Matrices, Intel MKL provides the –DMKL_DIRECT_CALL compiler flag to guarantee that the fastest code pathĮigen is an open-source, easy-to-use C++ library that provides operations ranging from matrix math to To eliminate overhead from additional error checking for DGEMM on small Important for our purposes, it provides a highly-tuned DGEMM function for It is compatible across many different compilers, languages, operating systems, Intel MKL provides highly optimized, threaded, and vectorized math functions that maximize performance on P = (I – K * H) * P’ Updated covariance estimate K = P’ * HT * S-1 Near-optimal Kalman gain S = H * P’ * HT+ R Innovation (or residual) covariance Y = z – H’ * x Innovation or measurement residual P’ = F * P *FT+ Q Predicted covariance estimate Table 1 shows the vectors and matrices the EKF uses to represent different states and estimates 4, 5 Measurements that are at a lower resolution than those from LIDAR 6. ![]() Measurements are typically in polar coordinate form and can be converted to Cartesian coordinates, forming LIDAR measurements that localize an object are defined in Cartesian coordinate form-(px,py). The coupled estimate of the vehicle's position fromįusing both RADAR and LIDAR has higher accuracy than using noisy LIDAR and RADAR by themselves. Noisy LIDAR and RADAR sensor measurements. In particular, this algorithm predicts the position of the vehicle (px,py) and its velocity (vx,vy) from Higher weights imply lower uncertainty 6. Updates the predicted estimates based on one important factor-the weighted average of the predictedĮstimate and the estimate from the current measurement.
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