33 results about "Unscented transform" patented technology

The invention relates to a self-contained navigation method for measurement of an earth ultraviolet sensor, which comprises the following steps of: initializing: utilizing satellite injection information to supply a state initial value X~1 and a covariance initial value P~1 outlier of a satellite at a starting moment, accumulating geocentric vector information and earth apparent radius information obtained by the ultraviolet sensor for a specified period of time, and then utilizing an outlier eliminating algorithm to eliminate the outlier in the information; batch processing: performing a least square method on the geocentric vector information and the earth apparent radius information after the outlier is eliminated, and calculating a state estimation X`k and a covariance estimation Pk of the satellite at an ending moment of observation; unscented transformation: utilizing the unscented transformation to realize the time updating of the state and the covariance, obtaining a state forecast X~(k+1) and covariance forecast P~(k+1) of the satellite at the next ending moment of observation; and recursion: continuously accumulating the geocentric vector information and the earth apparent radius information for a period of time, and repeating the steps and performing continuous recursion and calculation, thereby continuously obtaining the estimation of the state of the satellite. According to the method, the precision and the stability of a navigation system are increased.

The invention discloses a robot pose positioning method and system. It relates to the field of robot positioning, wherein, the method collects IMU odometer data as a local reference system, obtains a previous moment pose state vector and a previous moment covariance matrix according to the local reference frame, and samples the last moment pose state vector, And perform unscented transformation on the sampling points, use the system model to predict the unscented transformed pose state vector and the covariance matrix at the previous moment to obtain the predicted value at the current moment, and combine the actual measured value to predict the predicted value at the current moment The filtering process obtains the relative pose measurement value at the current moment. After the filtering, the estimated value of the global pose at the current moment is obtained through coordinate transformation according to the measured value of the relative pose at the current moment, and the robot pose is positioned according to the estimated value of the global pose at the current moment. Using the unscented Kalman filter algorithm combined with the IMU odometer data and the actual measurement value collected by the GPS satellite or the vision system to obtain the global pose estimation value, which is robust to complex environments and improves the positioning accuracy.