CN107356252B - Indoor robot positioning method integrating visual odometer and physical odometer - Google Patents
Indoor robot positioning method integrating visual odometer and physical odometer Download PDFInfo
- Publication number
- CN107356252B CN107356252B CN201710408258.9A CN201710408258A CN107356252B CN 107356252 B CN107356252 B CN 107356252B CN 201710408258 A CN201710408258 A CN 201710408258A CN 107356252 B CN107356252 B CN 107356252B
- Authority
- CN
- China
- Prior art keywords
- robot
- odometer
- physical
- pose
- closed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses an indoor robot positioning method fusing a visual odometer and a physical odometer. The invention adds a visual sensor to carry out closed-loop detection on the robot in a known environment, so as to eliminate the accumulated error of the particle filter-based physical odometer in the whole world, change the global error of the odometer into staged accumulation, and construct a closed map on the basis. The method disclosed by the invention effectively solves the problem of error accumulation of the physical odometer after integrating the visual odometer, can enable the robot to carry out self-positioning and accurate repositioning in a known environment, has small added calculation amount, can ensure efficiency and real-time performance, meets the indoor navigation requirement with accuracy, and is an effective method for solving the problem of inaccurate robot positioning in a large environment at the present stage.
Description
Technical Field
The invention relates to a method for automatically positioning precision of an indoor mobile robot, in particular to an indoor robot positioning method integrating a visual odometer and a physical odometer.
Background
In the related research of the intelligent navigation technology of the autonomous mobile robot, the simultaneous localization and mapping (SLAM) technology of the robot in an unknown environment is taken as a key technology, has double values in engineering and academia, and has become a research hotspot in the field in the last two decades. In this trend, the scholars propose various methods for solving the SLAM problem, and also apply various sensors to solve the environmental perception problem in SLAM.
The problem to be solved by SLAM technology is to select an appropriate sensor system to realize real-time robot positioning. In practical applications, sensors with high accuracy in both the range and the azimuth angle based on the laser radar are preferred sensors, and various sensors such as infrared, ultrasonic, IMU, visual sensor, and odometer are also needed to assist positioning to provide positioning accuracy. However, the multi-sensor fusion is always a technical difficulty in the SLAM field, and the SLAM method which can be effectively fused and commercialized at present is basically not available. For the indoor mobile robot, in consideration of the actual use scene and the current development condition, besides the laser radar and the physical odometer, the visual odometer is added to improve the positioning accuracy, and the method is the optimal solution for the SLAM technology of the indoor mobile robot in the real production stage.
The prior art can satisfy the situation that the robot is in an environment with a simple indoor structure and a small area through an improved Monte Carlo particle filtering and positioning method of a physical odometer, however, the physical odometer calculates through displacement increment of two time periods, only local movement is considered, so errors can be continuously superposed and accumulated until drift is too large and cannot be eliminated, and the positioning errors are larger particularly when wheels slip or incline.
Disclosure of Invention
In view of the above, the invention provides an indoor robot positioning method fusing a visual odometer and a physical odometer, which is used for accurately positioning a robot by extracting ORB (object-oriented features) characteristics through collected images to perform image matching, camera pose estimation and closed-loop detection.
An indoor robot positioning method integrating a visual odometer and a physical odometer comprises the following implementation steps:
step 1, acquiring color and depth images by using a camera;
2, extracting ORB characteristics from the obtained two continuous images, calculating a descriptor of each ORB characteristic point, and estimating the pose change of the camera through characteristic matching between adjacent images;
step 3, selecting the image with the most common characteristic points and the best matching in the adjacent frames of images as a key frame in the moving process of the robot, and simultaneously storing the robot track and laser data corresponding to each key frame;
step 4, when the robot moves to a known environment, firstly, searching a feature point matched with the current frame in an offline-trained BoW dictionary, repositioning the robot, then calculating the current pose of the robot through TF, and finally, releasing the pose information of the robot for closed-loop detection repositioning by a ros message mechanism;
step 5, subscribing visual odometer information of closed-loop detection and robot pose optimization of AMCL particle filtering real-time positioning according to an extended Kalman filter to obtain accurate real-time pose of the robot, so as to eliminate the global error accumulated by a physical odometer; the accumulated error of the robot odometer can be eliminated every time of local closed-loop detection, so that the global error is always in stage accumulation;
and 6, finally, when the robot returns to the initial position, global closed loop detection optimizes the whole motion track and the poses of all key frames, and a grid map is constructed by using the stored laser data to complete the whole process of simultaneously positioning and map construction.
Further, the step of estimating the pose change of the camera is: 1) combining the depth image to obtain depth information of the effective characteristic points; 2) matching according to orb features of the feature points and the depth values, and eliminating error point pairs by using a RANSAC algorithm; 3) and solving a rotation matrix R and a translation matrix T between adjacent images, and estimating pose transformation of the camera.
Has the advantages that:
the invention adds a visual sensor to carry out closed-loop detection on the robot in a known environment, so as to eliminate the accumulated error of the particle filter-based physical odometer in the whole world, change the global error of the odometer into staged accumulation, and construct a closed map on the basis. Compared with the traditional SLAM method, the method disclosed by the invention has the advantages that the problem of error accumulation of the physical odometer is effectively solved after the visual odometer is fused, the robot can carry out self-positioning and accurate relocation in the known environment, the increased calculation amount is small, the efficiency and the real-time performance can be ensured, the indoor navigation requirement can be met in precision, and the method is an effective method for solving the problem of inaccurate robot positioning in the large environment at the present stage.
Drawings
FIG. 1 is a flow chart of a fusion positioning method of the present invention;
FIG. 2 is a schematic diagram of the positioning process of the fusion visual odometer and physical odometer of the present invention.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
As shown in fig. 1 and 2, the present invention provides a method of manufacturing a semiconductor device
Step 1, acquiring color and depth images by using a luxurious depth camera Xtion;
step 2, extracting ORB characteristics from the obtained two continuous images, calculating a descriptor of each ORB characteristic point, and estimating the pose change of the camera through characteristic matching between adjacent images: 1) combining the depth image to obtain depth information of the effective characteristic points; 2) matching according to orb features of the feature points and the depth values, and eliminating error point pairs by using a RANSAC algorithm; 3) obtaining a rotation matrix R and a translation matrix T between adjacent images, and estimating pose transformation of the camera;
step 3, selecting the image with the most common characteristic points and the best matching in the adjacent frames of images as a key frame in the moving process of the robot, and simultaneously storing the robot track and laser data corresponding to each key frame;
step 4, when the robot moves to a known environment, firstly, searching a feature point matched with the current frame in an offline-trained BoW dictionary, repositioning the robot, then calculating the current pose of the robot through TF, and finally, releasing the pose information of the robot for closed-loop detection repositioning by a ros message mechanism;
step 5, subscribing visual odometer information of closed-loop detection and robot pose optimization of AMCL particle filtering real-time positioning according to an extended Kalman filter to obtain accurate real-time pose of the robot, so as to eliminate the global error accumulated by a physical odometer; the accumulated error of the robot odometer can be eliminated every time of local closed-loop detection, so that the global error is always in stage accumulation;
and 6, finally, when the robot returns to the initial position, global closed loop detection optimizes the whole motion track and the poses of all key frames, and a grid map is constructed by using the stored laser data to complete the whole process of simultaneously positioning and map construction.
The actual width of the closed map constructed by the method is 86.4m, and the height of the closed map is 38.4 m.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (1)
1. An indoor robot positioning method integrating a visual odometer and a physical odometer is characterized by comprising the following implementation steps:
step 1, acquiring color and depth images by using a camera;
step 2, extracting ORB characteristics from the obtained two continuous images, calculating a descriptor of each ORB characteristic point, estimating the change of the camera pose through characteristic matching between adjacent images, wherein the step of estimating the change of the camera pose is as follows: 1) combining the depth image to obtain depth information of the effective characteristic points; 2) matching according to orb features of the feature points and the depth values, and eliminating error point pairs by using a RANSAC algorithm; 3) obtaining a rotation matrix R and a translation matrix T between adjacent images, and estimating pose transformation of the camera;
step 3, selecting the image with the most common characteristic points and the best matching in the adjacent frames of images as a key frame in the moving process of the robot, and simultaneously storing the robot track and laser data corresponding to each key frame;
step 4, when the robot moves to a known environment, firstly, searching a feature point matched with the current frame in an offline-trained BoW dictionary, repositioning the robot, then calculating the current pose of the robot through TF, and finally, releasing the pose information of the robot for closed-loop detection repositioning by a ros message mechanism;
step 5, subscribing visual odometer information of closed-loop detection and robot pose optimization of AMCL particle filtering real-time positioning according to an extended Kalman filter to obtain accurate real-time pose of the robot, so as to eliminate the global error accumulated by a physical odometer; the accumulated error of the robot odometer can be eliminated every time of local closed-loop detection, so that the global error is always in stage accumulation;
and 6, finally, when the robot returns to the initial position, global closed loop detection optimizes the whole motion track and the poses of all key frames, and a grid map is constructed by using the stored laser data to complete the whole process of simultaneously positioning and map construction.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201710408258.9A CN107356252B (en) | 2017-06-02 | 2017-06-02 | Indoor robot positioning method integrating visual odometer and physical odometer |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201710408258.9A CN107356252B (en) | 2017-06-02 | 2017-06-02 | Indoor robot positioning method integrating visual odometer and physical odometer |
Publications (2)
Publication Number | Publication Date |
---|---|
CN107356252A CN107356252A (en) | 2017-11-17 |
CN107356252B true CN107356252B (en) | 2020-06-16 |
Family
ID=60271649
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201710408258.9A Expired - Fee Related CN107356252B (en) | 2017-06-02 | 2017-06-02 | Indoor robot positioning method integrating visual odometer and physical odometer |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN107356252B (en) |
Families Citing this family (31)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107092264A (en) * | 2017-06-21 | 2017-08-25 | 北京理工大学 | Towards the service robot autonomous navigation and automatic recharging method of bank's hall environment |
CN108958232A (en) * | 2017-12-07 | 2018-12-07 | 炬大科技有限公司 | A kind of mobile sweeping robot SLAM device and algorithm based on deep vision |
CN108247647B (en) * | 2018-01-24 | 2021-06-22 | 速感科技(北京)有限公司 | Cleaning robot |
CN110360999B (en) * | 2018-03-26 | 2021-08-27 | 京东方科技集团股份有限公司 | Indoor positioning method, indoor positioning system, and computer readable medium |
CN108931245B (en) * | 2018-08-02 | 2021-09-07 | 上海思岚科技有限公司 | Local self-positioning method and equipment for mobile robot |
CN111060101B (en) * | 2018-10-16 | 2022-06-28 | 深圳市优必选科技有限公司 | Vision-assisted distance SLAM method and device and robot |
CN111322993B (en) * | 2018-12-13 | 2022-03-04 | 杭州海康机器人技术有限公司 | Visual positioning method and device |
CN109658445A (en) * | 2018-12-14 | 2019-04-19 | 北京旷视科技有限公司 | Network training method, increment build drawing method, localization method, device and equipment |
CN109633664B (en) * | 2018-12-29 | 2023-03-28 | 南京理工大学工程技术研究院有限公司 | Combined positioning method based on RGB-D and laser odometer |
CN109813334B (en) * | 2019-03-14 | 2023-04-07 | 西安工业大学 | Binocular vision-based real-time high-precision vehicle mileage calculation method |
CN110221607A (en) * | 2019-05-22 | 2019-09-10 | 北京德威佳业科技有限公司 | A kind of control system and control method holding formula vehicle access AGV |
CN110196044A (en) * | 2019-05-28 | 2019-09-03 | 广东亿嘉和科技有限公司 | It is a kind of based on GPS closed loop detection Intelligent Mobile Robot build drawing method |
CN110274597B (en) * | 2019-06-13 | 2022-09-16 | 大连理工大学 | Method for solving problem of 'particle binding frame' when indoor robot is started at any point |
CN110333513B (en) * | 2019-07-10 | 2023-01-10 | 国网四川省电力公司电力科学研究院 | Particle filter SLAM method fusing least square method |
CN110472585B (en) * | 2019-08-16 | 2020-08-04 | 中南大学 | VI-S L AM closed-loop detection method based on inertial navigation attitude track information assistance |
CN110648354B (en) * | 2019-09-29 | 2022-02-01 | 电子科技大学 | Slam method in dynamic environment |
CN111076733B (en) * | 2019-12-10 | 2022-06-14 | 亿嘉和科技股份有限公司 | Robot indoor map building method and system based on vision and laser slam |
CN111337943B (en) * | 2020-02-26 | 2022-04-05 | 同济大学 | A mobile robot localization method based on vision-guided laser relocation |
CN111862163B (en) * | 2020-08-03 | 2021-07-23 | 湖北亿咖通科技有限公司 | Trajectory optimization method and device |
CN112450820B (en) * | 2020-11-23 | 2022-01-21 | 深圳市银星智能科技股份有限公司 | Pose optimization method, mobile robot and storage medium |
CN112596064B (en) * | 2020-11-30 | 2024-03-08 | 中科院软件研究所南京软件技术研究院 | Integrated global positioning method for indoor robots integrating laser and vision |
CN114675629A (en) * | 2020-12-10 | 2022-06-28 | 浙江欣奕华智能科技有限公司 | A mobile parameter determination method, device, intelligent device and storage medium |
CN115113215A (en) * | 2021-03-18 | 2022-09-27 | 京东科技信息技术有限公司 | Robot pose determination method, device and equipment |
CN113052906A (en) * | 2021-04-01 | 2021-06-29 | 福州大学 | Indoor robot positioning method based on monocular camera and odometer |
CN113203419B (en) * | 2021-04-25 | 2023-11-10 | 重庆大学 | Indoor inspection robot correction positioning method based on neural network |
CN113238554A (en) * | 2021-05-08 | 2021-08-10 | 武汉科技大学 | Indoor navigation method and system based on SLAM technology integrating laser and vision |
CN113777615B (en) * | 2021-07-19 | 2024-03-29 | 派特纳(上海)机器人科技有限公司 | Positioning method and system of indoor robot and cleaning robot |
CN113808270B (en) * | 2021-09-28 | 2023-07-21 | 中国科学技术大学先进技术研究院 | Network-based unmanned driving test environment map construction method and system |
CN114440892B (en) * | 2022-01-27 | 2023-11-03 | 中国人民解放军军事科学院国防科技创新研究院 | Self-positioning method based on topological map and odometer |
CN115015956B (en) * | 2022-04-12 | 2025-04-29 | 南京邮电大学 | A laser and visual SLAM system for indoor unmanned vehicles |
CN117452429B (en) * | 2023-12-21 | 2024-03-01 | 江苏中科重德智能科技有限公司 | Robot positioning method and system based on multi-line laser radar |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105045263A (en) * | 2015-07-06 | 2015-11-11 | 杭州南江机器人股份有限公司 | Kinect-based robot self-positioning method |
CN105953785A (en) * | 2016-04-15 | 2016-09-21 | 青岛克路德机器人有限公司 | Map representation method for robot indoor autonomous navigation |
CN106052674A (en) * | 2016-05-20 | 2016-10-26 | 青岛克路德机器人有限公司 | Indoor robot SLAM method and system |
CN106780699A (en) * | 2017-01-09 | 2017-05-31 | 东南大学 | A kind of vision SLAM methods aided in based on SINS/GPS and odometer |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9148650B2 (en) * | 2012-09-17 | 2015-09-29 | Nec Laboratories America, Inc. | Real-time monocular visual odometry |
-
2017
- 2017-06-02 CN CN201710408258.9A patent/CN107356252B/en not_active Expired - Fee Related
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105045263A (en) * | 2015-07-06 | 2015-11-11 | 杭州南江机器人股份有限公司 | Kinect-based robot self-positioning method |
CN105953785A (en) * | 2016-04-15 | 2016-09-21 | 青岛克路德机器人有限公司 | Map representation method for robot indoor autonomous navigation |
CN106052674A (en) * | 2016-05-20 | 2016-10-26 | 青岛克路德机器人有限公司 | Indoor robot SLAM method and system |
CN106780699A (en) * | 2017-01-09 | 2017-05-31 | 东南大学 | A kind of vision SLAM methods aided in based on SINS/GPS and odometer |
Non-Patent Citations (1)
Title |
---|
A HIGH EFFICIENT 3D SLAM ALGORITHM BASED ON PCA;施尚杰等;《The 6th Annual IEEE International Conference on Cyber Technology in Automation,Control, and Intelligent Systems(cyber)》;20160330;第109-114页 * |
Also Published As
Publication number | Publication date |
---|---|
CN107356252A (en) | 2017-11-17 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN107356252B (en) | Indoor robot positioning method integrating visual odometer and physical odometer | |
CN106679648B (en) | Visual inertia combination SLAM method based on genetic algorithm | |
CN107180215B (en) | Automatic mapping and high-precision positioning method of parking lot based on storage location and two-dimensional code | |
CN106840148B (en) | Wearable localization and path guidance method based on binocular camera in outdoor working environment | |
CN104236548B (en) | A method for indoor autonomous navigation of micro UAV | |
CN105843223B (en) | A kind of mobile robot three-dimensional based on space bag of words builds figure and barrier-avoiding method | |
CN106017463A (en) | Aircraft positioning method based on positioning and sensing device | |
CN112254729B (en) | Mobile robot positioning method based on multi-sensor fusion | |
CN110726409B (en) | A Map Fusion Method Based on Laser SLAM and Visual SLAM | |
CN103680291B (en) | The method synchronizing location and mapping based on ceiling vision | |
CN109186606B (en) | Robot composition and navigation method based on SLAM and image information | |
WO2021109167A1 (en) | Three-dimensional laser mapping method and system | |
CN107504969A (en) | Four rotor-wing indoor air navigation aids of view-based access control model and inertia combination | |
CN113960614B (en) | A method for constructing elevation maps based on frame-map matching | |
CN106289285A (en) | Map and construction method are scouted by a kind of robot associating scene | |
CN112461210A (en) | Air-ground cooperative building surveying and mapping robot system and surveying and mapping method thereof | |
CN113238554A (en) | Indoor navigation method and system based on SLAM technology integrating laser and vision | |
CN105987697B (en) | The wheeled AGV navigation locating method of Mecanum and system under a kind of quarter bend | |
CN112833892A (en) | Semantic mapping method based on track alignment | |
CN110032965A (en) | Vision positioning method based on remote sensing images | |
CN115218891B (en) | A mobile robot autonomous positioning and navigation method | |
CN116958418A (en) | High-precision three-dimensional mapping method for indoor multi-layer building fused with BEV features | |
Chen et al. | Trajectory optimization of LiDAR SLAM based on local pose graph | |
CN116429116A (en) | Robot positioning method and equipment | |
CN113403942B (en) | Label-assisted bridge detection unmanned aerial vehicle visual navigation method |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CF01 | Termination of patent right due to non-payment of annual fee | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200616 |