CN116088489B - Grid map updating method and device - Google Patents
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Abstract
The invention discloses a grid map updating method and device, which relate to the field of unmanned driving, and can determine the number of times that a sensor can observe in the process that unmanned equipment passes through a geographic area corresponding to one cell in a grid map, wherein the number of times is in negative correlation with the accumulated probability, and then determine the cell corresponding to an obstacle to be determined in the grid map as a target cell according to sensor data, accumulate the determined hit probability to obtain the occupation probability corresponding to the target cell, so as to obtain an updated grid map, and avoid the obstacle through the updated grid map, thereby reducing noise generated in the grid map.
Description
Technical Field
The present disclosure relates to the field of unmanned driving, and in particular, to a method and an apparatus for updating a grid map.
Background
In the unmanned technique, the unmanned device needs to sense an obstacle through sensor data observed by a sensor, so as to avoid the obstacle.
In the prior art, an unmanned device may create a grid map for representing a position where an obstacle exists, where each cell in the grid map corresponds to an occupancy probability, where the occupancy probability represents a probability that the obstacle exists in a geographic area to which the cell corresponds, and if the unmanned device determines, through sensor data, that the obstacle may exist in the geographic area to which a certain cell corresponds, the occupancy probability of the cell may be increased by a fixed probability, so as to update the grid map, and in a driving process of the unmanned device, avoidance of the obstacle may be performed through the continuously updated grid map.
However, in practical applications, the sensor may observe some noise, that is, due to a problem caused by the sensor itself, the sensor may erroneously observe an obstacle at a position where the obstacle is not present, and such noise may cause an excessively high occupancy probability to be erroneously accumulated in updating the grid map, so that an error exists in the occupancy probability in the grid map.
Therefore, how to reduce the errors in the grid map is a problem to be solved.
Disclosure of Invention
The present disclosure provides a method and an apparatus for updating a grid map, so as to partially solve the above-mentioned problems in the prior art.
The technical scheme adopted in the specification is as follows:
the specification provides a grid map updating method, which comprises the following steps:
determining the number of times that the sensor can observe in the process that the unmanned equipment passes through a geographic area corresponding to a cell in the grid map, wherein the number of times is used as the number of observation times;
determining the current corresponding hit probability according to the observation times, wherein the observation times and the hit probability are in a negative correlation, and the hit probability is used for accumulating the occupation probability corresponding to the cells in the grid map after the unmanned equipment observes the obstacle;
according to the sensor data acquired by the unmanned equipment, determining a corresponding cell of an obstacle to be determined in the grid map as a target cell, accumulating the hit probability to obtain an occupation probability corresponding to the target cell, so as to obtain an updated grid map, and avoiding the obstacle through the updated grid map, wherein the occupation probability corresponding to the target cell represents the probability that the obstacle to be determined occupies the target cell.
Optionally, determining the number of times that the sensor can observe in the process that the unmanned device passes through the geographic area corresponding to one cell in the grid map, as the number of observations, specifically includes:
And determining the observation times according to the current state information of the unmanned equipment.
Optionally, the status information includes a current running speed of the unmanned device;
according to the current state information of the unmanned equipment, determining the number of times that the sensor can observe in the process that the unmanned equipment passes through one cell in the grid map, wherein the number of times is used as the observation number of times, and the method specifically comprises the following steps:
and determining the observation times according to the running speed, the observation interval of the sensor and the side length of the unit cells of the unit cell map, wherein the observation interval is used for representing the interval duration between two adjacent observations of the sensor.
Optionally, the determining the observing times according to the running speed, the observing interval duration of the sensor, and the cell side length of the cells in the grid map specifically includes:
determining the length of a diagonal line in a cell of the grid map according to the side length of the cell of the grid map, and taking the length as the maximum driving distance corresponding to the cell;
and determining the observation times according to the maximum driving distance, the driving speed and the observation interval of the sensor.
Optionally, the determining the observing times according to the running speed, the observing interval duration of the sensor, and the cell side length of the cells in the grid map specifically includes:
and determining the observation times according to the running speed, the observation interval of the sensor, a preset error parameter and the cell side length of the cells in the grid map, wherein the error parameter is used for representing an error corresponding to the running speed and an error corresponding to the observation interval duration.
Optionally, determining the current corresponding hit probability according to the observation times specifically includes:
and determining the current corresponding hit probability according to the observation times and the set probability, wherein, for each cell in the grid map, if the corresponding occupation probability of the cell exceeds the set probability, determining that the cell is occupied by an obstacle, and avoiding the cell.
Optionally, determining the current corresponding hit probability according to the observation times specifically includes:
and if the obstacle to be determined with the constant relative position with the unmanned equipment exists according to the sensor data, determining the current corresponding hit probability according to the observation times.
Optionally, the grid map corresponds to a thermodynamic diagram, and the thermodynamic diagram includes a thermodynamic diagram cell corresponding to each cell in the grid map, and for each cell, if an occupation probability corresponding to the cell is positively correlated with a thermodynamic value corresponding to a thermodynamic diagram cell corresponding to the cell, the higher the occupation probability corresponding to the cell, the more prominent the thermodynamic diagram cell corresponding to the cell is in the thermodynamic diagram;
the method further comprises the steps of:
updating the thermodynamic diagram according to the updated grid map to obtain an updated thermodynamic diagram;
and aggregating thermodynamic diagram cells which are not smaller than a preset thermodynamic value and are close in distance in the updated thermodynamic diagram, and taking a geographical area corresponding to the cells corresponding to each aggregated thermodynamic diagram cell as a risk area so that the unmanned equipment avoids the risk area.
The present specification provides an updating apparatus of a grid map, including:
the number determining module is used for determining the number of times that the sensor can observe in the process that the unmanned equipment passes through the geographic area corresponding to one cell in the grid map, and the number of times is used as the number of observation times;
the probability determining module is used for determining the current corresponding hit probability according to the observation times, wherein the observation times and the hit probability are in a negative correlation relationship, and the hit probability is used for accumulating the occupation probability corresponding to the unit cells in the grid map after the unmanned equipment observes the obstacle;
The updating module is used for determining a cell corresponding to the obstacle to be determined in the grid map according to the sensor data acquired by the unmanned equipment, taking the cell as a target cell, accumulating the hit probability to obtain an occupation probability corresponding to the target cell, obtaining an updated grid map, and avoiding the obstacle through the updated grid map, wherein the occupation probability corresponding to the target cell represents the probability that the obstacle to be determined occupies the target cell.
The present specification provides a computer readable storage medium storing a computer program which when executed by a processor implements the above-described grid map updating method.
The present specification provides an unmanned device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above-described grid map updating method when executing the program.
The above-mentioned at least one technical scheme that this specification adopted can reach following beneficial effect:
according to the method, the number of times that the sensor can observe in the process that the unmanned equipment passes through the geographic area corresponding to one cell in the grid map can be determined, the number of times is used as the number of observations, the current corresponding hit probability is determined according to the number of observations, the number of observations and the accumulated probability are in a negative correlation relation, the cell corresponding to the obstacle to be determined in the grid map is determined according to the sensor data and is used as the target cell, the hit probability is accumulated, the occupancy probability corresponding to the target cell is obtained, the updated grid map is obtained, the obstacle is avoided through the updated grid map, and the occupancy probability corresponding to the target cell is used for representing the probability that the obstacle to be determined occupies the target cell.
From the above, it can be seen that, in the running process of the unmanned device, the method can dynamically adjust the hit probability of updating the grid map, when the observation times are large, the hit probability is small, and when the observation times are small, the hit probability is large, so when the observation times are large, the hit probability is small, the cell where the noise point observed by the sensor is located is not easy to accumulate too large occupation probability, and the fixed obstacle can be normally observed and avoided, and when the observation times are small, the situation is usually generated under the condition that the speed of the unmanned device is high, the hit probability is increased, and the unmanned device can ensure that the fixed obstacle is normally avoided.
Drawings
The accompanying drawings, which are included to provide a further understanding of the specification, illustrate and explain the exemplary embodiments of the present specification and their description, are not intended to limit the specification unduly. In the drawings:
FIG. 1 is a schematic diagram of noise observed by a sensor provided herein;
fig. 2 is a flow chart of a method for updating a grid map in the present disclosure;
FIG. 3 is a schematic illustration of a thermodynamic diagram as referred to in this specification;
fig. 4 is a schematic diagram of an updating device of a grid map provided in the present specification;
fig. 5 is a schematic view of the unmanned device corresponding to fig. 1 provided in the present specification.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present specification more apparent, the technical solutions of the present specification will be clearly and completely described below with reference to specific embodiments of the present specification and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present specification. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.
In the prior art, the sensor may observe a noise point that is fixed relative to the unmanned device, as shown in fig. 1.
Fig. 1 is a schematic diagram of noise observed by a sensor provided in the present specification.
In fig. 1, white circles are unmanned devices, black circles are noise points observed by a sensor, and the same noise points do not disappear in a short time, so that the sensor observes noise points consistent with the relative position of the unmanned devices in the short time, and the noise points are not actual obstacles only because of the problems of the sensor, and the noise points can be regarded as obstacles by the unmanned devices, so that updating of a grid map is affected.
That is, after the sensor observes such a noise, the unmanned device continuously increases the occupation probability of the cell where the noise is located in the process of continuously updating the occupation probability of the cell in the grid map, and the occupation probability determined in this way has a larger accumulated error (that is, the cell where no obstacle exists originally, the occupation probability of the cell is accumulated to be large), so that the unmanned device may possibly ignore the cell of the normal obstacle or cause unstable driving condition of the unmanned device in order to avoid such a cell. In order to solve the above problems, the update method of the grid map provided in the present disclosure enables the hit probability to be dynamically changed, and when the unmanned device can observe multiple times in one cell, the hit probability is lower, so that the occupation probability with larger error cannot be accumulated through noise points.
The following describes in detail the technical solutions provided by the embodiments of the present specification with reference to the accompanying drawings.
Fig. 2 is a flow chart of a method for updating a grid map in the present specification, which specifically includes the following steps:
s201: and determining the number of times that the sensor can observe in the process that the unmanned equipment passes through the geographic area corresponding to one cell in the grid map, and taking the number of times as the number of observation.
In practical application, the unmanned equipment can observe the external environment through the sensor so as to avoid the obstacle, so that the unmanned equipment can determine the number of times that the sensor can observe in the process that the unmanned equipment passes through one cell in the grid map as the observation number.
The grid map comprises a plurality of cells, each cell represents a geographic area in the real world, each cell corresponds to an occupation probability, and the occupation probability corresponding to one cell represents the probability that the geographic area corresponding to the cell is occupied by an obstacle. The sensor can observe the surrounding environment according to certain frequency in the unmanned equipment driving process, and each time the sensor observes, unmanned equipment can update the grid map once, namely, after unmanned equipment observes once through the sensor, can determine which cells have barriers, and then can increase certain hit probability with the occupation probability that these cells correspond, so, if the occupation probability that a certain cell corresponds is higher, then can regard as the cell that can have the barrier certainly to this cell, in unmanned equipment driving process, can avoid such cell to go.
The number of times that the sensor can observe in the process that the unmanned equipment passes through the geographic area corresponding to one cell in the grid map can be determined according to the current state information of the unmanned equipment, and the state information can be used as the number of observations, and can represent the running state of the unmanned equipment, such as running speed, acceleration and the like.
The above-mentioned number of observations may refer to the maximum number of observations that the sensor can observe in the process that the unmanned device passes through the geographic area corresponding to one cell in the grid map, or may refer to the minimum number of observations that the sensor can observe in the process that the unmanned device passes through the geographic area corresponding to one cell in the grid map, or the like (of course, the number of observations between the maximum number of observations and the minimum number of observations may also be determined).
Therefore, there may be various ways to determine the number of observations, and specifically, the number of observations may be determined according to the current running speed of the unmanned device, the observation interval of the sensor, and the cell side lengths of the cells in the grid map. The travel speed mentioned here may refer to a travel speed determined irrespective of the direction of the unmanned device, and may also refer to a travel speed in the direction of the cell furthest distance, and the observation interval mentioned here is used to indicate the interval duration between two adjacent observations of the sensor. The side length of the cell in the grid map is the minimum distance of the unmanned equipment running through the cell, so that the observation times can be determined directly according to the side length of the cell, and the determined observation times are the observation times of the sensor under the condition that the unmanned equipment runs through the minimum distance of the cell.
Of course, the length of the diagonal line in the cell of the grid map may be determined as the maximum travel distance corresponding to the cell by the side length of the cell, and the number of observations of the sensor may be determined according to the current travel speed of the unmanned device and the observation interval of the sensor, that is, the number of observations of the sensor under the condition that the unmanned device travels the maximum distance of the cell, and may be calculated specifically by the following formula.
The above formula is described by taking the example in which the cells in the grid map are three-dimensional cells, routing is the cell side length,for the maximum diagonal length within a cell, i.e. the maximum travel distance within the geographic area to which a cell corresponds, fps is the observation interval of the sensor and velnorm is the current travel speed of the unmanned device (here a model of the travel speed is required to be used). That is, this formula is the number of sensor observations under the condition that the maximum distance in one cell is determined to be travelled by the unmanned device, and of course, the above is exemplified by taking the cell as three-dimensional, and if the cell is two-dimensional, the above formula can be expressed as ∈>Replaced by- >
It should be noted that, since the running speed of the unmanned device is estimated by the data measured by the sensor, there may be a certain error with the actual running speed of the unmanned device, and the observation interval of the sensor may also have a certain error due to the delay of data transmission, then the error parameter may be determined in advance, and the observation times may be determined by the error parameter, the running speed, the observation interval of the sensor, and the cell side length, and in particular, the observation times may be determined by the following formula.
Wherein a in the above formula is an error parameter, and the error parameter is used for representing an error corresponding to the running speed and an error corresponding to the observation interval. The error parameter is pre-calibrated.
Of course, the determination of the number of observations may be performed in other manners, for example, a table of correspondence between the state information and the number of observations may be determined, and the number of observations matching with the current state information of the unmanned device may be determined from the table of correspondence between the state information and the number of observations, as the determined number of observations that can be made by the sensor during the process that the unmanned device passes through the geographic area corresponding to one cell in the grid map. The method can collect the times that the historical unmanned equipment passes through one cell in the grid map and is observed by the sensor, and count the average observation times of the unmanned equipment under different state information, so that the corresponding relation table is determined according to the average observation times of the unmanned equipment under each state information. The above-mentioned state information includes the traveling speed, and of course, state information such as acceleration of the unmanned device may be included.
S202: and determining the current corresponding hit probability according to the observation times, wherein the observation times and the hit probability are in a negative correlation, and the hit probability is used for accumulating the occupation probability corresponding to the cells in the grid map after the unmanned equipment observes the obstacle.
In order to reduce the above-mentioned accumulated error caused by the noise fixed in relative position to the unmanned device, the current corresponding hit probability may be determined according to the determined number of observations, where the current corresponding hit probability is in a negative correlation with the number of observations (that is, the size of the hit probability is related to the number of observations, the larger the number of observations, the smaller the hit probability, the smaller the number of observations, and the larger the hit probability, so that the hit probability is likely to be changed during the running of the unmanned device).
It can be seen from this that, when the number of observations is larger, the hit probability is smaller, so if there is a noise point that can generate the above-mentioned accumulated error, even if the running speed of the unmanned device is slower at this time, the number of times that the unmanned device accumulates the hit probability in one cell is larger (i.e., if there is a noise point that is fixed relative to the unmanned device, and the running speed of the unmanned device is slower, and the observation of the sensor is fixed, the update of the grid map is continuously performed, the occupancy probability of the cell corresponding to the noise point may be continuously increased), but the hit probability determined by the method is smaller, and the accumulated occupancy probability is not too high, so that the error of the occupancy probability in the grid map is reduced.
It should be further noted that, when the occupation probability corresponding to one cell is higher than the set probability, the unmanned device needs to avoid the cell, so, for each cell in the updated grid map, if the unmanned device determines that the occupation probability corresponding to the cell is not less than the set probability, it can determine that the cell is occupied by the obstacle, and control the unmanned device to avoid the cell.
When determining the hit probability, the current corresponding hit probability may be determined according to the observation times and the set probability, where the hit probability may be determined by the following formula:
as can be seen from this formula, the hit probability can be the ratio between the set probability and the number of observations, i.e., the hit probability thus calculated satisfies the condition: in the case where the unmanned apparatus travels a distance (e.g., a maximum distance) of one cell, the set probability can be obtained just after accumulating the hit probabilities according to the number of times the sensor can observe. In this way, even if the unmanned device runs slowly, in the case where there is a noise, since the hit probability is constrained to a small value, the occupation probability of the cell where the noise is located is not easily accumulated to be large rapidly.
It should be noted that, the unmanned device may further determine, according to the number of observations, the current corresponding hit probability under the condition that it is determined that the obstacle to be determined has a constant relative position to the unmanned device according to the above sensor data, for example, if it is determined that the obstacle to be determined has a constant relative position to the unmanned device according to the sensor data within the previous 1 second, then determine the current corresponding hit probability by the method, otherwise, update the grid map directly by the preset hit probability.
S203: according to the sensor data acquired by the unmanned equipment, determining a corresponding cell of an obstacle to be determined in the grid map as a target cell, accumulating the hit probability to obtain an occupation probability corresponding to the target cell, so as to obtain an updated grid map, and avoiding the obstacle through the updated grid map, wherein the occupation probability corresponding to the target cell represents the probability that the obstacle to be determined occupies the target cell.
After determining the hit probability, the unmanned device may determine, according to the sensor data, a cell corresponding to the obstacle to be determined (referred to as the obstacle to be determined because the sensor data may monitor a noise point and also monitor a real obstacle) in the grid map, as a target cell, and accumulate the determined hit probability to obtain an occupancy probability corresponding to the target cell (that is, update the occupancy probability corresponding to the target cell according to the determined hit probability), so as to obtain an updated grid map, where the unmanned device may avoid the obstacle through the updated grid map, and the occupancy probability corresponding to the target cell indicates a probability that the obstacle to be determined occupies the target cell, that is, an occupancy probability corresponding to one cell indicates a probability that the obstacle to be determined exists in the cell.
The accumulating of the determined hit probabilities to obtain the corresponding occupancy probability of the target cell may refer to increasing the current determined occupancy probability of the target cell by the original occupancy probability of the target cell to obtain the updated occupancy probability of the target cell (i.e., the hit probability may be determined at each time, for the time t+1, the hit probability determined at the time t+1 may be accumulated to the occupancy probability at the time t to obtain the hit probability at the time t+1, and of course, the determining of the hit probability may be not so frequent, the observing times may be determined only when the unmanned device changes the running speed, and the hit probability may be determined according to the observing times), and updating of the grid map is completed after updating the occupancy probability of each target cell, thereby obtaining the updated grid map.
In practical application, through the method, the unmanned device takes the cell with the occupation probability higher than the set probability as the cell occupied by the obstacle, although the problem of noise is avoided, the actual obstacle (such as a moving obstacle with a fixed relative position to the unmanned device) can be ignored, and although the obstacle can not influence the running of the unmanned device, in order to further ensure the safety of the unmanned device, the method can also provide a thermodynamic diagram corresponding to the grid map.
The thermodynamic diagram may include thermodynamic diagram cells corresponding to each cell in the grid map, and if the occupation probability corresponding to each cell is positively correlated with the thermodynamic value corresponding to the thermodynamic diagram cell corresponding to the cell, the higher the occupation probability corresponding to the cell, the more the thermodynamic diagram cell corresponding to the cell is highlighted in the thermodynamic diagram, as shown in fig. 3.
Fig. 3 is a schematic illustration of a thermodynamic diagram as referred to in this specification.
As can be seen from fig. 3, the thermodynamic diagram corresponding to the grid map includes thermodynamic diagram cells, each thermodynamic diagram cell corresponds to a cell in the grid map one by one, the thermodynamic value corresponding to the thermodynamic diagram cell can be determined by the occupation probability of the cell corresponding to the thermodynamic diagram cell, if the occupation probability corresponding to one cell is higher, the thermodynamic value corresponding to the thermodynamic diagram cell corresponding to the cell is higher, and if the thermodynamic diagram cell is displayed in the thermodynamic diagram, the thermodynamic diagram is darker.
In the case of one cell in the grid map, the ratio between the occupancy probability corresponding to the cell and the set probability may be used as the thermodynamic value of the thermodynamic diagram cell corresponding to the cell, so that even if the occupancy probability does not exceed the set probability, the thermodynamic diagram cell can be represented, and if there are a plurality of thermodynamic diagram cells whose occupancy probabilities are higher and are close (but the occupancy probabilities of the corresponding cells do not exceed the set probability), the thermodynamic diagram cells may be the region in which an actual obstacle (not the noise point detected by the sensor) passes in a period of time, and the geographical region corresponding to the thermodynamic diagram cells may be used as the risk region, so that the unmanned apparatus may avoid the risk region.
Therefore, the unmanned device may aggregate the thermodynamic diagram cells with thermodynamic values not smaller than the preset thermodynamic values and close to each other, and take the geographical areas corresponding to the cells corresponding to the thermodynamic diagram cells after aggregation as the risk areas, so that the unmanned device avoids the risk areas, and taking fig. 3 as an example, the thermodynamic diagram cell a and the thermodynamic diagram cell B in fig. 3 are close to each other and have thermodynamic values higher than the preset thermodynamic values, so that the thermodynamic diagram cell a and the thermodynamic diagram cell B may be aggregated, and the geographical areas corresponding to the thermodynamic diagram cell a and the thermodynamic diagram cell B are taken as the risk areas.
The thermodynamic diagram can represent obstacles existing around the current unmanned equipment in history, but the position of the future obstacle is also important for the unmanned equipment, so that according to the time sequence of the aggregated thermodynamic diagram cells (namely, which thermodynamic diagram cell appears first and then appears), the thermodynamic diagram cell where the obstacle is possibly located at the next moment can be determined, and the geographical area corresponding to the thermodynamic diagram cell is also used as a risk area, for example, in the thermodynamic diagram cell A and the thermodynamic diagram cell B in fig. 3, the thermodynamic diagram cell A generates a larger thermodynamic value first and the thermodynamic diagram cell B generates a larger thermodynamic value later, and therefore, the geographical area corresponding to the thermodynamic diagram cell C can be used as a risk area because the obstacle is possibly present at the position of the thermodynamic diagram cell C at the next moment.
The above-mentioned unmanned device may refer to a device capable of realizing automatic driving such as an unmanned vehicle, an unmanned plane, an automatic distribution device, or the like. Based on the above, the updating method of the grid map provided by the specification can be used for updating the grid map in the running process of the unmanned equipment, so that the obstacle avoidance is performed through the grid map, and the unmanned equipment can be particularly applied to the field of distribution through the unmanned equipment, such as business scenes of distribution such as express, logistics, take-out and the like by using the unmanned equipment.
According to the method, in the running process of the unmanned equipment, the hit probability of updating the grid map is dynamically adjusted, when the observation times are large, the hit probability is small, and when the observation times are small, the hit probability is large, so that when the observation times are large, the hit probability is small, the cell where the noise point observed by the sensor is located is not easy to accumulate too large occupied probability, normal observation and obstacle avoidance can be performed on the fixed obstacle, even if the observation times are small, the concentrated probability does not exceed the set probability, the occupation probability where the noise point is located is not accumulated too large, the situation that the observation times are small is usually generated under the condition that the speed of the unmanned equipment is high, and at the moment, the hit probability is adjusted to be ensured, and the unmanned equipment can normally avoid the fixed obstacle.
The above method for updating the grid map provided for one or more embodiments of the present disclosure further provides a corresponding device for updating the grid map based on the same concept, as shown in fig. 4.
Fig. 4 is a schematic diagram of an updating device for a grid map provided in the present specification, which specifically includes:
the number determining module 401 is configured to determine the number of times that the sensor can observe during the process that the unmanned device passes through the geographic area corresponding to one cell in the grid map, as the number of observations;
the probability determining module 402 is configured to determine a current corresponding hit probability, where the number of observations and the hit probability are in a negative correlation, and the hit probability is used to accumulate occupancy probabilities corresponding to cells in a grid map after the unmanned device observes an obstacle;
the updating module 403 is configured to determine, according to sensor data collected by the unmanned device, a cell corresponding to an obstacle to be determined in the grid map, as a target cell, and accumulate the hit probabilities to obtain an occupancy probability corresponding to the target cell, so as to obtain an updated grid map, and avoid the obstacle through the updated grid map, where the occupancy probability corresponding to the target cell represents a probability that the obstacle to be determined occupies the target cell.
Optionally, the number determining module 401 is specifically configured to determine the number of observations according to current state information of the unmanned device.
Optionally, the status information includes a current running speed of the unmanned device;
the number determining module 401 is specifically configured to determine the number of observations according to the running speed, an observation interval of the sensor, and a cell side length of a cell in the grid map, where the observation interval is used to represent an interval duration between two adjacent observations of the sensor.
Optionally, the number determining module 401 is specifically configured to determine, according to a cell side length of a cell in the grid map, a diagonal length in the cell of the grid map as a maximum travel distance corresponding to the cell; and determining the observation times according to the maximum driving distance, the driving speed and the observation interval of the sensor.
Optionally, the number determining module 401 is specifically configured to determine the number of observations according to the running speed, an observation interval of the sensor, a preset error parameter, and a cell side length of a cell in the grid map, where the error parameter is used to represent an error corresponding to the running speed and an error corresponding to the observation interval duration.
Optionally, the probability determining module 402 is specifically configured to determine, according to the number of observations and a set probability, a current corresponding hit probability, where, for each cell in the grid map, if an occupancy probability corresponding to the cell exceeds the set probability, determine that the cell is occupied by an obstacle, and avoid the cell.
Optionally, the probability determining module 402 is specifically configured to determine, if an obstacle to be determined having a constant relative position to the unmanned device exists according to the sensor data, and determine the current corresponding hit probability according to the number of observations.
Optionally, the grid map corresponds to a thermodynamic diagram, and the thermodynamic diagram includes a thermodynamic diagram cell corresponding to each cell in the grid map, and for each cell, if an occupation probability corresponding to the cell is positively correlated with a thermodynamic value corresponding to a thermodynamic diagram cell corresponding to the cell, the higher the occupation probability corresponding to the cell, the more prominent the thermodynamic diagram cell corresponding to the cell is in the thermodynamic diagram;
the apparatus further comprises:
a thermodynamic diagram module 404, configured to update the thermodynamic diagram according to the updated grid map, and obtain an updated thermodynamic diagram; and aggregating thermodynamic diagram cells which are not smaller than a preset thermodynamic value and are close in distance in the updated thermodynamic diagram, and taking a geographical area corresponding to the cells corresponding to each aggregated thermodynamic diagram cell as a risk area so that the unmanned equipment avoids the risk area.
The present specification also provides a computer-readable storage medium storing a computer program operable to perform the above-described updating method of the grid map provided in fig. 2.
The present specification also provides a schematic block diagram of the unmanned device shown in fig. 5. At the hardware level, the unmanned device includes a processor, an internal bus, a network interface, a memory, and a non-volatile storage, as described in fig. 5, although other hardware required by other services may be included. The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to implement the grid map updating method described in fig. 2. Of course, other implementations, such as logic devices or combinations of hardware and software, are not excluded from the present description, that is, the execution subject of the following processing flows is not limited to each logic unit, but may be hardware or logic devices.
In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable Gate Array, FPGA)) is an integrated circuit whose logic function is determined by the programming of the device by a user. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present specification.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the present specification may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing is merely exemplary of the present disclosure and is not intended to limit the disclosure. Various modifications and alterations to this specification will become apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, or the like, which are within the spirit and principles of the present description, are intended to be included within the scope of the claims of the present description.
Claims (11)
1. A method of updating a grid map, comprising:
determining the number of times that the sensor can observe in the process that the unmanned equipment passes through a geographic area corresponding to a cell in the grid map, wherein the number of times is used as the number of observation times;
determining the current corresponding hit probability according to the observation times, wherein the observation times and the hit probability are in a negative correlation;
according to the sensor data acquired by the unmanned equipment, determining a corresponding cell of an obstacle to be determined in the grid map as a target cell, accumulating the hit probability to obtain an occupation probability corresponding to the target cell, so as to obtain an updated grid map, and avoiding the obstacle through the updated grid map, wherein the occupation probability corresponding to the target cell represents the probability that the obstacle to be determined occupies the target cell.
2. The method of claim 1, wherein determining the number of times that the sensor can observe during the process that the unmanned device passes through the geographic area corresponding to one cell in the grid map, as the number of observations, specifically comprises:
and determining the observation times according to the current state information of the unmanned equipment.
3. The method of claim 2, wherein the status information includes a current travel speed of the unmanned device;
according to the current state information of the unmanned equipment, determining the number of times that the sensor can observe in the process that the unmanned equipment passes through one cell in the grid map, wherein the number of times is used as the observation number of times, and the method specifically comprises the following steps:
and determining the observation times according to the running speed, the observation interval of the sensor and the side length of the unit cells of the unit cell map, wherein the observation interval is used for representing the interval duration between two adjacent observations of the sensor.
4. The method of claim 3, wherein determining the number of observations based on the travel speed, the observation interval duration of the sensor, and the cell side lengths of the cells in the grid map, comprises:
Determining the length of a diagonal line in a cell of the grid map according to the side length of the cell of the grid map, and taking the length as the maximum driving distance corresponding to the cell;
and determining the observation times according to the maximum driving distance, the driving speed and the observation interval of the sensor.
5. The method of claim 3, wherein determining the number of observations based on the travel speed, the observation interval duration of the sensor, and the cell side lengths of the cells in the grid map, comprises:
and determining the observation times according to the running speed, the observation interval of the sensor, a preset error parameter and the cell side length of the cells in the grid map, wherein the error parameter is used for representing an error corresponding to the running speed and an error corresponding to the observation interval duration.
6. The method of claim 1, wherein determining the current corresponding hit probability based on the number of observations, comprises:
and determining the current corresponding hit probability according to the observation times and the set probability, wherein, for each cell in the grid map, if the corresponding occupation probability of the cell exceeds the set probability, determining that the cell is occupied by an obstacle, and avoiding the cell.
7. The method of claim 1, wherein determining the current corresponding hit probability based on the number of observations, comprises:
and if the obstacle to be determined with the constant relative position with the unmanned equipment exists according to the sensor data, determining the current corresponding hit probability according to the observation times.
8. The method of claim 1, wherein the grid map corresponds to a thermodynamic diagram, the thermodynamic diagram comprising thermodynamic diagram cells corresponding to each cell in the grid map, wherein for each cell, if an occupancy probability corresponding to the cell is positively correlated with a thermodynamic value corresponding to the thermodynamic diagram cell corresponding to the cell, the higher the occupancy probability corresponding to the cell, the more prominently the thermodynamic diagram cell corresponding to the cell is in the thermodynamic diagram;
the method further comprises the steps of:
updating the thermodynamic diagram according to the updated grid map to obtain an updated thermodynamic diagram;
and aggregating thermodynamic diagram cells which are not smaller than a preset thermodynamic value and are close in distance in the updated thermodynamic diagram, and taking a geographical area corresponding to the cells corresponding to each aggregated thermodynamic diagram cell as a risk area so that the unmanned equipment avoids the risk area.
9. An update apparatus for a grid map, comprising:
the number determining module is used for determining the number of times that the sensor can observe in the process that the unmanned equipment passes through the geographic area corresponding to one cell in the grid map, and the number of times is used as the number of observation times;
the probability determining module is used for determining the current corresponding hit probability according to the observation times, wherein the observation times and the hit probability are in a negative correlation;
the updating module is used for determining corresponding cells of the obstacle to be determined in the grid map according to the sensor data acquired by the unmanned equipment, taking the cells as target cells, accumulating the hit probability to obtain the occupancy probability corresponding to the target cells, obtaining an updated grid map, and avoiding the obstacle through the updated grid map, wherein the occupancy probability corresponding to the target cells represents the probability that the obstacle to be determined occupies the target cells.
10. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1-8.
11. An unmanned device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of the preceding claims 1-8 when executing the program.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107402011A (en) * | 2017-09-22 | 2017-11-28 | 常州大学 | A kind of compound grid map constructing method for Greenhouse Robot |
| CN109992894A (en) * | 2019-04-03 | 2019-07-09 | 哈尔滨工程大学 | A kind of unmanned boat local environment modeling method considering perception information error |
| CN110968083A (en) * | 2018-09-30 | 2020-04-07 | 科沃斯机器人股份有限公司 | Method for constructing grid map, method, device and medium for avoiding obstacles |
| CN111220988A (en) * | 2020-01-07 | 2020-06-02 | 上海钛米机器人科技有限公司 | Map data processing method, device, equipment and medium |
| CN111307166A (en) * | 2018-12-11 | 2020-06-19 | 北京图森智途科技有限公司 | Method, device and processing equipment for constructing occupied grid map |
| CN112581613A (en) * | 2020-12-08 | 2021-03-30 | 纵目科技（上海）股份有限公司 | Grid map generation method and system, electronic device and storage medium |
| CN113077551A (en) * | 2021-03-30 | 2021-07-06 | 苏州臻迪智能科技有限公司 | Occupation grid map construction method and device, electronic equipment and storage medium |
-
2021
- 2021-11-05 CN CN202111304862.XA patent/CN116088489B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107402011A (en) * | 2017-09-22 | 2017-11-28 | 常州大学 | A kind of compound grid map constructing method for Greenhouse Robot |
| CN110968083A (en) * | 2018-09-30 | 2020-04-07 | 科沃斯机器人股份有限公司 | Method for constructing grid map, method, device and medium for avoiding obstacles |
| CN111307166A (en) * | 2018-12-11 | 2020-06-19 | 北京图森智途科技有限公司 | Method, device and processing equipment for constructing occupied grid map |
| CN109992894A (en) * | 2019-04-03 | 2019-07-09 | 哈尔滨工程大学 | A kind of unmanned boat local environment modeling method considering perception information error |
| CN111220988A (en) * | 2020-01-07 | 2020-06-02 | 上海钛米机器人科技有限公司 | Map data processing method, device, equipment and medium |
| CN112581613A (en) * | 2020-12-08 | 2021-03-30 | 纵目科技（上海）股份有限公司 | Grid map generation method and system, electronic device and storage medium |
| CN113077551A (en) * | 2021-03-30 | 2021-07-06 | 苏州臻迪智能科技有限公司 | Occupation grid map construction method and device, electronic equipment and storage medium |
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