39results about How to "Accurate pose estimation" patented technology

An object pose estimating and matching system is disclosed for estimating and matching the pose of an object highly accurately by establishing suitable weighting coefficients, against images of an object that has been captured under different conditions of pose, illumination. Pose candidate determining unit determines pose candidates for an object. Comparative image generating unit generates comparative images close to an input image depending on the pose candidates, based on the reference three-dimensional object models. Weighting coefficient converting unit determines a coordinate correspondence between the standard three-dimensional weighting coefficients and the reference three-dimensional object models, using the standard three-dimensional basic points and the reference three-dimensional basic points, and converts the standard three-dimensional weighting coefficients into two-dimensional weighting coefficients depending on the pose candidates. Weighted matching and pose selecting unit calculates weighted distance values or similarity degrees between said input image and the comparative images, using the two-dimensional weighting coefficients, and selects one of the comparative images whose distance value up to the object is the smallest or whose similarity degree with respect to the object is the greatest, thereby to estimate and match the pose of the object.

The invention discloses a position and posture estimation method of a driverless car based on a distance from a point to a surface and cross correlation entropy rectification, which comprises the following steps: firstly, a three-dimensional laser radar is calibrated, and then the acquired three-dimensional laser radar data is subjected to coordinate conversion; point cloud alignment is carried out on the acquired data and the existing map data to obtain a rotary and translation transformation of a rigid body; so that the position and posture of an active moving body are obtained according tothe rotary and translation conversion. According to the invention, by using the three-dimensional laser radar as the data source, the function of estimating the position and posture of the driverlesscar is finished through the steps of coordinate system conversion, data drop sampling, point set rectification and the like. The method can well overcome the influence of weather, light and other environmental factors. Moreover, the error evaluation function based on the distance from the point to the surface and the cross correlation entropy has good resistance to noise and abnormal points, suchas mismatching of the scene and the map description part, dynamic obstacles and the like, therefore, the function of accurate and robust estimation of the position and posture of the driverless car can be achieved.

The present invention provides a pan-tilt attitude estimation method based on extended Kalman filtering. The method adopts quaternion to represent the current attitude of the object, and the system state quantity includes the offset error between the quaternion and the angle increment. The offset error of the angle increment is corrected by the magnetometer, which makes the attitude estimation more accurate, and the accelerometer correction and the magnetometer correction are divided into two stages, so that the accelerometer correction and the magnetometer correction do not interfere with each other, and the attitude estimation accuracy is improved; In the accelerometer correction, the quaternion third vector in the correction amount is set to zero, and in the magnetometer correction, the quaternion first vector and the second vector in the correction amount are set to zero. By adopting the present invention, more accurate attitude estimation information can be obtained.

A top-view-based self-positioning and map construction method for parking lots, using a low-cost fisheye camera surround view system as an information collection system, generating a top view based on the surround view system, and robustly extracting parking spaces near the vehicle The angle line features of the vehicle are tracked and mapped; the angle line map is generated in real time by using the graph matching technology, and the local map optimization and the global map optimization are used to perform real-time high-precision positioning and map construction of the vehicle. The method avoids the scale drift problem of monocular SLAM in principle, and uses the existing vehicle system to complete real-time, high-precision vehicle self-localization and map construction work with low-cost sensors and low-power vehicle processors , to assist the completion of the autonomous parking task. The present disclosure also provides a construction device, a construction system, an automatic driving vehicle and an autonomous parking system.

The invention discloses a mobile platform pose estimation method based on indirect Kalman filtering. The method includes the following steps that difference between a calculation value XI of a strapdown inertial navigation system and observation data XN of a global positioning system serves as observation input of an indirect Kalman filter, a prediction equation is built in the indirect Kalman filter, output of the filter is an estimated value of an error, part of parameter feedback in the output is used for correcting parameters of observation computation of the strapdown inertial navigation system and the global positioning system so as to correct error calculation of the two systems, and the sum of parameters, like coordinates and postures, in XI and the estimated value serves as an estimated value of all values in a final system, wherein please see the estimated value in the description. Compared with the traditional method in which a direct Kalman filtering algorithm is adopted, more accurate pose estimation results can be given, and work efficiency of a robot is effectively improved.