1060 results about "Monocular vision" patented technology

The invention discloses a method for detecting and tracking obstacles in front of a vehicle, aiming to overcome the defect and shortage in detection and tracking of obstacles in front of the vehicle by using a single-type sensor. The method comprises the following steps of: 1. establishing a relation for realizing data conversion between a millimeter wave radar coordinate system and a camera coordinate system; 2. receiving, resolving and processing the millimeter wave radar data, and carrying out preliminary classification on the obstacles; 3. synchronously collecting a camera image and receiving millimeter wave radar data; 4. classifying the obstacles in front of the vehicle: 1) projecting scanning points of the millimeter wave radar data on a camera coordinate system by combining the methods of millimeter wave radar and monocular vision, and establishing a region of interest (ROI) of the obstacles on the image; 2) carrying out preliminary classification on different obstacles in theROI established on the image, and confirming the types of the obstacles by using different image processing algorithms; and 5. tracking the obstacles in front of the vehicle.

The invention discloses a monocular vision / inertia autonomous navigation method for an indoor environment, belonging to the field of vision navigation and inertia navigation. The method comprises the following steps: acquiring feature point information based on local invariant features of images, solving a basis matrix by using an epipolar geometry formed by a parallax generated by camera movements, solving an essential matrix by using calibrated camera internal parameters, acquiring camera position information according to the essential matrix, finally combining the vision navigation information with the inertia navigation information to obtain accurate and reliable navigation information, and carrying out 3D reconstruction on space feature points to obtain an environment information mapto complete the autonomous navigation of a carrier. According to the invention, the autonomous navigation of the carrier in a strange indoor environment is realized with independent of a cooperative target, and the method has the advantages of high reliability and low cost of implementation.

The invention discloses a method based on monocular vision for detecting and roughly positioning the edge of a road, and relates to the field of machine vision and intelligent control. Aiming at a continuous road with different edge characteristics, two road edge detection methods are supplied and suitable for semistructured and nonstructured roads and can be applied to vision navigation and intelligent control over a robot. The invention provides a method for detecting the edge of the road based on colors and a method for detecting the edge of the road based on threshold value partitioning. On the basis of the obtained edge of the road, an image is subjected to inverted perspective projection transformation, so that a front view is transformed into a top view; and according to a linear corresponding relation between a pixel and an actual distance in the image, a perpendicular distance from the current position of the robot to the edge of the road and a course angle of the robot can be calculated. The method is easy to implement, high in anti-interference performance, high in instantaneity and suitable for the semistructured and nonstructured roads.

The invention relates to a dynamic target position and attitude measurement method based on monocular vision at the tail end of a mechanical arm and belongs to the field of vision measurement. The method comprises the following steps: firstly calibrating with a video camera and calibrating with hands and eyes; then shooting two pictures with the video camera, extracting spatial feature points in target areas in the pictures by utilizing a scale-invariant feature extraction method and matching the feature points; resolving a fundamental matrix between the two pictures by utilizing an epipolar geometry constraint method to obtain an essential matrix, and further resolving a rotation transformation matrix and a displacement transformation matrix of the video camera; then performing three-dimensional reconstruction and scale correction on the feature points; and finally constructing a target coordinate system by utilizing the feature points after reconstruction so as to obtain the position and the attitude of a target relative to the video camera. According to the method provided by the invention, the monocular vision is adopted, the calculation process is simplified, the calibration with the hands and the eyes is used, and the elimination of error solutions in the measurement process of the position and the attitude of the video camera can be simplified. The method is suitable for measuring the relative positions and attitudes of stationery targets and low-dynamic targets.

The invention relates to an intelligent mower system and an intelligent mowing method thereof. The intelligent mower system comprises a mower, a camera device arranged on the mower, sensors arranged on the mower, a boundary electronic fence and a charging station. In a mowing area defined by the boundary electronic fence, the intelligent mower finishes recognition of mowing conditions in a current local range, achieves intelligent mowing of a lawn in the area, and conducts shielding, boundary control and automatic return charging by using the camera device arranged on the mower and a mowing motor current detecting device and by integrating monocular vision technology and data of all the sensors. The method enables the intelligent mower to conduct different mowing actions according to conditions of lawn density and distribution and the like in the area, and therefore mowing efficiency is improved, service life of the intelligent mower is prolonged, energy consumption is saved, and independency of the mower is improved.

The invention discloses a monocular vision-combined RGB-D SLAM method. The method comprises the following steps of: respectively carrying out feature extraction, feature matching, motion estimation and optimization on data obtained by a depth sensor by adopting a monocular vision method and an RGB-D depth vision method; establishing a monocular local position map and an RGB-D local position map; carrying out three-dimensional map fusion on the monocular local position map and the RGB-D local position map by a robot in a motion process so as to construct a global map; and carrying out closed loop detection on the fused position maps and carrying out optimization to obtain an optimum global map. The RGB-D SLAM method disclosed by the invention is high in timeliness, strong in adaptability and good in stability, and can be used for solving the problem that RGB-D depth sensors cannot obtain depth information or are insufficient in obtaining of the depth information.

The invention discloses a vehicle driving state evaluating method based on road-switching behavior detection, comprising the steps of: carrying out video acquisition and real-time processing on front road conditions by using a camera so as to achieve purposes of monitoring dangerous driving behaviors of road-switching, long-time line-pressing driving and the like of a vehicle and immediately warn, i.e. arranging a camera facing the front direction inside the vehicle to obtain front road condition information; detecting and tracking a lane line; judging the current position state of the vehicle, if the vehicle is in a line-pressing state, carrying out state tracking to judge whether the vehicle is subjected to lane-switching operation or not; if the vehicle is subjected to the lane-switching operation, judging the behavior danger level of the vehicle according to the lane-switching times within a recent period of time; and if the total time in the line-pressing state within the recent period of time is m percent, making an alarm of the long-time line-pressing driving. For the vehicle active safe driving, by adopting a computer monocular vision and image processing technology, the invention has characteristics of simple system configuration, low cost, good real-time and capability of being applied in various climatic environments in the daytime and at night.

The invention discloses a monocular vision SLAM algorithm based on a semi-direct method and sliding window optimization. The monocular vision SLAM algorithm comprises the steps that 1) color image frames acquired by a monocular color camera are uploaded to a computer through a third-party image acquisition interface; 2) the algorithm initialization process is performed, the camera pose transformation relationship between the two initial frames is established, initial map points are established, the two initial frames act as key frames and the initial map points are inserted in the map and thesliding window; and 3) the map points observed in the previous frame are projected to the current frame, and bundle set optimization computation based on the luminosity error is performed between thetwo frames of images so as to obtain the camera pose transformation between the two frames to track the movement of the camera. The monocular vision SLAM algorithm has faster operation speed, and theused equipment is simple and easy to calibrate so as to have higher practical value and wider application occasions.

The invention discloses a vehicle ranging method based on monocular vision, and belongs to the field of target detection and ranging. The method comprises the steps of installing a monocular camera on a vehicle, measuring the height and the pitching angle of the camera, and determining focal distance parameters of the camera; acquiring a video image in an expressway environment; performing preliminary de-noising and filtration on the video image by adopting Gaussian filtration; performing interest region segmentation preprocessing before detection of a target vehicle on the video image; performing vehicle detection in a segmented video image region, wherein an Haar feature for increasing a wheel feature and a tail feature is adopted in the target vehicle detection, so as to effectively improve the accuracy of target vehicle identification; and measuring the distance of the target vehicle, wherein a ranging method based on pin-hole imaging is adopted within a short-distance range, while a ranging method of data fitting is adopted within a long-distance range, so that the error rate is reduced, and a real-time ranging effect can be achieved. The method has the advantages of high detection speed, high accuracy, strong real-time property and low cost.

The invention discloses a monocular vision vehicle distance measuring method based on road and vehicle information. The method can be adopted by a running vehicle on road to automatically measure the distance to the front vehicle according to the device equipped on the running vehicle, so as to realize the purposes of preventing vehicle rear-end collision and reminding drivers of driving safety. Specifically, a camera facing forward is mounted on the front part of the vehicle for collecting the front road and vehicle images; the lane marking line and the front vehicle are detected, and the vehicle license plate is also positioned; the vehicle distance is obtained through the transformation relationship of the lane width and the vehicle distance of the vehicle in the road image, or the vehicle distance is obtained through the transformation relationship of the size information of the license plate and the vehicle distance in the road image, or through the transformation relationship of the character height of the license plate and the vehicle distance in the road image. The invention can accurately measure the distance to the front vehicle, has high instantaneity, high robustness, low hardware cost, is applicable to various types of vehicles and is easy for popularization.

The invention discloses an image appearance based loop closure detecting method in monocular vision SLAM (simultaneous localization and mapping). The image appearance based loop closure detecting method includes acquiring images of the current scene by a monocular camera carried by a mobile robot during advancing, and extracting characteristics of bag of visual words of the images of the current scene; preprocessing the images by details of measuring similarities of the images according to inner products of image weight vectors and rejecting the current image highly similar to a previous history image; updating posterior probability in a loop closure hypothetical state by a Bayesian filter process to carry out loop closure detection so as to judge whether the current image is subjected to loop closure or not; and verifying loop closure detection results obtained in the previous step by an image reverse retrieval process. Further, in a process of establishing a visual dictionary, the quantity of clustering categories is regulated dynamically according to TSC (tightness and separation criterion) values which serve as an evaluation criterion for clustering results. Compared with the prior art, the loop closure detecting method has the advantages of high instantaneity and detection precision.

The invention relates to a robot positioning method with fusion of visual features and IMU information. The invention puts forward a method of fusion of monocular vision with IMU. Visual front-end pose tracking is performed and the post of a robot is estimated by using feature points; an IMU deviation model, an absolute scale and a gravity acceleration direction are estimated by using pure visualinformation; IMU solution is performed to obtain high-precision pose information and thus an initial reference is provided to optimize the search process, and the initial reference, a state quantity and visual navigation information are used for participating in optimization; and a rear end employs a sliding-window-based tightly coupled nonlinear optimization method to realize pose and map optimization. And the computational complexity is fixed while the speedometer is calculated based on a sliding window method, so that the robustness of the algorithm is enhanced.

The invention discloses a cooperative navigation and localization system and method. The cooperative navigation and localization system comprises a sensor data collecting module, an RGB-D image generation module, a kinematic modeling module, a zero-deflection correction module, a position tracking module, a global optimization module and a map storage module. The cooperative navigation and localization system and method have the advantages that the system is provided with four sensors, namely the GPS, the monocular vision sensor, the inertial navigation element and the laser radar, the advantages of the sensors are combined, the precision and the application range of the sensors are improved, the cooperative navigation and localization system can normally work in various complex environments, and the cooperative navigation and localization system and method have the advantages of being quick in localization speed, high in localization precision, high in robustness, wide in application range and the like.

The invention belongs to the technical field of intelligent traffic and relates to a monocular vision based front vehicle detection and ranging method. The method is implemented by comprising: (1) converting a video image acquired by a vehicle-mounted camera into a gray image; (2) selecting a region of interest according to a camera imaging range to perform histogram equalization; (3) initially detecting an equalized image by using a classifier to obtain a rectangular region containing vehicle information; (4) verifying an initial detection result by using priori knowledge to remove false detections; (5) calculating an intersecting line of a tire and the ground by utilizing a vehicle bottom shadow to obtain an accurate vehicle position; and (6) performing vehicle ranging by taking the vehicle position and a lane line detection result as priori information. According to the method, the problems of illumination sensitivity due to single use of a priori knowledge based method and excessive false detections generated in a machine vision based method are solved; the vehicle can be accurately and effectively detected and the vehicle ranging is accurate; and the real-time requirement of intelligent vehicle control can be met.

The invention discloses an object posture measuring method based on a CAD model and monocular vision. The object posture measuring method based on the CAD model and the monocular vision comprises the following steps: after obtaining a movement relationship between a template shooting camera of an assumed movement and a fixed camera by virtue of movement assumption and iterative calculation of the template shooting camera to obtain an outer parameter of a binocular system consisting of the template shooting camera of the assumed movement and the fixed camera, performing three-dimensional reconstruction on a target to obtain three-dimensional point cloud data of the target, and rectifying a CAD model including object three-dimensional structure information to obtain a corresponding relation between the object posture of the target object under a current world coordinate system and the CAD model, and accurately figuring the posture of the target object. A movable camera fixing carrier is not needed and the CAD model and the three-dimensional vision information of the object are combined, so that the cost is low, the precision is high, and the demand of the actual industrial application can be met.

The invention discloses a simultaneous localization and map construction method of a monocular vision robot based on a retracking strategy. The method comprises the following steps: 1) extracting ORB characteristics of each picture; 2) tracking camera postures by utilizing characteristic matching of the adjacent pictures; 3) carrying out relocation and retracking strategy on pictures which are not tracked; 4) if the relocation is successfully executed, estimating postures of a current camera; stopping the retracking strategy and deleting a temporary variable generated by the retracking strategy; 5) if the retracking strategy is successfully executed, generating a new track; 6) judging the quantity of the generated tracks; if the quantity is more than a threshold value, eliminating an initial track; 7) carrying out closed-loop detection on each key frame and carrying out track fusion after the detection is successful; 8) when a localization system is finished, screening the tracks to obtain one track with the accurate posture. The simultaneous localization and map construction method disclosed by the invention has the advantages that the complete camera track can be located under the conditions of rapidness in movement, shielding, insufficient textures and illumination changes.

The invention discloses a method for measuring and pre-warning a lane departure distance based on monocular vision, belonging to the technical field of computer imaging processing. The method comprises the following steps of: collecting a video image through a monocular video camera installed in the front of an automobile at first, completing the detection of a lane line after processing through an image processing technology, and extracting geometrical information of the lane line; obtaining vertical distances between the automobile and the lane lines at left and right sides by utilizing thethree-dimensional geometry transformation relation of a pinhole imaging principle; and establishing a departure pre-warning decision method according to the vertical distances measured in real time, and providing effective information for an intelligent assistant driving technology. According to the method disclosed by the invention, when the lane line is detected by utilizing Hough transform, a constraint condition is added, a part of virtual lane line is excluded, and the operation speed and the lane line detection accuracy are increased; simultaneously, the lane departure pre-warning can be realized only by utilizing image information; the measurement influence of a vehicle departure angle on the lane departure distance is low; furthermore, the solving operation speed is high owing to the use of the three-dimensional geometry transform method; and the requirements of the intelligent assistant driving technology can be satisfied.

The invention belongs to the technical field of vehicle active safety and relates to a lane line detection method based on monocular vision. The lane line detection method comprises the following steps: acquiring an original image of a road condition in front of a vehicle; intercepting subimages of boundary regions at the two sides, wherein the subimages include the original image; removing the high-frequency noise of the subimages and carrying out grey level histogram equalization and binaryzation; intercepting left and right lane line detection region images; filtering based on the fixed width range of a lane line according to the features of low grey value pavements at the two sides of the lane line; preliminarily determining feature points of the lane line in a sampling line scanning manner and screening; and carrying out lane detection. The lane line detection method has the characteristics of good real time and high reliability.

The invention discloses a mechanical arm tail end tracking and measuring system and method based on a coding azimuth device. The system comprises a mechanical arm, a polyhedron coding point feature measuring target, a mechanical arm tail end executor, a mechanical arm control cabinet, a measuring camera and an upper computer. The system and method has the advantages that monocular vision pose estimation is used to satisfy the requirements of large visual fields, the system is simple in structure and low in manufacturing cost, and influence of light pollution in complex industrial environments on positioning precision is avoided by the polyhedron coding point feature measuring target.

The invention provides a monocular vision system, a portable ball target used by the monocular vision system and a measuring method of the monocular vision system. The ball target is composed of ball bodies, a connecting block and a series of probes with different shapes and lengths. Each probe is composed of an extension bar and a measuring head. The connecting block is used for supporting each ball body and being connected with the probes. 14 even threaded holes are drilled in the surface of the ball-shaped connecting block which is connected with extension bars and balls in a threaded mode. Different connecting bars are designed to adapt to different measuring occasions. Each ball body part is composed of over three non-coplane balls, a single camera can shoot a picture so that intrinsic parameters of the camera can be fast calibrated and ball center positions can be determined so as to obtain a three-dimensional coordinate of a point to be detected, and a feasible approach is provided for obtaining depth information under a monocular measuring system. After the camera moves, external parameters of the camera can be fast calibrated according to a single image of each ball body part of the ball target, the spatial data splicing purpose is achieved, and the measuring range is effectively enlarged and the measuring speed are effectively accelerated.

The invention belongs to the technical field of intelligent transportation and relates to a front vehicle ranging method based on monocular vision. The method includes: adjusting the posture of a vehicular camera, considering the pitch angle variation range due to bumping to adjust the pitch angle of the camera, and using the obtained actual measurement data to segmentally fit the functional relation between an ordinate and an actual longitudinal distance at different pitch angles and the functional relation between the ordinate and an actual transverse distance represented by per horizontal pixel at different pitch angles; and when a vehicle runs, calibrating real-time pitch angle of the camera according to left and right lane lines detected in real time and a three-line calibration method, and then using the linear interpolation method to calculate to obtain the distance between the vehicle and a target vehicle. The front vehicle ranging method based on monocular vision has a real-time dynamic compensation effect on ranging error caused by vehicle bumping and can be used for accurately calculating the distance between the vehicle and the front vehicle.

The invention discloses a depth extraction method based on monocular vision. The method comprises: 1, demarcating the camera of a phone to obtain the inner parameters and image resolution of the camera; 2, establishing a depth extraction model described in the descripton; 3, acquiring the pixel value u, v of the target point through image acquisition of the target to be measured; 4, calculating the depth value of the object to be measured on the image by using the camera internal parameter and the pixel value of the object point obtained in the step 1 and combining with the camera depth extraction model. The depth extraction method based on monocular vision of the invention can be applied to cameras with different parameters such as field angle, focal length, image resolution, etc. to improve ranging accuracy and provide support for object measurement in machine vision and three-dimensional reconstruction of real scene.

The invention discloses a fast monocular vision odometer navigation and positioning method fusing a feature point method and a direct method, which comprises the following steps: S1, starting the vision odometer and obtaining a first frame image I1, converting the image I1 into a gray scale image, extracting ORB feature points, and constructing an initialization key frame; 2, judging whether thatinitialization has been carry out; If it has been initialized, it goes to step S6, otherwise, it goes to step S3; 3, defining a reference frame and a current frame, extracting ORB feature and matchingfeatures; 4, simultaneously calculating a homography matrix H and a base matrix F by a parallel thread, calculating a judgment model score RH, if RH is great than a threshold value, selecting a homography matrix H, otherwise selecting a base matrix F, and estimating a camera motion according to that selected model; 5, obtaining that pose of the camera and the initial 3D point; 6, judging whetherthat feature point have been extracted, if the feature points have not been extracted, the direct method is used for tracking, otherwise, the feature point method is used for tracking; S7, completingthe initial camera pose estimation. The invention can more precisely carry out navigation and positioning.

The invention discloses a monocular vision range finding method based on geometric relation, and the method comprises the following steps: S1, calculating the distance between a section straight line of a target object and a camera lens in a horizontal plane; S2, calculating the distance between a target object and the optical axis of a monocular lens through the triangle similarity principle. According to the projection model of a camera, the method obtains the relation between a road surface coordinate system and an image coordinate system through geometric derivation, is simple in calculation process, is low in calculation complexity, and is lower in requirements for hardware. Meanwhile, the method can reach higher precision, and the error of the method is less than 0.5m. The method can effectively calculate the distance between the front vehicles or other obstacles under the conditions that the hardware configuration condition is very simple and almost no additional load and electric quantity is added, thereby achieving the effective avoidance. The method and device are good in application prospect, such as the technical field of automobile automatic driving or intelligent robot walking.

The invention provides a nonlinear CCD 3D locating device combined with a laser transmitter and a locating method thereof. The locating method comprises the following steps: calibrating nonlinear CCD camera parameters in a plane; adjusting the relative position relation of a stepping motor, the laser transmitter and a camera so as to satisfy that a shaft of the stepping motor is vertical to a horizontal plane, an optical center of the CCD camera is seriously coplanar with emitting points of the line laser transmitter on a vertical plane, and a plane formed by the emitting line of the laser transmitter is vertical to the horizontal plane; and obtaining a 3D coordinate of an objective by virtue of the collected image information and in combination with a triangular structure of the device. In the invention, the laser transmitter is employed to increase auxiliary locating information, which compensates for the defect of inaccurate locating precision resulted from inexact calculation in monocular vision and avoids the problem of difficulty in stereo matching in binocular vision; and the stepping motor is introduced to facilitate angle measurement in trigonometry without error accumulation and improve the locating precision.

The invention discloses a vehicle detection system based on binocular stereo vision and a method thereof. The system comprises binocular parallel cameras, a DSP processor, communication and other modules. The system is vertically installed above a lane in a top-down mode so as to carry out real-time detection on road vehicles. The method comprises the following steps of (1) carrying out main point difference calibration on left and right cameras in advance and acquiring an accurate main point difference parameter; (2) collecting left and right images in real time so as to carry out detection and extraction of a foreground moving object; (3) carrying out stereo matching on an extracted and acquired foreground object area and acquiring a current frame parallax image; (4) carrying out subsequent processing on the acquired parallax image and removing a false detection area, simultaneously acquiring a two-dimensional ground plane mapping image; (5) through calculation, acquiring correlation parameters of a speed, a vehicle height, a vehicle model and the like. In the invention, the binocular cameras are used; a stereo vision principle is used so as to acquire depth information of the object; problems that a monocular vision technology is sensitive to light changes and is easy to be disturbed by shadow are solved.

The invention discloses a method for designing a monocular vision odometer with a light stream method and a feature point matching method integrated. Accurate real-time positioning is of great significance to an autonomous navigation system. Positioning based on the SURF feature point matching method has the advantages of being robust for illumination variations and high in positioning accuracy, and the defects of the SURF feature point matching method are that the processing speed is low and real-time positioning can not be achieved. The light steam tracking method has good real-time performance, and the defect of the light steam tracking method is that positioning accuracy is poor. The method integrates the advantages of the two methods, and the monocular vision odometer integrated with the light stream method and the feature point matching method is designed. Experimental results show that the algorithm after integration can provide accurate real-time positioning output and has robustness under the condition that illumination variations and road surface textures are few.

The invention discloses a railway fastener abnormality detection system based on monocular vision and laser speckles. The railway fastener abnormality detection system is used for detecting whether fasteners are abnormal or not and comprises laser speckle projectors, area array cameras, a wheel encoder, an RFID detector and an industrial personal computer, wherein the laser speckle projectors andthe area array cameras are arranged above a bottom sleeper of a train body, the laser speckle projectors are positioned right above the fasteners and project laser speckles towards fastener areas perpendicularly, the wheel encoder is fixed on a rotating shaft of a wheel and connected with the area array cameras, the RFID detector is fixed below the train body, and the industrial personnel computerpositioned in a carriage is connected with the area array cameras, the wheel encoder and the RFID detector. The railway fastener abnormality detection system has the advantages that with mottled grains on the surfaces of the fasteners increased through the laser speckle projectors, image block matching precision can be improved and the three-dimensional topography of the fasteners is constructedaccurately, so that various abnormalities of different types of fasteners can be detected effectively.

The invention discloses a monocular vision and laser radar fusion-based road travelable region detection method and belongs to the intelligent transportation field. Existing unmanned vehicle road detection methods are mainly based on methods such as monocular vision, stereo vision, laser sensor and multi-sensor fusion methods, have defects of low robustness to illumination, complex three-dimensional matching, laser sparseness, low overall fusion efficiency and the like. Although some supervised methods have achieved better accuracy, the training processes of the supervised methods are complex, and the generalization effects of the supervised methods are poor. According to the monocular vision and laser radar fusion-based road travelable region detection method provided by the present invention, ultra-pixel and point cloud data fusion is adopted; on the basis of features, road regions can be obtained through machine self learning; and the features are fused through the Bayesian frame, so that road information is obtained, and a final region can be obtained. With the method adopted, strong hypothesis information and complex training processes are not required. The monocular vision and laser radar fusion-based road travelable region detection method has the advantages of excellent generalization performance, high robustness, fast speed and high precision, and can be popularized and used more easily in practical application.