The invention provides a method for external reference calibration between laser radar and an industrial camera, and belongs to the fields of computer vision and robots. The to-be-solved problem is that a coordinate system needs to be unified and the external reference between the laser radar and the industrial camera needs to be estimated when the laser radar and the industrial camera work synergistically. The method mainly comprises the steps that camera postures are obtained through a camera checkerboard, the postures of the laser radar are obtained through radar data, the external reference between the laser radar and the industrial camera can be estimated according to an external reference relationship between the laser radar and the camera, and the external reference between the laser radar and the industrial camera is further optimized by combining multiple frames of data. The system and method for external reference calibration between the laser radar and the camera can well estimate the external reference between the laser radar and the camera.

System and method for external reference calibration between laser radar and camera

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.

Manufacturing method of multi-feature fusion map facing autonomous vehicle

The invention provides a mapping method and device and a mobile terminal. The method comprises the following steps: obtaining laser frame data collected by a laser sensor; obtaining image frame data collected by a visual sensor; according to the laser frame data, determining a movement track of the laser sensor; based on the movement track of the laser sensor and the image frame data, establishinga visual map; and fusing a laser map established based on the laser frame data with the visual map. According to the technical scheme, the mapping method and device can realize laser and vision combined localization and mapping; and laser SLAM and visual SLAM adopt same motion information, so that the finally generated laser map and visual map are allowed to have the same space information, and robustness is high.

Mapping method and device

The invention discloses a multi-robot co-localization and fusion mapping method under multi-view in an open space. The method comprises the following steps of: completely covering a detection scene byusing multiple observations in the air and the ground, integrating scene image data collected by aerial robots and ground robots, and positioning the robots by visual constraints and restoring three-dimensional scene information; optimizing three-dimensional point cloud map of the mapping and positioning system and six-degree-of-freedom pose of the robots by specific visual features adhered to the robot. The pose optimization and map fusion algorithm based on visual features significantly improve reconstruction and positioning accuracy, revise map scales, so that local maps of each robot canbe shared among multiple heterogeneous robots, the coverage of three-dimensional reconstruction is improved, the reliable environmental information is quickly provided for mission planning, disaster environment search and rescue, and military anti-terrorism.

Multi-robot co-localization and fusion mapping method under multi-view in open space

The invention relates to a three-dimensional wire frame structure method and system fusing a binocular camera and IMU positioning. On the basis of binocular vision, the invention initializes and fusesinertial measurement information by using a divide-and-conquer strategy, implements tracking, positioning and drawing, and can robustly run in indoor and outdoor environments and complex motion conditions. On the basis of accurate positioning, 3D wireframe reconstruction and iterative optimization are carried out based on the posture of the key frame. Linear segments are matched by local featuresand spatial geometric constraints and back-projected into three-dimensional space. Through the angle and distance constraints, the straight line segments are divided into different sets. Based on thegrouping results, the fitting region is determined and the straight line segments are merged. Finally, a 3-D wireframe structure is output. The invention fuses multi-source information to improve thestability and robustness of the system on the traditional vision-based positioning and mapping method. At the same time, line information is added to the key frame to sparsely express the structuralcharacteristics of the three-dimensional environment, which improves the computational efficiency.

A three-dimensional wire frame structure method and system fusing a binocular camera and IMU positioning

The invention provides a high-precision instant locating and mapping method based on multiple sensors. Namely, a surrounding environment is subjected to high-precision real-time three-dimensional construction by using a color camera and a laser radar, and the position and attitude of the camera are measured and calculated in real time. Based on fast visual SLAM, information of the laser radar is introduced and selectively used; a result is corrected in time; and a three-dimensional map is constructed by depending on the corrected result, so that locating and mapping have correct scale and relatively high precision, and an algorithm has relatively low complexity.

High-precision instant locating and mapping method based on multiple sensors

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High-precision instant locating and mapping method based on multiple sensors