  Odometry is crucial for robot navigation, particularly in situations where
global positioning methods like global positioning system (GPS) are
unavailable. The main goal of odometry is to predict the robot's motion and
accurately determine its current location. Various sensors, such as wheel
encoder, inertial measurement unit (IMU), camera, radar, and Light Detection
and Ranging (LiDAR), are used for odometry in robotics. LiDAR, in particular,
has gained attention for its ability to provide rich three-dimensional (3D)
data and immunity to light variations. This survey aims to examine advancements
in LiDAR odometry thoroughly. We start by exploring LiDAR technology and then
scrutinize LiDAR odometry works, categorizing them based on their sensor
integration approaches. These approaches include methods relying solely on
LiDAR, those combining LiDAR with IMU, strategies involving multiple LiDARs,
and methods fusing LiDAR with other sensor modalities. In conclusion, we
address existing challenges and outline potential future directions in LiDAR
odometry. Additionally, we analyze public datasets and evaluation methods for
LiDAR odometry. To our knowledge, this survey is the first comprehensive
exploration of LiDAR odometry.
