  Robust and precise localization is essential for the autonomous system with
navigation requirements. Light detection and ranging (LiDAR) odometry is
extensively studied in the past decades to achieve this goal. Satisfactory
accuracy can be achieved in scenarios with abundant environmental features
using existing LiDAR odometry (LO) algorithms. Unfortunately, the performance
of the LiDAR odometry is significantly degraded in urban canyons with numerous
dynamic objects and complex environmental structures. Meanwhile, it is still
not clear from the existing literature which LO algorithms perform well in such
challenging environments. To fill this gap, this paper evaluates an array of
popular and extensively studied LO pipelines using the datasets collected in
urban canyons of Hong Kong. We present the results in terms of their
positioning accuracy and computational efficiency. Three major factors
dominating the performance of LO in urban canyons are concluded, including the
ego-vehicle dynamic, moving objects, and degree of urbanization. According to
our experiment results, point-wise achieves better accuracy in urban canyons
while feature-wise achieves cost-efficiency and satisfactory positioning
accuracy.
