Abstract: A method for position and/or deviation correction of a moving object in a simultaneous localization and mapping (SLAM) environment. The method comprises receiving at the moving object point cloud data for an area associated with the moving object from a sensor located at a fixed location. A processor of the moving object performs the steps of: transforming the received point cloud data to a coordinate system of the moving object; matching the transformed point cloud data with point cloud data generated by a sensor at the moving object for the area associated with the moving object; and using the matched point cloud data to determine a corrected position and/or deviation of the moving object within a selected coordinate system.

Abstract: A fixture for unpacking a carton, wherein the fixture includes a fixed portion and a rotating portion. The fixed portion includes a first frame, a first plurality of suction components, and a first driving component arranged on the first frame. The a rotating portion includes a second frame connected to the first frame and capable of rotating or being fixed relative to the first frame under the driving of the first driving component, a third frame arranged on the second frame, a second plurality of suction components arranged on the third frame, and a second driving component connected to the second frame and the third frame.

Abstract: An iterative multi-image camera orientation estimation comprising: capturing an image of a scene before the camera; detecting line segments in the scene; computing a maximum likelihood (ML) camera orientation by maximizing a likelihood objective by rotating the camera's X-Y-Z coordinate system such that it is being optimally aligned with the line segments in at least two of the frontal, the lateral, and the vertical orthogonal directions; estimating a maximum a-posteriori (MAP) camera orientation that maximizes an a-posteriori objective such that the MAP camera orientation is an optimal value in between the priori camera orientation and the ML camera orientation, and is closer to the one with smaller uncertainty; iterating the multi-image camera orientation estimation with the priori camera orientation and its corresponding priori camera orientation uncertainty set to the computed MAP camera orientation and its corresponding uncertainty respectively until the uncertainty is lower than a threshold.

Abstract: Provided are a method, a device and electronic equipment for detecting a stability of a generator set in a power system. The method includes following steps: determining a target matrix by using Liapunov's direct method based on a state space matrix corresponding to power management unit (PMU) data; obtaining a state deviation corresponding to the PMU data and an oscillation curve corresponding to the state deviation in a case of determining the power system being in a stable state according to the target matrix; determining extreme point values corresponding to the oscillation curve at each oscillation; and evaluating a working state of the generator set in the power system according to the extreme point values.

Abstract: A method for estimating camera orientation relative to a ground surface. Line segments are detected from an image captured by a camera. A first virtual cube having three orthogonal vanishing points with a random 3D orientation is superimposed on to the image. The line segments of the image are classified grouped into 3D-directional groups. A second virtual cube is superimposed on to the image with an initial 3D orientation. An optimal 3D orientation of the second virtual cube is computed by iteratively changing the 3D orientation of the second virtual cube and measuring perpendicular distances of the three orthogonal vanishing points to the three line segment groups in each iteration starting with the initial 3D orientation, wherein the optimal 3D orientation of the second virtual cube being one that provides shortest perpendicular distances. Co-variances of the orthogonal vanishing points of the second virtual cube at the optimal orientation are computed.

Abstract: A method for estimating camera orientation relative to a ground surface. Line segments are detected from an image captured by a camera. A first virtual cube having three orthogonal vanishing points with a random 3D orientation is superimposed on to the image. The line segments of the image are classified grouped into 3D-directional groups. A second virtual cube is superimposed on to the image with an initial 3D orientation. An optimal 3D orientation of the second virtual cube is computed by iteratively changing the 3D orientation of the second virtual cube and measuring perpendicular distances of the three orthogonal vanishing points to the three line segment groups in each iteration starting with the initial 3D orientation, wherein the optimal 3D orientation of the second virtual cube being one that provides shortest perpendicular distances. Co-variances of the orthogonal vanishing points of the second virtual cube at the optimal orientation are computed.

Abstract: An iterative multi-image camera orientation estimation comprising: capturing an image of a scene before the camera; detecting line segments in the scene; computing a maximum likelihood (ML) camera orientation by maximizing a likelihood objective by rotating the camera's X-Y-Z coordinate system such that it is being optimally aligned with the line segments in at least two of the frontal, the lateral, and the vertical orthogonal directions; estimating a maximum a-posteriori (MAP) camera orientation that maximizes an a-posteriori objective such that the MAP camera orientation is an optimal value in between the priori camera orientation and the ML camera orientation, and is closer to the one with smaller uncertainty; iterating the multi-image camera orientation estimation with the priori camera orientation and its corresponding priori camera orientation uncertainty set to the computed MAP camera orientation and its corresponding uncertainty respectively until the uncertainty is lower than a threshold.

Abstract: A method and system for tracking a position and orientation of a target object that moves along a two-dimensional plane, a camera to generate video frames of the target object, and a processor configured to correct perspective scaling of a current one of the video frames; estimate a current location and angle of the target object using a motion model or its previous location/angle; cut out a region of interest from the current video frame around the estimated current location; resize the region of interest to shrink its size by a predetermined ratio M (width=width/M, height=height/M) or equivalently reducing the image resolution of the region of interest by the ratio M (resolution=resolution/M); track a new location of the target object by template matching the current region of interest with a previously stored template; and either conclude the tracked target object location and angle are accurate and updating the template if a stopping criterion is reached with indication of successful tracking, or proceed