391 results about "Simultaneous localization and mapping" patented technology

A method calibrates one or more cameras, wherein the one or more cameras acquire images of a scene for different viewpoints, and wherein the images are non-overlapping, by first constructing a 3D model of the scene using a calibration camera that is independent of the one or more cameras, and a. simultaneous localization and mapping (SLAM) procedure. 2D-to-3D correspondences between each of the images and the 3D model are determined. Then, calibration parameters of each of the one or more cameras are estimated using a 2D-to-3D registration procedure.

Embodiments of the invention relate to a determination method and apparatus for the position and orientation of a mobile robot. The method comprises the following steps: in virtue of simultaneous localization and mapping (SLAM) technology, establishing a global map of an environment where the mobile robot plays a navigation role by using a laser scanner; when the mobile robot is energized, establishing a local map of an environment in the energization moment of the mobile robot by using a laser scanner in virtue of SLAM technology; and subjecting the local map to image matching in the global map so as to obtain the initial position and orientation of the mobile robot in the global map. According to the embodiments of the invention, the absolute position and orientation of the mobile robot in the global map in the moment the mobile robot is energized and started can be obtained, thereby realizing initialization of the position and orientation of the mobile robot; moreover, determined position and orientation of the mobile robot can be further corrected.

The invention discloses a vision-inertia integrated SLAM (Simultaneous Localization and Mapping) method based on a genetic algorithm. The method comprises the following steps: integrating a vision navigation coordinate system with an inertia navigation coordinate system, and calibrating parameters of a binocular camera so as to solve a three-dimensional space coordinate according to an image pixel coordinate; independently calculating by inertia navigation; calculating by vision navigation; integrating vision navigation information with inertia navigation information by using an extended Kalman filter, and building a system filter model; and observing global feature point road signs by using the binocular camera by taking localization locality into account, carrying out data association on map features based on the genetic algorithm, and feeding extended state vectors back to a filter. The method is capable of carrying out long-time and high-accuracy localization; the genetic algorithm is added for improving the data association of the map, so that the simultaneous mapping accuracy is greatly improved.

The invention is related to methods and apparatus that use a visual sensor and dead reckoning sensors to process Simultaneous Localization and Mapping (SLAM). These techniques can be used in robot navigation. Advantageously, such visual techniques can be used to autonomously generate and update a map. Unlike with laser rangefinders, the visual techniques are economically practical in a wide range of applications and can be used in relatively dynamic environments such as environments in which people move. One embodiment further advantageously uses multiple particles to maintain multiple hypotheses with respect to localization and mapping. Further advantageously, one embodiment maintains the particles in a relatively computationally-efficient manner, thereby permitting the SLAM processes to be performed in software using relatively inexpensive microprocessor-based computer systems.

The invention provides a visual ranging-based simultaneous localization and map construction method. The method includes the following steps that: a binocular image is acquired and corrected, so that a distortion-free binocular image can be obtained; feature extraction is performed on the distortion-free binocular image, so that feature point descriptors can be generated; feature point matching relations of the binocular image are established; the horizontal parallax of matching feature points is obtained according to the matching relations, and based on the parameters of a binocular image capture system, real space depth is calculated; the matching results of the feature points of a current frame and feature points in a world map are calculated; feature points which are wrongly matched with each other are removed, so that feature points which are successfully matched with each other can be obtained; a transform matrix of the coordinates of the feature points which are successfully matched with each other under a world coordinate system and the three-dimension coordinates of the feature points which are successfully matched with each other under a current reference coordinate system is calculated, and a pose change estimated value of the binocular image capture system relative to an initial position is obtained according to the transform matrix; and the world map is established and updated. The visual ranging-based simultaneous localization and map construction method of the invention has low computational complexity, centimeter-level positioning accuracy and unbiased characteristics of position estimation.

Embodiments of the invention relate to a determination method and apparatus for poses of a mobile robot. The method comprises the following steps: when the pose of the mobile robot changes, calculating the first pose of the mobile robot in a global map by using an inertial navigation sensor; establishing a local map of a surrounding environment where the mobile robot is located by using a laser scanner in virtue of SLAM technology; subjecting the local map to image matching in the global map so as to obtain the second pose of the mobile robot in the global map and matching credibility, wherein the second pose is an optimal pose of the mobile robot in the global map obtained through image matching; and determining the pose of the mobile robot to be the first pose or the second pose according to matching credibility. According embodiments in the invention, determination of the poses of the mobile robot can be improved, and errors in pose determination can be reduced.

The invention discloses a vSLAM implementation method and system based on point and line feature fusion. The method includes Step S110, obtaining an image frame sequence of a target scene; Step S120,preprocessing each frame of image; Step S130, according to successfully matched point features and lie features, initializing an environmental map; Step S140, tracking based on the environmental map,and estimating the pose of a current frame of image; Step S150, judging whether the current frame of image meets a key frame condition, if yes, executing Step S160, otherwise repeatedly executing StepS110 to Step S150; Step S160, executing a step of a local map thread; Step S170, executing a step of a closed-loop detection thread; and Step S180, executing a step of a global optimization thread toobtain an optimized environmental map and complete simultaneous localization and mapping. The extraction and matching processes of line features are improved, so as to improve the accuracy of data association in the front end, and thus the defects of vSLAM which exist in a complicated low-texture scene can be effectively overcome.

The invention relates to the technical field of simultaneous localization and mapping methods of three-wheel omnidirectional mobile robots integrating multiple vehicle-mounted sensors, and particularly relates to an SLAM device integrating the multiple vehicle-mounted sensors and a control method of the device.The SLAM device integrating the multiple vehicle-mounted sensors comprises the vehicle-mounted sensors, a vehicle-mounted encoder, a vehicle-mounted inertia measurement unit, a vehicle-mounted controller and an upper computer; the vehicle-mounted encoder and the vehicle-mounted inertia measurement unit are connected with the vehicle-mounted controller, and the vehicle-mounted controller is connected with the upper computer.The localization and mapping effects of the robots can be improved, the problem of localization and mapping errors brought by depth value deletion or characteristic point scarcity which are generated when SLAM is conducted only by relying on an RGB-D sensor is solved, and therefore the robustness and accuracy of SLAM are improved.

The invention discloses a visual sense simultaneous localization and mapping method based on dot and line integrated features. The method comprehensively utilizes the line features and the dot features extracted and obtained from a binocular camera image and is able to be used for the positioning and the attitude estimation of a robot in both an external environment and an internal environment. As the dot features and the line features are integrated for use, the system becomes more robust and more accurate. For the parameterization of linear features, the Pluck coordinates are used for straight line calculations, including geometric transformations, 3D reconstructions, and etc. In the back-end optimization, the orthogonal representation of the straight line is used to minimize the number of the parameters of the straight line. The off-line established visual dictionary for the dot and line integrated features is used for closed loop detections; and through the method of adding zone bits, the dot characteristics and the line characteristics are treated differently in the visual dictionary and when an image database is created and image similarity is calculated. The invention can be applied to the construction of a scene image both indoors and outdoors. The constructed map integrates the feature dots and the feature lines, therefore, able to provide even richer information.

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 provides a simultaneous localization and mapping (SLAM) method based on parking locations and two-dimensional codes. A surround view system based on a low-cost front-view monocular camera and composed of a fisheye camera performs robust identification of parking locations, parking location numbers and pre-arranged two-dimensional codes in an environment; the heading and speed of a vehicle are observed by using a low-cost inertial navigation device; and the optimal estimation of the parking locations and marking locations of the two-dimensional codes is realized by an SLAM algorithm, a garage plane distribution map is automatically created, and the real-time and high-precision decimeter localization of an unmanned vehicle is realized by using the mapping result.

The invention discloses a simultaneous localization and mapping (SLAM) method for an unmanned aerial vehicle based on mixed vision odometers and a multi-scale map, and belongs to the technical field of autonomous navigation of unmanned aerial vehicles. According to the SLAM method, an overlooking monocular camera, a foresight binocular camera and an airborne computer are carried on an unmanned aerial vehicle platform; the monocular camera is used for the visual odometer based on a direct method, and binocular camera is used for the visual odometer based on feature point method; the mixed visual odometers conduct information fusion on output of the two visual odometers to construct the local map for positioning, and the real-time posture of the unmanned aerial vehicle is obtained; then theposture is fed back to a flight control system to control the position of the unmanned aerial vehicle; and the airborne computer transmits the real-time posture and collected images to a ground station, the ground station plans the flight path in real time according to the constructed global map and sends waypoint information to the unmanned aerial vehicle, and thus autonomous flight of the unmanned aerial vehicle is achieved. Real-time posture estimation and environmental perception of the unmanned aerial vehicle under the non-GPS environment are achieved, and the intelligent level of the unmanned aerial vehicle is greatly increased.

The invention discloses an SLAM (Simultaneous Localization and Mapping)-based positioning method and system. The method disclosed by the invention comprises the following steps: calculating an initialposition of a camera in a global map, wherein the global map refers to a binocular SLAM global map; extracting a local map from the global map according to the initial position of the camera; matching a current frame and a local map point; based on a matching result of the current frame and the local map point, triggering monocular SLAM positioning, wherein the monocular SLAM positioning is usedfor generating a new map point through a monocular camera and positioning. The SLAM-based positioning method and system can be used for triggering the monocular SLAM positioning to supplement the mappoint when the positioning quality is poor, the positioning accuracy is increased and the positioning efficiency is improved; under the condition that the positioning quality is good and positioning is lost, basic map positioning is carried out.

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 an automatic parking system and method based on stereoscopic vision localization and mapping. The automatic parking system comprises an image acquisition module, an SLAM (Simultaneous Localization and Mapping) module and a route planning module, wherein the image acquisition module corrects an image acquired by a binocular camera calibrated in advance and obtains a disparity image, so that distance information between a vehicle and surrounding objects is obtained; scene-flow motion detection is realized through the matching of the image of a front frame and the image of a rear frame, so that dynamic points in the image are obtained; the SLAM module integrates acquired color images and obtained three-dimensional information, so that the position of the vehicle is located, and a map is established; the route planning module plans a route to find a parking position according to the established map; after the position of the vehicle and a target position are obtained, whether the space of the parking position is enough or not is judged, if parking conditions are met, an optimal parking track is planned to control the vehicle, so that automatic parking is realized. The automatic parking system is simple in operation process, easy to implement, low in cost and wide in application range.

The invention discloses a manufacturing method of a multi-feature fusion map facing an autonomous vehicle. The method comprises the following steps: collecting data of each sensor by utilizing an on-board laser radar device; generating a three-dimensional point cloud map by utilizing IMU(Inertial Measurement Unit) data and laser measurement data; generating a visual feature map by utilizing the IMU data and camera data; preprocessing GPS data, and converting a geodetic coordinates into a spatial rectangular coordinates; performing global optimization fusion by utilizing a continuous time SLAM(Simultaneous Localization and Mapping) algorithm; and generating the multi-feature fusion map. According to the manufacturing method of the multi-feature fusion map, various sensor data of a laser radar, an IMU, a camera and a GPS, etc., are integrated, so that the stability and the accuracy are greatly improved; the data are processed by fusing a visual SLAM algorithm and a laser SLAM algorithm,thereby obtaining a better composition effect than a single visual SLAM algorithm or a single laser SLAM algorithm; and the method is simple, practical and low in cost, and the map produced is accurate and rich in attributes, thereby improving the safety of the driving process of the autonomous vehicle.

A mobile electron device is used for positioning and map building in an unknown environment. The mobile electron device comprises: a first sensor, a second sensor, a feature point extraction unit, a matching unit, and a positioning and map building unit, wherein the first sensor is used for acquiring a first image of the current scene with the current position and orientation; the second sensor is used for acquiring a second image of the current scene with the current position and orientation; the feature point extraction unit is used for extracting feature points of the first image and feature points of the second image by utilization of a feature extraction algorithm; the matching unit is used for matching the feature points of the current scene and feature points of a previous scene, and acquiring a transformation matrix from the previous scene to the current scene; and the positioning and map building unit is used for, based on the transformation matrix and the matched feature points, determining changes on position and orientation of the mobile electron device relative to the previous position and orientation so as to perform positioning, and is also used for combining the images of the current scene with a known map.

The invention discloses a path planning method, a device and a mobile device. The method comprises: collecting environment information within the perspective by a sensor of the mobile device, using the simultaneous localization and mapping(SLAM) algorithm to process the environment information to obtain a grid map; dividing the grid map into pixel blocks, taking the area composed of the pixel blocks having no obstacles as a search area of the path planning, and obtaining a processed grid map; determining a reference point based on pixel points in the search area, arranging topological points on the processed grid map according to the determined reference point, and constructing a topological map; and calculating the optimal path between a starting point and a target point using a predetermined algorithm based on the constructed topological map. The embodiments of the invention improve the path planning efficiency and save storage resources.

The invention discloses a multisensor fusion-based unmanned aerial vehicle SLAM (simultaneous localization and mapping) navigation method and a multisensor fusion-based unmanned aerial vehicle SLAM navigation system. The multisensor fusion-based unmanned aerial vehicle SLAM navigation method comprises the following steps: acquiring image information of the surrounding environment of an unmanned aerial vehicle in real time, and acquiring pose information of the unmanned aerial vehicle according to the image information; acquiring depth information of the unmanned aerial vehicle and an obstaclein real time, fusing the pose information and the depth information to construct an obstacle depth map, and acquiring global position information of the unmanned aerial vehicle according to the obstacle depth map; according to the global pose information and the obstacle depth map, generating a flight path of the unmanned aerial vehicle by an online dynamic path planning method, and controlling autonomous obstacle-avoiding flight of the unmanned aerial vehicle according to the flight path. Through the multisensor fusion-based unmanned aerial vehicle SLAM navigation method and the multisensor fusion-based unmanned aerial vehicle SLAM navigation system, real-time localization and mapping of the unmanned aerial vehicle in a complex environment can be achieved; compared with the conventional unmanned aerial vehicle navigation technology, the navigation technology adopted by the invention has the advantages as follows: real-time localization and mapping and autonomous navigation are achieved, and the intelligence degree and the navigation accuracy of the unmanned aerial vehicle are improved.

The invention discloses a method for simultaneous localization and mapping of a mobile robot based on an improved particle filter. The method comprises the following steps: initializing an initial-moment pose of a robot; obtaining a t-moment prior probability density function according to the pose information at a t-1 moment, and generating a sampling particle set p; initializing the weights of particles; selecting an importance probability density function, generating a new sampling particle set q, calculating the weights of particles, updating the weights of the particles, and normalizing the weights; calculating the weighted sum of random sample particles at current moment t to express posterior probability density, and obtaining the moving pose and environmental map information; judging whether a new observed value is input; if so, returning; otherwise, ending the cycle; before returning, judging whether resampling is needed or not. According to the difference of the system state, a dynamic threshold is set for judgment, and a genetic algorithm is combined. According to the method disclosed by the invention, influence of a problem of particle degeneration on SLAM is reduced, and the calculated amount of the SLAM problem is reduced.