  Vehicle odometry is an essential component of an automated driving system as
it computes the vehicle's position and orientation. The odometry module has a
higher demand and impact in urban areas where the global navigation satellite
system (GNSS) signal is weak and noisy. Traditional visual odometry methods
suffer from the diverse illumination status and get disparities during pose
estimation, which results in significant errors as the error accumulates.
Odometry using light detection and ranging (LiDAR) devices has attracted
increasing research interest as LiDAR devices are robust to illumination
variations. In this survey, we examine the existing LiDAR odometry methods and
summarize the pipeline and delineate the several intermediate steps.
Additionally, the existing LiDAR odometry methods are categorized by their
correspondence type, and their advantages, disadvantages, and correlations are
analyzed across-category and within-category in each step. Finally, we compare
the accuracy and the running speed among these methodologies evaluated over the
KITTI odometry dataset and outline promising future research directions.
