CN108052103B - Underground space simultaneous positioning and map construction method of inspection robot based on depth inertia odometer - Google Patents
Underground space simultaneous positioning and map construction method of inspection robot based on depth inertia odometer Download PDFInfo
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Abstract
A method for simultaneously positioning underground space and constructing a map of an inspection robot based on a depth inertia odometer comprises the steps of loosely coupling a depth camera and an inertia measurement unit, acquiring point cloud information through a depth map acquired by the depth camera, and extracting plane features; converting an RGB image collected by a depth camera into a gray level image and fusing plane features, and optimizing by using an iterative closest point algorithm; and loosely coupling the data after the iterative closest point optimization and the inertial measurement unit data, and improving the accuracy of the pose graph by using loop detection to obtain the running track of the inspection robot, a point cloud map and a tree jump table map so as to achieve the effect of simultaneously positioning and map building the inspection robot indoors. The method improves the positioning precision and robustness of the inspection robot in the underground space, and achieves the effects of positioning and map construction of the inspection robot in the underground space. When the inspection robot works in the underground space, the method adopted by the invention has good robustness under the strong rotation environment.
Description
Technical Field
The invention relates to the field of inspection robot simultaneous positioning, in particular to an inspection robot underground space simultaneous positioning and map construction method based on a depth inertia odometer.
Background
With the progress of science and technology, the inspection robot is more and more widely applied in the fields of industry, military and the like. In many cases, the information of the working space of the inspection robot is complicated and unknown. The inspection robot is required to realize functions of indoor autonomous navigation, target identification, automatic obstacle avoidance and the like, and the accurate and simultaneous positioning is particularly important. The traditional simultaneous positioning method mostly takes global satellites such as GPS and Beidou as main positioning, but the common GPS sensor has lower simultaneous positioning precision and cannot meet the requirement of precise simultaneous positioning of the inspection robot.
Although the differential GPS has high positioning accuracy outdoors, the differential GPS is expensive and cannot work in the GPS failure environments such as tunnels, roadways, and basements. Underground spaces such as tunnels, roadways and basements cannot be irradiated by sunlight all the year round, and the illumination is low. In the aspect of visual positioning, the positioning accuracy is lower by generally using a simple camera at present, and the effective positioning effect of the inspection robot cannot be achieved.
Along with the development of computer vision and image processing technologies, a machine vision method conducts navigation through a perception environment and is widely applied to the aspect of real-time positioning of a robot. The principle of the vision simultaneous positioning method is that a camera arranged on a robot body collects images in the motion process in real time, relevant information is extracted from the images, the operation posture and the track of the robot are further judged and calculated, and finally navigation and real-time positioning are achieved. However, the vision sensor is easily affected by light, and the simultaneous positioning is easily lost in the case of strong exposure, low brightness, and the like. In addition, a simple monocular vision sensor has no scale information, cannot sense the depth of the surrounding environment where the robot is located, and features are lost when the robot turns in place, which easily causes the real-time positioning failure of the robot.
The inspection robot uses an inertia measurement unit to perform positioning development earlier, and the inertia positioning is to calculate six-degree-of-freedom simultaneous positioning information of a carrier by using linear acceleration and rotation angular rate measured by the inertia measurement unit. The angular rate of the carrier is measured by a gyroscope, and the angular rate of the carrier is mainly used for calculating a rotation matrix of the robot and providing a conversion relation between a carrier coordinate system and a navigation coordinate system; the linear acceleration of the carrier is measured through an accelerometer, the velocity information and the displacement information of the robot are solved through the obtained acceleration integration, and finally the positioning is completed through converting the six-degree-of-freedom information of the robot into a navigation coordinate system. However, the error accumulation of the simple inertial measurement unit under the repeated path is large, and effective loop detection cannot be performed. In addition, due to the properties of random walk of the inertia measurement unit and the like, a large amount of hysteresis errors are generated when the inspection robot starts and the acceleration changes greatly.
Consumer depth cameras represented by woo xtion and microsoft Kinect can acquire RGB images and depth maps, and are widely applied to the field of indoor robots. However, the field of view of the depth camera is generally narrow, so that the tracking target of the algorithm is easily lost, and meanwhile, a great deal of noise exists in the depth data, and even some data cannot be used. In the conventional method, the visual feature extraction algorithm is often based on the difference of pixels, but in the depth data measured by the depth camera, the points located at the corners are not easily recognized. And under the condition of large rotation, the mobile robot is easy to lose by adopting a single depth camera for positioning.
Simultaneous localization and mapping (SLAM) was originally applied in the field of robotics. Although the method using a single sensor has a small calculation amount, the positioning accuracy is not high and the robustness is not strong. Simultaneous localization and mapping methods using multiple sensor fusion have become the mainstream of development and lack of effective simultaneous localization and mapping of depth camera and inertial measurement unit fusion.
Disclosure of Invention
According to the defects of the prior art, the invention provides the inspection robot underground space simultaneous positioning and map construction method based on the depth inertia odometer, and the method improves the positioning precision and robustness of the inspection robot in the underground space, thereby achieving the effect of simultaneously positioning and map construction of the inspection robot in the underground space. When the inspection robot works in the underground space, the method adopted by the invention has good robustness under the strong rotation environment.
In order to achieve the purpose, the invention adopts the technical scheme that:
a method for simultaneously positioning and mapping underground space of an inspection robot based on a depth inertia odometer comprises the following steps:
loosely coupling a depth camera and an inertia measurement unit, acquiring point cloud information through a depth map acquired by the depth camera, and extracting plane features; converting an RGB image collected by a depth camera into a gray level image and fusing plane features, and optimizing by using an iterative closest point algorithm; and loosely coupling the data after the iterative closest point optimization and the inertial measurement unit data, and improving the accuracy of the pose graph by using loop detection to obtain the running track of the inspection robot, a point cloud map and a tree jump table map so as to achieve the effect of simultaneously positioning and map building the inspection robot indoors.
Preferably, the depth camera collects two adjacent frames as a scene S and a model M, and two sets of matching points are respectively denoted as P ═ Pi1,. n } and Q ═ Q ·i|i＝1,...,n}；
Wherein p isiAnd q isiAll represent three-dimensional space points and can be parameterized
Preferably, the depth camera model is:
wherein (u, v) is a spatial point (x, y, z)TThe corresponding pixel position, d is the depth value, and C is the camera internal reference.
Preferably, the movement of M to S is described by a rotation R and a translation t, solved using an iterative closest point algorithm:
preferably, plane features are extracted from the point cloud obtained from the depth map, describing the plane of the three-dimensional space with four parameters:
p＝(a,b,c,d)＝{x,y,z|ax+by+cz+d＝0}；
and d is equal to 0, each fitted plane is projected on an imaging plane to obtain the imaging position (u, v) of a plane point, and a projection equation is used for solving:
wherein f isx,fy,cx,cyFor the depth camera's internal reference, s is the scaling factor of the depth data.
Preferably, the gray level histogram normalization is performed once on each plane graph to enhance the contrast thereof, and then the feature points are extracted and the depths of the feature points are calculated:
preferably, too many key frames will cause extra computation for the back-end and loop detection, while too few key frames will cause too much motion between key frames and insufficient feature matching, resulting in easy loss. After extracting the plane of the image, calculating that the relative motion between the plane of the image and the previous key frame exceeds a certain threshold, the image is considered as a new key frame.
Preferably, the threshold is calculated by evaluating the translation and euler angle rotation:
where (Δ x, Δ y, Δ z) is the relative translation and (α, β, γ) is the relative euler angle;
w1＝(m,m,m),m∈(0.6,0.7)；w2∈(0.95,1.05)。
the invention has the beneficial effects that:
by the method, the positioning precision and the robustness of the inspection robot during operation in the underground space are improved, and the effects of simultaneous positioning and map construction of the inspection robot in the underground space environment are achieved. When the inspection robot works in the underground space, the method adopted by the invention has good robustness under the strong rotation environment.
Drawings
FIG. 1 is a schematic diagram of a process framework of the present invention;
FIG. 2 is a schematic diagram of a grayscale representation of the RGB image captured by the depth camera of the present invention;
FIG. 3 is a schematic view of a depth camera of the present invention acquiring a depth image;
FIG. 4 is a three-dimensional point cloud map of a construction environment according to the present invention;
FIG. 5 is a three-dimensional tree skip list map of a build environment of the present invention;
fig. 6 is a robot running track of the present invention.
Detailed Description
The invention is further described by the following specific embodiments with reference to the attached drawings.
As shown in fig. 1 to 6, a method for simultaneously positioning and mapping an underground space of an inspection robot based on a depth inertia odometer comprises the following steps: and loose coupling is carried out by utilizing the depth camera and the inertia measurement unit, point cloud information is obtained through a depth map collected by the depth camera, and plane features are extracted. Fusing the RGB image collected by the depth camera and the plane features, and optimizing by using an Iterative Closest Point (ICP) algorithm; and loosely coupling the data after ICP optimization and Inertial Measurement Unit (IMU) data, and improving the accuracy of the pose graph by Loop closure (Loop closure) to obtain the running track of the inspection robot, a point cloud map and a tree jump table map.
The depth camera collects two adjacent frames as a scene S and a model M, and two groups of matching points are respectively recorded as P ═ Pi1,. n } and Q ═ Q ·i1., n }. Wherein p isiAnd q isiAll represent three-dimensional space points and can be parameterized
The depth camera model is:
wherein (u, v) is a spatial point (x, y, z)TThe corresponding pixel position, d is the depth value, and C is the camera internal reference.
The motion from M to S is described by the rotation R and translation t, and is solved by the ICP algorithm:
extracting plane features from a point cloud obtained from a depth map, and describing a plane of a three-dimensional space with four parameters:
p＝(a,b,c,d)＝{x,y,z|ax+by+cz+d＝0}
and d is equal to 0, each fitted plane is projected on an imaging plane to obtain the imaging position (u, v) of a plane point, and a projection equation is used for solving:
wherein f isx,fy,cx,cyFor the depth camera's internal reference, s is the scaling factor of the depth data.
Performing gray level histogram normalization on each plane graph once to enhance the contrast ratio of each plane graph, then extracting feature points and calculating the depths of the feature points:
camera pose in three-dimensional space, expressed in translation and unit quaternion: x ═ x, y, z, qx,qy,qz,qw}；
Set of planes P ═ { P ] extracted from the frameiAnd each plane comprises the plane parameters and the corresponding characteristic points.
Too many key frames will bring extra computation to the back-end and loop detection, while too few key frames will result in too much motion between key frames, and insufficient feature matching, resulting in easy loss. After extracting the plane of the image, calculating that the relative motion between the plane of the image and the previous key frame exceeds a certain threshold, the image is considered as a new key frame. The threshold is calculated by evaluating the translation and euler angle rotation:
here (Δ x, Δ y, Δ z) are relative translations, and (α, β, γ) are relative euler angles.
w1＝(m,m,m),m∈(0.6,0.7)，w2∈(0.95,1.05)。
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not intended to limit the present invention in any way, so that any person skilled in the art can make modifications or changes in the technical content disclosed above, and equivalent embodiments can be obtained. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are still within the protection scope of the present invention, unless they depart from the technical spirit of the present invention.
Claims (6)
1. A method for simultaneously positioning and mapping underground space of an inspection robot based on a depth inertia odometer is characterized by comprising the following steps:
loosely coupling a depth camera and an inertia measurement unit, acquiring point cloud information through a depth map acquired by the depth camera, and extracting plane features;
converting an RGB image collected by a depth camera into a gray level image and fusing plane features, and optimizing by using an iterative closest point algorithm;
loosely coupling the data after the iterative closest point optimization and the inertial measurement unit data, and improving the accuracy of a pose graph by using loop detection to obtain a running track of the inspection robot, a point cloud map and a tree jump table map so as to achieve the effects of simultaneously positioning and mapping the inspection robot in an underground space;
the depth camera collects two adjacent frames as a scene S and a model M, and two groups of matching points are respectively recorded as P ═ Pi1,. n } and Q ═ Q ·i|i＝1,...,n}；
Wherein p isiAnd q isiAll represent three-dimensional space points and can be parameterized
The motion from M to S is described by rotation R and translation t, and the iterative closest point algorithm is used for solving:
2. the inspection robot underground space simultaneous localization and mapping method based on the depth inertia odometer according to claim 1, characterized in that:
the depth camera model is:
wherein (u, v) is a spatial point (x, y, z)TThe corresponding pixel position, d is the depth value, and C is the camera internal reference.
3. The inspection robot underground space simultaneous localization and mapping method based on the depth inertia odometer according to claim 1, characterized in that:
extracting plane features from a point cloud obtained from a depth map, and describing a plane of a three-dimensional space with four parameters:
p＝(a,b,c,d)＝{x,y,z|ax+by+cz+d＝0}；
and d is equal to 0, each fitted plane is projected on an imaging plane to obtain the imaging position (u, v) of a plane point, and a projection equation is used for solving:
d＝z·s
wherein f isx,fy,cx,cyFor the depth camera's internal reference, s is the scaling factor of the depth data.
4. The inspection robot underground space simultaneous localization and mapping method based on the depth inertia odometer according to claim 3, characterized in that:
performing gray level histogram normalization on each plane graph once to enhance the contrast ratio of each plane graph, then extracting feature points and calculating the depths of the feature points:
5. the inspection robot underground space simultaneous localization and mapping method based on the depth inertia odometer according to claim 1, characterized in that:
too many key frames bring extra calculation amount to the back end and loop detection, while too few key frames cause too large movement among the key frames, and the number of feature matching is not enough, thereby causing easy loss; after extracting the plane of the image, calculating that the relative motion between the plane of the image and the previous key frame exceeds a certain threshold, the image is considered as a new key frame.
6. The inspection robot underground space simultaneous localization and mapping method based on the depth inertia odometer according to claim 5, characterized in that:
the threshold is calculated by evaluating the translation and euler angle rotation:
where (Δ x, Δ y, Δ z) is the relative translation and (α, β, γ) is the relative euler angle;
w1＝(m,m,m),m∈(0.6,0.7)；w2∈(0.95,1.05)。
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| US11747825B2 (en) * | 2018-10-12 | 2023-09-05 | Boston Dynamics, Inc. | Autonomous map traversal with waypoint matching |
| CN110322511B (en) * | 2019-06-28 | 2021-03-26 | 华中科技大学 | Semantic SLAM method and system based on object and plane features |
| US11268816B2 (en) | 2019-08-06 | 2022-03-08 | Boston Dynamics, Inc. | Intermediate waypoint generator |
| CN110722559A (en) * | 2019-10-25 | 2020-01-24 | 国网山东省电力公司信息通信公司 | Auxiliary inspection positioning method for intelligent inspection robot |
| CN112258568B (en) * | 2020-10-12 | 2022-07-01 | 武汉中海庭数据技术有限公司 | High-precision map element extraction method and device |
| CN112697131A (en) * | 2020-12-17 | 2021-04-23 | 中国矿业大学 | Underground mobile equipment positioning method and system based on vision and inertial navigation system |
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| CN113486854A (en) * | 2021-07-29 | 2021-10-08 | 北京超维世纪科技有限公司 | Recognition detection algorithm for realizing industrial inspection robot based on depth camera |
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