Abstract: A device for detecting a rotary angle used for an excavator. The device can include: a synchronous belt, arranged around a rotating shaft of a slewing mechanism of the excavator, a tooth-shaped surface of the synchronous belt being away from a surface of the rotating shaft; a transmission part, engaged with the tooth-shaped surface of the synchronous belt and arranged on a supporting base; an angle detection part, in transmission connection to the transmission part; and the supporting base, connected to a chassis of the excavator.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for controlling an excavator to excavate. The method includes: acquiring a two-dimensional image of a material pile; generating a three-dimensional model of the material pile based on the two-dimensional image; analyzing the three-dimensional model to determine a target excavating point and a target excavating trajectory of the material pile; and controlling an excavator to excavate a material at the target excavating point along the target excavating trajectory.

Abstract: Embodiments of the present disclosure relate to a three-dimensional reconstruction method and apparatus for a material pile, an electronic device, and a computer-readable medium. The method may include: acquiring, in response to an instruction for controlling an excavator body of an excavator to rotate to transport materials being detected, a sequence of depth images of an excavated material pile collected by a binocular camera provided on a side of the excavator; and performing three-dimensional reconstruction based on the sequence of depth images of the material pile, to generate a three-dimensional model of the material pile.

Abstract: Embodiments of the present disclosure disclose an apparatus and method for fixing a sensing system to an unmanned construction machinery. The fixing apparatus includes an internal fixing module and an external fixing module, in which, the internal fixing module is configured to fix a sensing device in a sensing system of an unmanned construction machinery to the external fixing module in a predetermined fixing manner; and the external fixing module is configured to control the sensing device in the sensing system to perform an environment sensing on a target area of the unmanned construction machinery in a predetermined control manner.

Abstract: A method and an apparatus for controlling an excavator are provided according to the embodiments.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for controlling vehicle driving. The method includes: generating a global path of a driving site of a vehicle; executing following controlling: generating a local path of a site in front of a current position of the vehicle based on the global path, the local path following a direction of the global path, controlling the vehicle to drive along the local path until reaching an end point of the local path, determining whether the vehicle reaches an end point of the global path, and terminating the controlling if the vehicle reaches the endpoint of the global path; and continuing, in response to determining the vehicle failing to reach the end point of the global path, executing the controlling.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for outputting information. The method includes: acquiring a target excavating trajectory, the target excavating trajectory including at least two sub-trajectories; determining trajectory parameters of the at least two sub-trajectories; determining, based on the trajectory parameters, positions of a plurality of control points; and outputting the positions of the plurality of control points.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for controlling vehicle driving. The method includes: generating a global path of a driving site of a vehicle; executing following controlling: generating a local path of a site in front of a current position of the vehicle based on the global path, the local path following a direction of the global path, controlling the vehicle to drive along the local path until reaching an end point of the local path, determining whether the vehicle reaches an end point of the global path, and terminating the controlling if the vehicle reaches the endpoint of the global path; and continuing, in response to determining the vehicle failing to reach the end point of the global path, executing the controlling.

Abstract: Presented are systems and methods for improving speed and quality of real-time per-pixel depth estimation of scene layouts from a single image by using an end-to-end Convolutional Spatial Propagation Network (CSPN). An efficient linear propagation model performs propagation using a recurrent convolutional operation. The affinity among neighboring pixels may be learned through a deep convolutional neural network (CNN). The CSPN may be applied to two depth estimation tasks, given a single image: (1) to refine the depth output of existing methods, and (2) to convert sparse depth samples to a dense depth map, e.g., by embedding the depth samples within the propagation procedure. The conversion ensures that the sparse input depth values are preserved in the final depth map and runs in real-time and is, thus, well suited for robotics and autonomous driving applications, where sparse but accurate depth measurements, e.g., from LiDAR, can be fused with image data.

Abstract: Embodiments of the present disclosure disclose a control system and a control method of an unmanned engineering machinery. The system includes at least a slave computer, a master computer and an execution device. The slave computer is configured to receive a current sensing value fed back by a sensing device in the unmanned engineering machinery, and to send the current sensing value to the master computer. The master computer is configured to generate a control instruction according to the current sensing value and a predetermined target sensing value of the sensing device, and to send the control instruction to the execution device through the slave computer.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for generating a driving path. The method includes acquiring a two-dimensional image of a driving site obtained by a camera provided on an unmanned aerial vehicle through aerial photography, and a two-dimensional image of a site in front of a vehicle photographed by a camera provided on the vehicle; generating a global map based on the two-dimensional image of the driving site, and generating a local map based on the two-dimensional image of the site in front of the vehicle; and performing path planning based on the global map and the local map, to generate a global path and a local path, the local path following a direction of the global path.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for controlling equipment. The method includes: acquiring an electrical signal collected by a displacement sensor; determining a displacement of a movable rod of a hydraulic cylinder relative to a cylinder tube of the hydraulic cylinder according to the electrical signal; determining, according to the displacement, a rotating angle of a mechanical arm connected to the movable rod relative to a mechanical arm connected to the cylinder tube; and controlling equipment according to the rotating angle.

Abstract: Embodiments of the present disclosure provide a method, apparatus for determining a rotation angle of an engineering mechanical device, an electronic device and a computer readable medium. The method may include: acquiring a depth image sequence acquired by a binocular camera disposed at a rotating portion of the engineering mechanical device during rotation of the rotating portion of the engineering mechanical device; converting the depth image sequence into a three-dimensional point cloud sequence; and determining a matching point between three-dimensional point cloud frames in the three-dimensional point cloud sequence, determining a rotation angle of the binocular camera during the rotation of the rotating portion of the engineering mechanical device based on the matching point between the three-dimensional point cloud frames as the rotation angle of the engineering mechanical device.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for selecting a target excavating point. The method includes: acquiring a height map of a material pile; discretizing the height map to obtain an excavating point set; acquiring an excavating trajectory set of an excavating point in the excavating point set; and selecting a target excavating point based on the excavating trajectory set of the excavating point in the excavating point set.

Abstract: Presented are systems and methods for improving speed and quality of real-time per-pixel depth estimation of scene layouts from a single image by using an end-to-end Convolutional Spatial Propagation Network (CSPN). An efficient linear propagation model performs propagation using a recurrent convolutional operation. The affinity among neighboring pixels may be learned through a deep convolutional neural network (CNN). The CSPN may be applied to two depth estimation tasks, given a single image: (1) to refine the depth output of existing methods, and (2) to convert sparse depth samples to a dense depth map, e.g., by embedding the depth samples within the propagation procedure. The conversion ensures that the sparse input depth values are preserved in the final depth map and runs in real-time and is, thus, well suited for robotics and autonomous driving applications, where sparse but accurate depth measurements, e.g., from LiDAR, can be fused with image data.

Abstract: Embodiments of the present disclosure provide a method and apparatus for determining position and orientation of a bucket of an excavator, an electronic device and a computer readable medium. The method may include: acquiring an image of a bucket of an excavator collected by a camera provided on an excavator body, the image of the bucket including a preset marker provided on the bucket; determining position and orientation information of the camera relative to the bucket on the basis of the image of the bucket and pre-acquired three-dimensional feature information of the preset marker; and converting the position and orientation information of the camera relative to the bucket into position and orientation information of the bucket relative to the excavator body.

Abstract: Presented are systems and methods for improving speed and quality of real-time per-pixel depth estimation of scene layouts from a single image by using a 3D end-to-end Convolutional Spatial Propagation Network (CSPN). An efficient linear propagation model performs propagation using a recurrent convolutional operation. The affinity among neighboring pixels may be learned through a deep convolutional neural network (CNN). The CSPN may be applied to two depth estimation tasks, given a single image: (1) to refine the depth output of existing methods, and (2) to convert sparse depth samples to a dense depth map, e.g., by embedding the depth samples within the propagation procedure. For stereo depth estimation, the 3D CPSN is applied to stereo matching by adding a diffusion dimension over discrete disparity space and feature scale space. This aids the recovered stereo depth to generate more details and to avoid error matching from noisy appearance caused by sunlight, shadow, and similar effects.

Abstract: Embodiments of the present disclosure provide a method, apparatus for determining a rotation angle of an engineering mechanical device, an electronic device and a computer readable medium. The method may include: acquiring a depth image sequence acquired by a binocular camera disposed at a rotating portion of the engineering mechanical device during rotation of the rotating portion of the engineering mechanical device; converting the depth image sequence into a three-dimensional point cloud sequence; and determining a matching point between three-dimensional point cloud frames in the three-dimensional point cloud sequence, determining a rotation angle of the binocular camera during the rotation of the rotating portion of the engineering mechanical device based on the matching point between the three-dimensional point cloud frames as the rotation angle of the engineering mechanical device.

Abstract: Embodiments of the present disclosure provide a method and apparatus for determining position and orientation of a bucket of an excavator, an electronic device and a computer readable medium. The method may include: acquiring an image of a bucket of an excavator collected by a camera provided on an excavator body, the image of the bucket including a preset marker provided on the bucket; determining position and orientation information of the camera relative to the bucket on the basis of the image of the bucket and pre-acquired three-dimensional feature information of the preset marker; and converting the position and orientation information of the camera relative to the bucket into position and orientation information of the bucket relative to the excavator body.

Abstract: Presented are systems and methods for improving speed and quality of real-time per-pixel depth estimation of scene layouts from a single image by using a 3D end-to-end Convolutional Spatial Propagation Network (CSPN). An efficient linear propagation model performs propagation using a recurrent convolutional operation. The affinity among neighboring pixels may be learned through a deep convolutional neural network (CNN). The CSPN may be applied to two depth estimation tasks, given a single image: (1) to refine the depth output of existing methods, and (2) to convert sparse depth samples to a dense depth map, e.g., by embedding the depth samples within the propagation procedure. For stereo depth estimation, the 3D CPSN is applied to stereo matching by adding a diffusion dimension over discrete disparity space and feature scale space. This aids the recovered stereo depth to generate more details and to avoid error matching from noisy appearance caused by sunlight, shadow, and similar effects.