing the point cloud key frame to represent a plurality of layers of point cloud key frames scanned at different moments in the same view area and corresponding position and posture information into a single surface layer in an incremental manner, and generating an updated TSDF map layer includes: converting the acquired original point cloud data expressed under the point cloud coordinate system into point cloud data under a TSDF (time-resolved diffusion) terrain global coordinate system; under a TSDF (time-dependent dynamic distribution function) terrain global coordinate system, calculating node center coordinates and truncation distances corresponding to each point in the point cloud key frame data; and updating the TSDF map layer data by using the calculated node center coordinate and the truncated distance corresponding to each point. Further, the step of extending the preset distance outward along the surface normal direction to add a safety region with a predetermined thickness to the map layer of the updated TSDF map, and the step of generating the elevation map including the safety boundary includes: in the vertical direction, the map layer is expanded upwards along the height of the terrain to enable the map layer to be expanded outwards along the surface normal direction by a preset distance; and in the horizontal direction, the map layer is expanded outwards along the horizontal direction so as to expand the map layer outwards along the surface normal direction by a preset distance. .
Further, the method further comprises: establishing an elevation node corresponding to an elevation map according to the updated node in the TSDF map; wherein, in the vertical direction, upwards expand the map layer along the topography height and make the map layer expand outward along surface normal direction and predetermine the distance and include: updating the truncation distance of the corresponding elevation node according to the updated truncation distance of the node in the TSDF map and a preset distance; wherein, on the horizontal direction, outwards expand map layer along the horizontal direction and make map layer expand outward along surface normal direction and predetermine the distance, include: searching elevation topographic map nodes of the elevation nodes in a preset neighborhood radius range to serve as neighborhood nodes; and updating the truncation distance of the neighborhood nodes according to the truncation distance of the neighborhood nodes and the truncation distance of the current elevation node.
Further, any of the above real-time topographic methods further comprises: and carrying out simplified smoothing processing on the boundary of the elevation map of the safety boundary, and compressing the data volume of the updated elevation map of the safety boundary.
Further, the simplified smoothing is performed on the boundary of the elevation map of the safety boundary, and compressing the data volume of the updated elevation map of the safety boundary includes: calculating the absolute height value of the current node by using the absolute height value of the current node before updating, the preset smooth height and the absolute height value of the anchor point at the bottom of the current node; and updating the absolute height value of the current node in the elevation map data.
The invention also provides a real-time topographic mapping system, which is applied to an aircraft and comprises a flight control unit and a topographic real-time mapping module, wherein the topographic real-time mapping module generates an updated safety boundary topographic map, and the topographic real-time mapping module executes any one of the above real-time topographic mapping methods.
According to the invention, the real-time acquired data is processed, a safety region with a certain thickness is added on the basis of the updated map, and a topographic map generated by real-time generated topographic data can be used for flight control obstacle avoidance.
Drawings
FIG. 1 is a schematic diagram of a real-time topographic mapping method in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a real-time topographic mapping method in accordance with another embodiment of the present invention;
FIG. 3 is a schematic diagram of a method of real-time topographic mapping in accordance with yet another embodiment of the present invention;
FIG. 4 is a schematic diagram of a real-time topographic mapping method in accordance with yet another embodiment of the present invention
FIG. 5 is a schematic diagram of a method of real-time topographic mapping in accordance with yet another embodiment of the present invention;
FIG. 6 is a schematic diagram of a method of real-time topographic mapping in accordance with another embodiment of the present invention;
FIG. 7 illustrates in two dimensions the principle of generation of a safety margin topographic map in accordance with an embodiment of the present invention;
FIG. 8 is a flowchart illustrating a process for generating a safety margin map, in accordance with one embodiment of the present invention;
FIG. 9 is a block diagram of a real-time topographical mapping system in accordance with one embodiment of the present invention;
fig. 10 is a detailed schematic diagram of a real-time terrain mapping system in accordance with another embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Referring to fig. 1, a real-time topographic mapping method according to a preferred embodiment of the present invention includes the steps of: sc, acquiring a point cloud key frame and position and attitude information corresponding to the point cloud key frame in real time; se, updating a map layer of a TSDF (truncated signed distance function) map in an incremental manner by using the point cloud key frame and the corresponding position and posture information; and Sg, expanding the map layer of the updated TSDF map by a preset thickness to generate an elevation map containing a safety boundary.
According to the invention, the data acquired in real time are processed, the safety region with a certain thickness is added on the basis of the updated map, the map layer of the updated TSDF map is expanded by the preset thickness to generate the elevation map containing the safety boundary, and the terrain data generated in real time can be used for generating the terrain map for flight control obstacle avoidance.
Please refer to fig. 2. A step of acquiring a point cloud key frame and position and attitude information corresponding to the point cloud key frame in real time, namely the step Sc comprises the following steps:
sc 1: the method comprises the steps of obtaining point cloud information of a current working scene in a visual field range And corresponding high-frequency space pose information And low-frequency Global space position information through a sensor, specifically, providing the high-frequency space pose information through an IMU (inertial measurement unit), providing the point cloud information of the working scene in the visual field range through a LIDAR (Light Detection And Ranging), And providing the low-frequency Global space position information through a GNSS (Global Navigation Satellite System) to perform accumulated error compensation on the high-frequency space pose information provided by the IMU.
Sc 3: and performing information pre-integration on the high-frequency space pose information. In the information pre-integration, IMU information pre-integration is performed by calculating relative motion between point cloud frames of the laser radar.
Sc 5: assuming that the point cloud frame is acquired by the IMU at the uniform linear velocity and the uniform angular velocity, performing interpolation calculation through the information pre-integration result in the step Sc3, and calculating a pose matrix of each point relative to the initial point in the point cloud frame for correcting the point cloud frame.
Sc 7: and correcting the point cloud frame according to the pose matrix and the low-frequency global spatial position information to obtain a corrected point cloud frame. Points in one frame of point cloud are not acquired at the same time, in the acquisition process, the laser radar moves along with the carrier, so that the coordinate systems of different laser points are changed all the time, and finally formed point cloud frame data cannot accurately reflect the geometric shape of an object in a visual field, namely distortion is generated; in order to simplify the operation, a carrier is assumed to collect a frame of point cloud under the uniform linear velocity and the uniform angular velocity, and a pose matrix of each point relative to a first point in the frame is calculated by interpolating a pre-integration result of IMU information and is used for correcting the point cloud frame.
Sc 9: when the variation of the position or the angle of the carrier is larger than a preset threshold, selecting a corrected point cloud frame corresponding to the moment as a point cloud key frame, namely selecting a point cloud frame corresponding to the moment and composed of the point cloud data as the point cloud key frame, wherein due to the fact that the data volume of the point cloud is large, on the premise that topographic mapping needs are met, the excessive data volume is not beneficial to processing instantaneity, and therefore key frame detection and extraction are needed; in the invention, the detection of the key frame only depends on the variation of the position and the angle of the carrier, and when any variation is larger than a preset threshold value, the corrected point cloud frame corresponding to the moment is selected as the key frame.
In a preferred embodiment, if a binocular camera or other sensor capable of indirectly acquiring point cloud data is used instead of the LIDAR, the step of motion compensation of the point cloud frame may be omitted, and referring to fig. 3, at this time, the step of acquiring the point cloud key frame and the position and orientation information corresponding to the point cloud key frame in real time, that is, step Sc includes the steps of: sc 2: providing high-frequency space pose information through the IMU, and acquiring point cloud data through a binocular camera; sc 4: performing information pre-integration on the high-frequency space pose information; sc 6: and when the variation of the position or the angle of the carrier is larger than a preset threshold value, selecting a point cloud frame formed by the point cloud data corresponding to the moment as a point cloud key frame.
Referring to fig. 4, the step of updating the map layer of the TSDF map in an incremental manner using the point cloud key frame and the corresponding position and posture information, i.e., step Se, includes the steps of: se2, fusing the point cloud key frames into a single surface layer in an incremental manner to generate an updated map layer, wherein the point cloud key frames represent multiple layers of point cloud key frames scanned at different moments in the same view field area and the position and posture information corresponding to the point cloud key frames. More specifically, step Se2 includes the steps of: se22, converting the acquired original point cloud data expressed under the point cloud coordinate system into point cloud data under a TSDF terrain global coordinate system, specifically, converting the point cloud data expressed under the point cloud coordinate system into the TSDF terrain global coordinate system, so that the data acquired from the laser radar sensors at different positions at different times are unified under the same coordinate system; se24, calculating node center coordinates and truncation distances corresponding to each point in the point cloud key frame data under a TSDF (time delay distribution function) terrain global coordinate system; specifically, a node index, namely a node center coordinate, corresponding to each point in the point cloud frame data is calculated so as to facilitate retrieval and creation; calculating the mean value of the node distances belonging to the midpoint of the same node index, and establishing or updating the value of the truncation distance of the corresponding node according to the value; se26, updating the map layer data of the TSDF map by using the calculated node center coordinates and cutoff distances corresponding to each point, and determining an update iteration mode of the range and cutoff distances of the nodes to be updated, wherein the main objective of the step is to incrementally fuse the multi-layer point cloud data scanned at different times in the same region into a single surface layer; when the TSDF method is adopted, the TSDF map layer is composed of a series of TSDF nodes of a cube with a determined side length, and the side length is the resolution of the TSDF map, so that the data volume of the TSDF map is only in direct proportion to the area of the map to be represented, and is not related to the decoupling of the coverage times of the scanning surface.
More specifically, referring to fig. 5, when the TSDF method is adopted, the step of fusing the point cloud key frame representing multiple layers of point cloud key frames scanned at different times in the same view area and corresponding position and posture information into a single surface layer in an incremental manner to generate an updated TSDF map layer includes: se221, according to the input k key frame point cloud
And its corresponding global pose
Converting point cloud from sensor coordinate system to TSDF global coordinate system
The origin of the frame point cloud is recorded and converted into a TSDF global coordinate system
Point cloud data expressed in a point cloud coordinate system is converted into a TSDF global coordinate system, so that data acquired from a laser radar sensor at different positions at different moments are unified to the same TSDF seatUnder the mark system; se241, respectively calculating the node center coordinates corresponding to each point in the key frame, and each point pi(x, y, z) corresponds to a TSDF node index of
The center coordinates of the nodes are
Wherein
Expressing rounding-down, and calculating TSDF node indexes corresponding to each point in the point cloud frame data so as to facilitate retrieval and creation; and calculating the average value p of all points belonging to the same TSDF node in the key frame point cloudmNode index
And center coordinates
Check the index as
If not, a new TSDF node is created, and the distance of the node is calculated as
wi+1＝1，
Wi+1＝Wi+wi+1......(3)。
referring to fig. 6, 7 and 8, the step of expanding the safety area with the preset distance along the surface normal direction to add a predetermined thickness to the map layer of the updated TSDF map and generate the elevation map including the safety boundary, i.e. the step Sg, includes the steps of:
sg4, updating the corresponding topographic map node for each TSDF node in polling processing, and processing the height value of the topographic map node according to the following formula (4). This step applies a security boundary parameter dsIn the vertical direction, the terrain height is enlarged upwards by dsA safe distance of length;
for each node, in the vertical direction, the map layer is expanded upwards along the height of the terrain to enable the map layer to be expanded outwards along the surface normal direction by a preset distance;
specifically, the step Sg4 includes a step Sg45, where the node surface safety distance calculation is performed, that is, the cutoff distance of the corresponding elevation node is updated according to the updated cutoff distance of the node in the TSDF map and the preset distance: determining whether an index exists in the safety boundary terrain map as
The topographic map node of (a): if not, then a node is created
And distance it from
Is initialized to
Wherein
Is a node
An internal cutoff distance; if yes, updating the truncation distance inside the nodes of the topographic map as follows:
wherein the content of the first and second substances,
is a node of a topographic map
The distance value before update of dsIs the flaring distance;
sg6, enlarging the safety boundary of the terrain in the horizontal direction by dsA distance; and for each node, in the horizontal direction, outwards expanding the map layer along the horizontal direction to enable the map layer to outwards expand a preset distance along the surface normal direction.
Specifically, unlike Sg45 in which the truncated distance of the corresponding elevation node is updated according to the truncated distance of the updated node in the TSDF map and the preset distance, step Sg6 includes step Sg67 and step Sg 69. Step Sg 67: and searching the elevation topographic map node of the elevation node in a preset neighborhood radius range to serve as a neighborhood node. Step Sg 69: updating the truncation distance of the neighborhood node according to the truncation distance of the neighborhood node and the truncation distance of the current elevation node, namely updating the elevation node
Neighborhood radius dsThe truncation distance of other nodes within range.
In the step Sg69, updating the elevation nodes
Neighborhood radius dsAnd when the truncation distance of other nodes in the range is the truncation distance of the current elevation node and the truncation distances of other nodes in the neighborhood of the node, generating a new distance value through a preset rule for assignment.For example, the truncated distance for the current elevation node may be compared to the truncated distances for other nodes in the node's neighborhood, and the larger or largest value may be selected as the new distance value for the current node.
Through the steps of Sg45, Sg67 and Sg69, the elevation nodes corresponding to the elevation map can be created according to the updated nodes in the TSDF map. In other words, an elevation node corresponding to an elevation map is created according to the updated node in the TSDF map, in the vertical direction, the map layer is expanded upwards along the terrain height to expand the map layer outwards along the surface normal direction by a preset distance, and the truncation distance of the corresponding elevation node is updated according to the truncation distance and the preset distance of the updated node in the TSDF map; in the horizontal direction, the map layer is expanded outwards along the horizontal direction to enable the map layer to expand outwards along the surface normal direction by a preset distance, firstly, elevation map nodes of the elevation nodes within a preset neighborhood radius range are searched to serve as neighborhood nodes, and then the cutoff distances of the neighborhood nodes are updated according to the cutoff distances of the neighborhood nodes and the cutoff distance of the current elevation node.
Before the step of expanding the map layer upwards along the height of the terrain in the vertical direction to expand the map layer by the preset distance in the direction of the surface normal line for each node, that is, step Sg4, step Sg2 may be further included, and a storage space is allocated to each node for the updated map layer according to the sequence of node updating. Specifically, step Sg2 includes the steps of: step Sg21, determining that a TSDF integrator adds TSDF nodes with changed internal values to a hash value queue according to the time sequence, and independently executing the TSDF nodes as a thread or a process, constructing the hash value queue of the nodes with updated TSDF values when the TSDF integrator updates map increments, preferably constructing the hash value queue of the nodes with updated TSDF values according to the sequence of node updating, firstly judging whether the queue is empty when the updating process starts, and continuously waiting until a new TSDF node is added; step Sg23, polling TSDF node data in a hash value queue, taking out hash values from the hash value queue in sequence, and acquiring indexes of corresponding TSDF nodes according to the hash values
Distance to node
Preferably, the hash values are sequentially taken out from the hash value queue according to a first-in first-out principle, the hash value of the first elevation node is firstly obtained, then the hash values of the neighbor nodes are obtained, and the step Sg45 is executed once for each hash value.
Further, a safety region with a predetermined thickness may be added to the updated TSDF map layer, that is, the real-time topographic mapping method may further include step Si: and carrying out simplified smoothing treatment on the boundary of the elevation map of the safety boundary, and compressing the data volume of the updated safety boundary topographic map.
Specifically, referring to fig. 7 and 8, step Si includes step Si1 of calculating an absolute height value of the current node using the absolute height value of the current node before update, a preset smoothing height, and an absolute height value of the current node bottom anchor point; si3, the absolute height value of the current node is updated in the elevation map data. The step of calculating the absolute height value of the current node using the absolute height value of the current node before updating, the preset smoothing height, and the absolute height value of the anchor point at the bottom of the current node, that is, step Si1 specifically includes: setting the preset smooth height to be HtolI.e. a smooth height range of Htol，HtolCorresponding to the height of the cylinder in fig. 7, the anchor point of the cylinder is the center of the circle of the lower end surface of the cylinder, i.e., the node. And updating the distance value of the nodes of the topographic map by adopting a breadth-first searching method. The absolute height value before the last node update is h "n-1The absolute height value before current node update is h'nAnd the absolute height value of the bottom anchor point of the current node is h'n+HtolThen the updated absolute height value of the current node is
The steps shown in FIGS. 6 and 7 described aboveThe main objective of (1) is to increase a safety area with a certain thickness on the basis of the establishment of a TSDF map, and also to reduce the data volume of a unit area terrain map. FIG. 7 is a schematic diagram in a two-dimensional sense, in consideration of the existence of factors affecting the mapping accuracy, such as the accuracy of the laser radar, the pose data error, etc., a safety region with a certain thickness needs to be added, and the thickness of the safety region is recorded as dsThe safety zone is defined as the distance d extending outward in the direction of the surface normalsThe resulting boundary surface being displaced outwardly by a distance d in the direction of the normal to the outer surfacesAnd the resulting boundary surface. Because there are often a large number of trees, houses, etc. on the ground surface with low height objects, and the air course of the aircraft is generally above these objects, similar detailed information is of little significance to the air flight, and in order to reduce the data volume of the topographic map, it is necessary to filter out these details. The cylinders with cross marks and dot marks in fig. 7 respectively represent the terrain data before and after filtering, detail information fluctuating within the height range of the cylinders is smoothed, that is, the cylinders with dot marks are smoothed, and finally, the terrain map is expressed by a small number of points represented by the cylinders with cross marks.
The simplified smooth boundary step can also be converted into a simplified method based on a local area map, such as a concave packet simplified algorithm commonly used in three-dimensional grids.
The real-time topographic mapping method may further include the steps of: sb: and constructing a hash table according to the characteristics of the TSDF map layer, wherein each node in the TSDF map layer comprises three fields, namely a hash value hn, a node center coordinate (x, y, z) n and a truncation distance dn.
The invention also provides a real-time topographic mapping system, please refer to fig. 9, which is applied to an aircraft and comprises a flight control unit FCU and a topographic real-time mapping module, wherein the topographic real-time mapping module generates an updated safety boundary topographic map, and the topographic real-time mapping module executes any one of the above real-time topographic mapping methods. The real-time topographic mapping module maps the terrain in real time, has the functions of maintenance, updating and loading, can acquire a safety boundary topographic map within a certain range, and transmits the topographic map to the FCU to control the flight so as to achieve the purpose of guiding and avoiding obstacles.
With further reference to fig. 10, the present application provides the above real-time topographic method and system aiming at the characteristic of low data volume used for real-time topographic map mapping and unit area representation in air flight, and the method and system can be mainly divided into three parts, namely data preprocessing, TSDF map incremental updating and safety boundary topographic map incremental updating.
The data preprocessing part aims to provide the TSDF map increment updating part with the point cloud key frame after motion compensation and the position and the posture information corresponding to the key frame. The method comprises the steps that information collection is carried out by an IMU (inertial measurement unit), a LIDAR (Light Detection And Ranging) And a GNSS (Global Navigation Satellite System) to serve as input data, IMU information provides high-frequency space pose information, the LIDAR provides point cloud information of a working scene in a visual field range, And the GNSS provides low-frequency Global space position information to perform accumulated error compensation on the pose information provided by the IMU. The part can be divided into IMU information pre-integration, point cloud frame motion compensation and key frame detection. In the IMU information pre-integration, the IMU information pre-integration is carried out by calculating the relative motion between point cloud frames of the laser radar, and the process can be realized by a relatively classical and general method. In point cloud frame motion compensation, points in a frame of point cloud are not acquired at the same time. In the acquisition process, the laser radar moves along with the carrier, so that the coordinate systems of different laser points are changed all the time, and finally formed point cloud frame data cannot accurately reflect the geometric shape of an object in a visual field, namely distortion is generated. In order to simplify the operation, a carrier, namely an aircraft, is assumed to collect a frame of point cloud under the uniform linear velocity and the uniform angular velocity, and a pose matrix of each point relative to a first point in the frame is calculated by interpolating a pre-integration result of IMU information and is used for correcting the point cloud frame. In the key frame detection, because the data volume of the point cloud is large, the excessive data volume is not beneficial to the real-time property of processing on the premise of meeting the requirement of topographic mapping, and therefore the key frame detection and extraction are required. In the invention, the detection of the key frame only depends on the variation of the position and the angle of the carrier, and when any variation is larger than a preset threshold value, the point cloud frame corresponding to the moment is selected as the key frame.
The main objective of the TSDF map incremental updating part is to incrementally fuse the multiple layers of point cloud data scanned at different times in the same area, i.e., the point cloud key frame, and the position and attitude information corresponding to the point cloud key frame into a single surface layer to generate an updated TSDF map layer. Because the TSDF map layer is composed of a series of TSDF nodes with a cube with a determined side length, which is the resolution of the TSDF map, the data volume of the TSDF map is only in direct proportion to the area of the map to be represented, and is not related to the covering times of the scanning surface. The TSDF map layer is a hash table, as shown in table 1, each node has three fields, which are a hash value, a node center coordinate, and a truncation distance, respectively. The Hash value is calculated by the node center coordinates, so that the low repeatability of the Hash value of each node is ensured, and the node attribute can be conveniently and rapidly inquired through the Hash value.
TABLE 1 TSDF map hash table
Let the truncation distance be DTAnd after the resolution of the TSDF node is rho, performing incremental updating on the TSDF map.
The main objective of the safe boundary topographic map increment updating part is to increase a certain thickness of safe region on the basis of the TSDF map establishment and simultaneously reduce the data volume of the topographic map per unit area. Reference may be made in particular to the above explanation of step Sg and of fig. 6, 7 and 7.
Finally, the present application provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of any of the above-mentioned real-time mapping methods.
In conclusion, the invention can generate the topographic map from the topographic data generated in real time for flight control obstacle avoidance by processing the data acquired in real time, and the invention enables the generated topographic map to have a safety boundary with higher confidence coefficient by improving the measurement characteristics and precision of the measurement sensor, and simultaneously simplifies the earth surface geometry and has low data volume.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A method of real-time topographical mapping, comprising:
acquiring a point cloud key frame and position and attitude information corresponding to the point cloud key frame in real time;
updating a map layer of the TSDF map in an incremental manner by using the point cloud key frame and the corresponding position and posture information;
and expanding the map layer of the updated TSDF map by a preset thickness to generate an elevation map containing a safety boundary.
2. The method of real-time topographic mapping according to claim 1, wherein said incrementally updating a map layer of a TSDF map using the point cloud keyframes and the corresponding position and pose information comprises:
and fusing a plurality of layers of point cloud key frames which are scanned at different moments in the same view field area and corresponding position and attitude information into a single surface layer in an incremental mode to generate an updated TSDF map layer.
3. The method of real-time topographic mapping according to claim 2, wherein said expanding the map layer of the updated TSDF map by a preset thickness generates an elevation map including a safety margin, comprising:
and expanding a preset distance outwards along the surface normal direction to form a safety area with a preset thickness for the map layer of the updated TSDF map, and generating an elevation map containing a safety boundary.
4. A method of real-time topographic mapping according to claim 1, wherein: the real-time acquisition of the point cloud key frame and the position and attitude information corresponding to the point cloud key frame specifically comprises:
acquiring point cloud information of a current working scene in a visual field range, corresponding high-frequency space pose information and corresponding low-frequency global space position information through a sensor;
performing information pre-integration on the high-frequency space pose information;
interpolating and calculating a pose matrix of each point relative to a first point in the point cloud frame through an information pre-integration result;
correcting the point cloud frame according to the pose matrix and the low-frequency global spatial position information to obtain a corrected point cloud frame;
and when the variation of the position or the angle of the carrier is larger than a preset threshold value, selecting the corrected point cloud frame corresponding to the moment as a point cloud key frame.
5. The method of real-time topographic mapping of claim 2, wherein incrementally fusing the point cloud keyframe representing multiple layers of point cloud keyframes scanned at different times within the same field of view and corresponding position and pose information into a single surface layer, generating an updated TSDF map layer comprises:
converting the acquired original point cloud data expressed under the point cloud coordinate system into point cloud data under a TSDF (time-resolved diffusion) terrain global coordinate system;
under a TSDF (time-dependent dynamic distribution function) terrain global coordinate system, calculating node center coordinates and truncation distances corresponding to each point in the point cloud key frame data;
and updating the TSDF map layer data by using the calculated node center coordinate and the truncated distance corresponding to each point.
6. The method of real-time topographic mapping according to claim 3, wherein the extending the predetermined distance outward in the surface normal direction to add a safety zone of a predetermined thickness to the map layer of the updated TSDF map, and wherein the generating an elevation map including a safety margin comprises:
in the vertical direction, the map layer is expanded upwards along the height of the terrain to enable the map layer to be expanded outwards along the surface normal direction by a preset distance;
and in the horizontal direction, the map layer is expanded outwards along the horizontal direction so as to expand the map layer outwards along the surface normal direction by a preset distance.
7. The method of real-time topographical mapping according to claim 6, further comprising:
establishing an elevation node corresponding to an elevation map according to the updated node in the TSDF map;
wherein, in the vertical direction, upwards expand the map layer along the topography height and make the map layer expand outward along surface normal direction and predetermine the distance and include:
updating the truncation distance of the corresponding elevation node according to the updated truncation distance of the node in the TSDF map and a preset distance;
wherein, on the horizontal direction, outwards expand map layer along the horizontal direction and make map layer expand outward along surface normal direction and predetermine the distance, include:
searching elevation topographic map nodes of the elevation nodes in a preset neighborhood radius range to serve as neighborhood nodes;
and updating the truncation distance of the neighborhood nodes according to the truncation distance of the neighborhood nodes and the truncation distance of the current elevation node.
8. A method of real-time topographic mapping according to any of the claims 1-7, further comprising:
and carrying out simplified smoothing processing on the boundary of the elevation map of the safety boundary, and compressing the data volume of the updated elevation map of the safety boundary.
9. A method of real-time topographical mapping as recited in claim 8, wherein the boundary of the elevation map of the safety boundary is subjected to a simplified smoothing process, and compressing the data volume of the elevation map of the updated safety boundary comprises:
calculating the absolute height value of the current node by using the absolute height value of the current node before updating, the preset smooth height and the absolute height value of the anchor point at the bottom of the current node;
and updating the absolute height value of the current node in the elevation map data.
10. A real-time topographic system for an aircraft comprising a flight control unit and a real-time topographic module for generating an updated topographic map of a safety boundary, the real-time topographic module performing the method of any one of claims 1 to 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111095595.XA CN113804183B (en) | 2021-09-17 | 2021-09-17 | Real-time topographic mapping method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111095595.XA CN113804183B (en) | 2021-09-17 | 2021-09-17 | Real-time topographic mapping method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113804183A true CN113804183A (en) | 2021-12-17 |
| CN113804183B CN113804183B (en) | 2023-12-22 |
Family
ID=78939587
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111095595.XA Active CN113804183B (en) | 2021-09-17 | 2021-09-17 | Real-time topographic mapping method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113804183B (en) |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1567385A (en) * | 2003-06-19 | 2005-01-19 | 邓兴峰 | Panoramic reconstruction method of three dimensional image from two dimensional image |
| CN103116360A (en) * | 2013-01-31 | 2013-05-22 | 南京航空航天大学 | Unmanned aerial vehicle obstacle avoidance controlling method |
| CN106446815A (en) * | 2016-09-14 | 2017-02-22 | 浙江大学 | Simultaneous positioning and map building method |
| CN106663411A (en) * | 2014-11-16 | 2017-05-10 | 易欧耐特感知公司 | Systems and methods for augmented reality preparation, processing, and application |
| US20180350088A1 (en) * | 2017-05-31 | 2018-12-06 | Google Llc | Non-rigid alignment for volumetric performance capture |
| CN109923589A (en) * | 2016-11-14 | 2019-06-21 | 深圳市大疆创新科技有限公司 | Building and update hypsographic map |
| CN110335316A (en) * | 2019-06-28 | 2019-10-15 | Oppo广东移动通信有限公司 | Method, apparatus, medium and electronic equipment are determined based on the pose of depth information |
| CN110631581A (en) * | 2018-06-22 | 2019-12-31 | 华为技术有限公司 | Method for establishing indoor 3D map and unmanned aerial vehicle |
| CN112083725A (en) * | 2020-09-04 | 2020-12-15 | 湖南大学 | Structure-shared multi-sensor fusion positioning system for automatic driving vehicle |
| CN112947531A (en) * | 2021-03-26 | 2021-06-11 | 上海工程技术大学 | Control method, device and equipment of unmanned aerial vehicle and storage medium |
| CN113052906A (en) * | 2021-04-01 | 2021-06-29 | 福州大学 | Indoor robot positioning method based on monocular camera and odometer |
| CN113063427A (en) * | 2020-01-02 | 2021-07-02 | 广东博智林机器人有限公司 | Indoor high-precision map production method, device, equipment and storage medium |
| CN113342038A (en) * | 2016-02-29 | 2021-09-03 | 星克跃尔株式会社 | Method and system for generating a map for flight of an unmanned aerial vehicle |
| CN113379910A (en) * | 2021-06-09 | 2021-09-10 | 山东大学 | Mobile robot mine scene reconstruction method and system based on SLAM |
-
2021
- 2021-09-17 CN CN202111095595.XA patent/CN113804183B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1567385A (en) * | 2003-06-19 | 2005-01-19 | 邓兴峰 | Panoramic reconstruction method of three dimensional image from two dimensional image |
| CN103116360A (en) * | 2013-01-31 | 2013-05-22 | 南京航空航天大学 | Unmanned aerial vehicle obstacle avoidance controlling method |
| CN106663411A (en) * | 2014-11-16 | 2017-05-10 | 易欧耐特感知公司 | Systems and methods for augmented reality preparation, processing, and application |
| CN113342038A (en) * | 2016-02-29 | 2021-09-03 | 星克跃尔株式会社 | Method and system for generating a map for flight of an unmanned aerial vehicle |
| CN106446815A (en) * | 2016-09-14 | 2017-02-22 | 浙江大学 | Simultaneous positioning and map building method |
| CN109923589A (en) * | 2016-11-14 | 2019-06-21 | 深圳市大疆创新科技有限公司 | Building and update hypsographic map |
| US20180350088A1 (en) * | 2017-05-31 | 2018-12-06 | Google Llc | Non-rigid alignment for volumetric performance capture |
| CN110631581A (en) * | 2018-06-22 | 2019-12-31 | 华为技术有限公司 | Method for establishing indoor 3D map and unmanned aerial vehicle |
| CN110335316A (en) * | 2019-06-28 | 2019-10-15 | Oppo广东移动通信有限公司 | Method, apparatus, medium and electronic equipment are determined based on the pose of depth information |
| CN113063427A (en) * | 2020-01-02 | 2021-07-02 | 广东博智林机器人有限公司 | Indoor high-precision map production method, device, equipment and storage medium |
| CN112083725A (en) * | 2020-09-04 | 2020-12-15 | 湖南大学 | Structure-shared multi-sensor fusion positioning system for automatic driving vehicle |
| CN112947531A (en) * | 2021-03-26 | 2021-06-11 | 上海工程技术大学 | Control method, device and equipment of unmanned aerial vehicle and storage medium |
| CN113052906A (en) * | 2021-04-01 | 2021-06-29 | 福州大学 | Indoor robot positioning method based on monocular camera and odometer |
| CN113379910A (en) * | 2021-06-09 | 2021-09-10 | 山东大学 | Mobile robot mine scene reconstruction method and system based on SLAM |
Non-Patent Citations (1)
| Title |
|---|
| 高成强 等: "面向室内动态环境的半直接法RGB-D SLAM算法", 《机器人》, vol. 41, no. 3, pages 372 - 383 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113804183B (en) | 2023-12-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102001728B1 (en) | Method and system for acquiring three dimentional position coordinates in non-control points using stereo camera drone | |
| CN108763287B (en) | Construction method of large-scale passable regional driving map and unmanned application method thereof | |
| KR102007567B1 (en) | Stereo drone and method and system for calculating earth volume in non-control points using the same | |
| CN102426019B (en) | Unmanned aerial vehicle scene matching auxiliary navigation method and system | |
| CN102506824B (en) | Method for generating digital orthophoto map (DOM) by urban low altitude unmanned aerial vehicle | |
| KR100884100B1 (en) | System and method for detecting vegetation canopy using airborne laser surveying | |
| CN109143257A (en) | Unmanned aerial vehicle onboard radar mining land change monitors system and method | |
| AU2019446069B2 (en) | Generation system and method for high-precision three-dimensional navigation map of fully mechanized mining surface | |
| AU2007355942A2 (en) | Arrangement and method for providing a three dimensional map representation of an area | |
| KR100948099B1 (en) | System and method for calculating vegetation area using airborne laser surveying | |
| KR102127679B1 (en) | System for correcting geometry of mobile platform with sensor based on an orthophoto | |
| KR20180127568A (en) | Method and apparatus of generating 3-dimension route applied geographic information | |
| CN115127510A (en) | Triphibian three-dimensional unmanned multi-platform linkage landslide intelligent patrol system | |
| US20230186561A1 (en) | Method for 3d reconstruction from satellite imagery | |
| KR101877900B1 (en) | 3d flight route creating system and method by predicting battery consumption | |
| CN113155126B (en) | Visual navigation-based multi-machine cooperative target high-precision positioning system and method | |
| Tse et al. | 3D city modelling from LIDAR data | |
| CN116136417B (en) | Unmanned vehicle local path planning method facing off-road environment | |
| CN117289301A (en) | Air-ground unmanned platform collaborative path planning method under unknown off-road scene | |
| CN116912443A (en) | Mining area point cloud and image fusion modeling method using unmanned aerial vehicle aerial survey technology | |
| CN112132951A (en) | Method for constructing grid semantic map based on vision | |
| CN113804183A (en) | Real-time topographic surveying and mapping method and system | |
| KR102488553B1 (en) | Drone used 3d mapping method | |
| KR101181742B1 (en) | Apparatus and method for land-use map renewel | |
| KR102557775B1 (en) | Drone used 3d mapping method |
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 |