CN112161635B - Cooperative graph building method based on minimum loop detection - Google Patents
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Abstract
The invention belongs to the field of robot sensing algorithms, and relates to a cooperative graph building method based on minimum loop detection. Firstly, a single robot generates a local map based on scanning matching and map optimization; secondly, data are exchanged among a plurality of robots through a network; and then, carrying out coordinate transformation by using a coordinate system unified algorithm, and finally, searching for a minimum loop in the unified coordinate system and optimizing. According to the cooperative mapping method based on minimum loopback detection, provided by the invention, under the condition that multiple robots participate in mapping, the intersection set among the trajectories of the robots is fully utilized, the minimum loopback construction constraint is searched, and the condition that the rear-end optimization is in a small range and multiple times is ensured, so that the effects of reducing the propagation of accumulated errors and reducing the time consumption of the rear-end optimization are achieved, and finally, the environment map which better accords with a real scene is established.
Description
Technical Field
The invention belongs to the field of robot perception algorithms, particularly relates to a method for multi-robot collaborative map building, and more particularly relates to a collaborative map building method based on minimum loop detection.
Background
Slam (simultaneous localization and mapping) instant positioning and map construction are key technologies for autonomous mobile robots and automatic driving of automobiles. Point cloud matching is a mathematical computation process that converts a collection of large-capacity three-dimensional spatial data points in two or more coordinate systems into a unified coordinate system, in fact, the transformation relationship between the two coordinate systems is to be found. The relation can be described by a rotation matrix R and translation vector T, and the point cloud matching is required to be solved for (R, T). The point cloud matching algorithm frequently used at present includes: iterative closest point algorithm (ICP), RANSAC, etc. The graph optimization is a back-end optimization mode commonly used in the SLAM field, and the main realization mode is that a constraint equation is constructed for the whole system, and the constraint equation is solved through a specific mathematical method, so that the result of the minimum global error is achieved.
Patent CN108362294A, published as 20180803, discloses a multi-vehicle cooperative map building method applied to automatic driving, which utilizes a laser sensor to obtain point cloud information of the surrounding environment, and generates a global map of the whole environment through algorithm processing. But the patent does not consider the case of loop back optimization. When a plurality of nodes exist, the conversion relation between a certain node and other nodes is calculated through a self matching algorithm, and errors are accumulated when the node runs for a long distance.
Disclosure of Invention
In order to overcome at least one defect in the prior art, the invention provides a collaborative graph establishing method based on minimum loop detection, and a graph optimization method is introduced to be applied to a minimum loop, so that overlarge one-time optimization calculation amount and overlong time consumption in a large-range scene are avoided.
In order to solve the technical problems, the invention adopts the technical scheme that: a collaborative map building method based on minimum loop detection comprises the following steps that firstly, a single robot generates a local map based on scanning matching and map optimization; secondly, data are exchanged among a plurality of robots through a network; then, coordinate transformation is carried out by utilizing a coordinate system unified algorithm, namely, the unification of the coordinate system and data fusion are carried out; and finally, searching a minimum loop in the unified coordinate system and optimizing.
Further, the whole SLAM mapping process is divided into a front end part and a rear end part, and the front end determines a key frame through laser data matching at continuous time and a loop detected in a single-vehicle travelling track; and the rear end corrects the pose information of all the robots by a general map optimization method, and then performs the same offset change on the point cloud data corresponding to the pose according to the pose offset before and after correction to obtain a final map.
In one embodiment, the single robot generates a local map based on scanning matching and map optimization, a 16-line laser radar is adopted, rich point clouds can be obtained in a scene with a short distance, interframe matching is carried out on point cloud data of continuous time by using a closest point iterative algorithm ICP (inductively coupled plasma) or a Normal Distribution Transformation (NDT) algorithm, a key frame is extracted according to a set threshold value, and a vertex of the map is formed; the point cloud data contained in the vertexes is also the local map generated by the current node.
In one embodiment, the data is exchanged among a plurality of robots through a network, and the data comprises a vehicle ID, a time stamp, IMU data, GPS data and laser odometry data when the point cloud of the frame is collected besides the point cloud data of the current node; the data is packaged and shared together with other nodes within the distance threshold value of the current node.
In one embodiment, the coordinate transformation is performed by using a coordinate system unified algorithm, after other nodes receive data, whether a conversion relation is known or not is confirmed according to the vehicle ID, and if the conversion relation is confirmed, the conversion relation is directly combined into a local map of the local nodes; otherwise, the conversion relation is calculated, if the conversion relation is successful, the next steps are the same as the known conversion relation, and if the conversion relation is failed, the conversion relation is temporarily stored and is calculated together after more data are received.
In one embodiment, the finding and optimizing the minimum loop in the unified coordinate system includes:
under the initial condition, the whole generated forest forms connected branches by taking the number of vehicles as a unit, and an initial key frame of each vehicle forms a first node of the current connected branch of the current forest;
along with the operation of the algorithm, more key frames are added into the corresponding connected branches according to the ID of the vehicle, and the forest structure is generated to be like a plurality of independent linked lists;
with continuous observation and mapping calculation, more nodes can be added to the connected branches in the forest;
when different connected branch nodes are close to each other or even coincide with each other, indicating that different vehicles coincide on the track, constructing relative observation constraint between the two nodes, simultaneously merging two originally independent connected branches in the forest, reducing the number of the connected branches in the forest, and triggering global loop detection;
generating new relative observation constraints to trigger minimum loopback search based on the current node as new key frames are continuously added; since a large loop consisting of more key frames will introduce more errors, the minimum loop searched for is optimized.
In one embodiment, when the above collaborative map building method is adopted, the method specifically includes the following steps:
s1, initializing the interior of a single robot;
s2, when the node is in a sending terminal mode, constructing a local map of the current environment, and simultaneously sending the point cloud map and the pose of the node together with additional information to a data sharing module for realizing data sharing between the nodes;
s3, when the node is in a receiving end mode, receiving data sent by other nodes, and calculating and forwarding a conversion relation;
and S4, when the triggering condition is met, searching the minimum loop where the current frame is located by the system, and optimizing.
In one embodiment, in step S1, a laser radar is installed at the top of each node, and point cloud data of the surrounding environment is collected at a frequency of 10HZ and stored in a memory in a specific format; meanwhile, a GPS antenna is also arranged at the top of the node, and longitude and latitude coordinates of the current position are obtained at the frequency of 10 HZ; simultaneously, the nodes are also provided with IMUs (inertial measurement units), record the acceleration and the angular velocity and store the acceleration and the angular velocity in a specific format; each node is both a sender and a receiver.
In one embodiment, in step S2, the point cloud data collected in step S1 is used to construct a local map; meanwhile, estimated values of the current pose are obtained by using a laser odometer and an IMU respectively, and are packed together with point cloud data, a timestamp and GPS position data and sent to other nodes in a distance range.
In one embodiment, in step S3, when receiving a new data packet, the node first searches its own maintained conversion relationship list, and if the data is data of a known conversion matrix, merges the map data after coordinate conversion into a local map data; if the data is the data of the unknown conversion matrix, the calculation of the conversion relation with the data temporarily stored in the local is tried, if the calculation is successful, the next steps are the same as those of the known conversion matrix, otherwise, the data is temporarily stored in the local, and the conversion matrix is calculated after more data are received.
In one embodiment, in step S4, the conversion relationship between the nodes is basically known along with the operation of the system, at this time, there is a large amount of intersection between the trajectories of the nodes, when a new key frame is added, the minimum loop where the key frame is located is found by using the minimum loop algorithm, and the minimum loop is optimized for the minimum loop
Compared with the prior art, the beneficial effects are:
1. each vehicle respectively maintains a global map based on a coordinate system of the vehicle, and the global map is calculated in a dynamic process without knowing the coordinate system conversion relation among the vehicles in advance;
2. in the process of single vehicle traveling, the track may also form a loop, so that local loop detection and local optimization also exist for the situation of single vehicle map building;
3. the graph optimization method is introduced to be applied to the minimum loop, so that overlarge one-time optimization calculation amount and overlong time consumption under a large-range scene are avoided;
in summary, according to the collaborative graph building method based on minimum loopback detection provided by the invention, under the condition that multiple robots participate in graph building, the intersection set among the trajectories of the robots is fully utilized to find the minimum loopback construction constraint, so that the situation that the back-end optimization is in a small range and in multiple frequencies is ensured, the effects of reducing the propagation of accumulated errors and reducing the time consumption of the back-end optimization are achieved, and finally, the environment map which better conforms to the real scene is built.
Drawings
FIG. 1 is a schematic diagram of a framework of the collaborative mapping method according to the present invention.
Fig. 2 is a schematic view of the minimum loop of the present invention.
FIG. 3 is a schematic diagram of the local map construction (single-car SLAM) of the present invention.
FIG. 4 is a diagram of the global map construction of the present invention.
Fig. 5 is a schematic diagram of a general branch reachability search algorithm.
Fig. 6 is a schematic diagram of a minimum loop algorithm.
Detailed Description
The drawings are for illustration purposes only and are not to be construed as limiting the invention; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the invention.
Example 1:
as shown in fig. 1, a collaborative mapping method based on minimum loop detection includes that, firstly, a single robot generates a local map based on scanning matching and map optimization; secondly, data are exchanged among a plurality of robots through a network; then, coordinate transformation is carried out by utilizing a coordinate system unified algorithm, namely, the unification of the coordinate system and data fusion are carried out; and finally, searching a minimum loop in the unified coordinate system and optimizing.
In the embodiment, the whole SLAM mapping process is divided into a front end part and a rear end part, and the front end determines a key frame through laser data matching at continuous time and a loop detected in a single-vehicle travelling track; and the rear end corrects the pose information of all the robots by a general map optimization method, and then performs the same offset change on the point cloud data corresponding to the pose according to the pose offset before and after correction to obtain a final map.
In one embodiment, a local map is constructed based on a laser SLAM of a single node, which is the front-end portion of the entire framework; adopting a 16-line laser radar, obtaining rich point cloud under a scene with a short distance, performing interframe matching on point cloud data of continuous time by using a closest point iterative algorithm (ICP) or a Normal Distribution Transformation (NDT), extracting a key frame according to a set threshold value, and forming a vertex of a graph; the point cloud data contained by the vertexes is also a local map generated by the current node.
In one embodiment, data sharing is carried out among nodes through a network, and the data comprises vehicle ID, time stamp, IMU data, GPS data and laser odometry data when the current node point cloud is collected, besides the point cloud data of the current node; the data is packaged and shared together with other nodes within the distance threshold value of the current node.
In one embodiment, the unification of the coordinate system is fused with the data: after other nodes receive the data, whether a conversion relation is known or not is confirmed according to the vehicle ID, and if the conversion relation is confirmed, the conversion relation is directly combined into a local map of the other nodes; otherwise, the conversion relation is calculated, if the conversion relation is successful, the next steps are the same as the known conversion relation, and if the conversion relation is failed, the conversion relation is temporarily stored and is calculated together after more data are received.
In one embodiment, the minimum loop is found in the unified coordinate system and optimized, that is, the global map based on the minimum loop is constructed, which is the back end part of the whole frame; as shown in FIG. 2(a), in the initial situation, the whole forest is formed into connected branches by the number of vehicles, and the initial key frame of each vehicle forms the first node of the current connected branch of the current forest, such as A0；
Along with the operation of the algorithm, more key frames are added into the corresponding connected branches according to the ID of the vehicle, and the forest is generated to be in the shape of a plurality of independent linked lists, such as A0A1A2；
With continuous observation and mapping calculation, more nodes can be added to the connected branches in the forest; as shown in fig. 2(b), the number of nodes representing 3 vehicle connected branches increases with incremental map construction;
when the nodes of different connected branches approach to each other or even coincide with each other, indicating that different vehicles coincide on the track, constructing relative observation constraint between the two nodes, simultaneously merging two originally independent connected branches in the forest, reducing the number of the connected branches in the forest, and triggering global loop detection at the moment;
as new keyframes are added continuously, the generation of new relative observation constraints will trigger a minimum loopback search based on the current node; due to moreThe large loop formed by the key frames introduces more errors, and therefore, the minimum loop searched for is optimized. As shown in FIG. 2(d), the new node C3At the same time at A0A1A2A3B0B1C3C2C1C0B3And B3B2B1C3C2C1C0B3The latter range is smaller, so the latter is chosen for optimization.
In one embodiment, when the above collaborative map building method is adopted, the method specifically includes the following steps:
s1, initializing the interior of a single robot;
s2, when the node is in a sending terminal mode, constructing a local map of the current environment, and simultaneously sending the point cloud map and the pose of the node together with additional information to a data sharing module;
s3, when the node is in a receiving end mode, receiving data sent by other nodes, and calculating and forwarding a conversion relation;
and S4, when the triggering condition is met, searching the minimum loop where the current frame is located by the system, and optimizing.
In one embodiment, in step S1, a laser radar is installed at the top of each node, and point cloud data of the surrounding environment is collected at a frequency of 10HZ and stored in a memory in a specific format; meanwhile, a GPS antenna is also installed on the top of the node, and longitude and latitude coordinates of the current position are obtained at the frequency of 10 HZ; simultaneously, the nodes are also provided with IMUs (inertial measurement units), record the acceleration and the angular velocity and store the acceleration and the angular velocity in a specific format; each node is both a sender and a receiver.
In one embodiment, in step S2, the point cloud data collected in step S1 is used to construct a local map; meanwhile, estimated values of the current pose are obtained by using a laser odometer and an IMU respectively, and are packed together with the point cloud data, the timestamp and the GPS position data and sent to other nodes in the distance range.
In one embodiment, in step S3, when receiving a new data packet, the node first searches its own maintained conversion relationship list, and if the data is data of a known conversion matrix, merges the map data after coordinate conversion into a local map data; if the data is the data of the unknown conversion matrix, the calculation of the conversion relation with the data temporarily stored in the local is tried, if the calculation is successful, the next steps are the same as those of the known conversion matrix, otherwise, the data is temporarily stored in the local, and the conversion matrix is calculated after more data are received.
In one embodiment, in step S4, the conversion relationship between the nodes is basically known along with the operation of the system, at this time, there is a large amount of intersection between the trajectories of the nodes, and when a new key frame is added, the minimum loop where the key frame is located is found by using the algorithm of minimum loop, and the minimum loop is optimized.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (9)
1. A collaborative map building method based on minimum loop detection is characterized in that firstly, a single robot generates a local map based on scanning matching and map optimization; secondly, data are exchanged among a plurality of robots through a network; then, coordinate transformation is carried out by utilizing a coordinate system unified algorithm; and finally, searching a minimum loop in the unified coordinate system and optimizing:
under the initial condition, the whole generated forest forms connected branches by taking the number of vehicles as a unit, and an initial key frame of each vehicle forms a first node of the current connected branch of the current forest;
along with the operation of the algorithm, more key frames are added into the corresponding connected branches according to the ID of the vehicle, and the forest structure is generated to be like a plurality of independent linked lists;
with continuous observation and mapping calculation, more nodes can be added to the connected branches in the forest;
when different connected branch nodes are close to each other or even coincide with each other, indicating that different vehicles coincide on the track, constructing relative observation constraint between the two nodes, simultaneously merging two originally independent connected branches in the forest, reducing the number of the connected branches in the forest, and triggering global loop detection;
as new keyframes are added continuously, the generation of new relative observation constraints will trigger a minimum loopback search based on the current node; since a large loop consisting of more key frames will introduce more errors, the minimum loop searched for is optimized.
2. The cooperative mapping method based on minimum loopback detection as claimed in claim 1, wherein the whole process of SLAM mapping is divided into a front end and a back end, the front end determines the key frame through laser data matching at continuous time and loopback detected in the single vehicle travel track; and the rear end corrects the pose information of all the robots by a general map optimization method, and then performs the same offset change on the point cloud data corresponding to the pose according to the pose offset before and after correction to obtain a final map.
3. The collaborative mapping method based on minimum loopback detection according to claim 2, characterized in that the single robot generates a local map based on scanning matching and map optimization, adopts a 16-line laser radar to obtain rich point clouds in a scene with a close distance, performs inter-frame matching on point cloud data of continuous time by using a closest point iterative algorithm (ICP) or a normal distribution transformation algorithm (NDT), extracts key frames according to a set threshold value, and forms the vertex of the map; the point cloud data contained in the vertexes is also the local map generated by the current node.
4. The collaborative mapping method based on minimum loopback detection according to claim 3, wherein, in the data exchange among a plurality of robots through the network, the data comprises the point cloud data of the current node, and further comprises vehicle ID, time stamp, IMU data, GPS data and laser odometry data when the frame point cloud is collected; the data is packaged and shared together with other nodes within the distance threshold value of the current node.
5. The collaborative mapping method based on minimum loopback detection according to claim 4, characterized in that the coordinate transformation is performed by using a coordinate system unified algorithm, when other nodes receive data, whether a transformation relation is known or not is confirmed according to a vehicle ID, and if the transformation relation is confirmed, the other nodes are directly merged into a local map of the nodes; otherwise, the conversion relation is calculated, if the conversion relation is successful, the next steps are the same as the known conversion relation, and if the conversion relation is failed, the conversion relation is temporarily stored and is calculated together after more data are received.
6. The collaborative mapping method based on minimum loopback detection according to claim 5, specifically comprising the steps of:
s1, initializing the interior of a single robot;
s2, when the node is in a sending end mode, constructing a local map of the current environment, and simultaneously sending the point cloud map and the pose of the node together with additional information to a data sharing module for sharing data among nodes;
s3, when the node is in a receiving end mode, receiving data sent by other nodes, and calculating and forwarding a conversion relation;
and S4, when the triggering condition is met, searching the minimum loop where the current frame is located by the system, and optimizing.
7. The collaborative mapping method according to claim 6, wherein in step S1, a lidar is installed on top of each node, and point cloud data of the surrounding environment is collected at a frequency of 10HZ and stored in a memory in a specific format; meanwhile, a GPS antenna is also arranged at the top of the node, and longitude and latitude coordinates of the current position are obtained at the frequency of 10 HZ; simultaneously, the nodes are also provided with IMUs (inertial measurement units), record the acceleration and the angular velocity and store the acceleration and the angular velocity in a specific format; each node is both a sender and a receiver.
8. The collaborative mapping method based on minimum loopback detection as claimed in claim 7, wherein in step S2, the point cloud data collected in step S1 is used to construct a local map; meanwhile, estimated values of the current pose are obtained by using a laser odometer and an IMU respectively, and are packed together with point cloud data, a timestamp and GPS position data and sent to other nodes in a distance range.
9. The minimum loopback detection-based collaborative mapping method according to claim 7, wherein in step S3, when a node receives a new data packet, it first searches its own maintained transformation relationship list, and if it is data of a known transformation matrix, merges the coordinate-transformed map data into the local map; if the data is the data of the unknown conversion matrix, the calculation of the conversion relation with the data temporarily stored in the local is tried, if the calculation is successful, the next steps are the same as those of the known conversion matrix, otherwise, the data is temporarily stored in the local, and the conversion matrix is calculated after more data are received; in step S4, with the operation of the system, there may be a large number of intersections between the trajectories of the nodes, and when a new key frame is added, a minimum loop algorithm is used to find the minimum loop where the key frame is located, and the minimum loop is optimized.
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