The accurate estimation of measurements covariance is a fundamental problem\nin sensors fusion algorithms and is crucial for the proper operation of filtering algorithms.\nThis paper provides an innovative solution for this problem and realizes the proposed\nsolution on a 2D indoor navigation system for unmanned ground vehicles (UGVs) that\nfuses measurements from a MEMS-grade gyroscope, speed measurements and a light\ndetection and ranging (LiDAR) sensor. A computationally efficient weighted line\nextraction method is introduced, where the LiDAR intensity measurements are used, such\nthat the random range errors and systematic errors due to surface reflectivity in LiDAR\nmeasurements are considered. The vehicle pose change is obtained from LiDAR line feature\nmatching, and the corresponding pose change covariance is also estimated by a weighted\nleast squares-based technique. The estimated LiDAR-based pose changes are applied as\nperiodic updates to the Inertial Navigation System (INS) in an innovative extended Kalman\nfilter (EKF) design. Besides, the influences of the environment geometry layout and line\nestimation error are discussed. Real experiments in indoor environment are performed to\nevaluate the proposed algorithm. The results showed the great consistency between the\nLiDAR-estimated pose change covariance and the true accuracy. Therefore, this leads to a\nsignificant improvement in the vehicle�s integrated navigation accuracy.
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