  Currently, the improvement of LiDAR poses estimation accuracy is an urgent
need for mobile robots. Research indicates that diverse LiDAR points have
different influences on the accuracy of pose estimation. This study aimed to
select a good point set to enhance accuracy. Accordingly, the sensitivity and
uncertainty of LiDAR point residuals were formulated as a fundamental basis for
derivation and analysis. High-sensitivity and low -uncertainty point residual
terms are preferred to achieve higher pose estimation accuracy. The proposed
selection method has been theoretically proven to be capable of achieving a
global statistical optimum. It was tested on artificial data and compared with
the KITTI benchmark. It was also implemented in LiDAR odometry (LO) and LiDAR
inertial odometry (LIO), both indoors and outdoors. The experiments revealed
that utilizing selected LiDAR point residuals simultaneously enhances
optimization accuracy, decreases residual terms, and guarantees real-time
performance.
