  In unstructured outdoor environments, robotics requires accurate and
efficient odometry with low computational time. Existing low-bias LiDAR
odometry methods are often computationally expensive. To address this problem,
we present a lightweight LiDAR odometry method that converts unorganized point
cloud data into a spherical range image (SRI) and filters out surface, edge,
and ground features in the image plane. This substantially reduces computation
time and the required features for odometry estimation in LOAM-based
algorithms. Our odometry estimation method does not rely on global maps or loop
closure algorithms, which further reduces computational costs. Experimental
results generate a translation and rotation error of 0.86\% and 0.0036{\deg}/m
on the KITTI dataset with an average runtime of 78ms. In addition, we tested
the method with our data, obtaining an average closed-loop error of 0.8m and a
runtime of 27ms over eight loops covering 3.5Km.
