  Robust and lane-level positioning is essential for autonomous vehicles. As an
irreplaceable sensor, LiDAR can provide continuous and high-frequency pose
estimation by means of mapping, on condition that enough environment features
are available. The error of mapping can accumulate over time. Therefore, LiDAR
is usually integrated with other sensors. In diverse urban scenarios, the
environment feature availability relies heavily on the traffic (moving and
static objects) and the degree of urbanization. Common LiDAR-based SLAM
demonstrations tend to be studied in light traffic and less urbanized area.
However, its performance can be severely challenged in deep urbanized cities,
such as Hong Kong, Tokyo, and New York with dense traffic and tall buildings.
This paper proposes to analyze the performance of standalone NDT-based graph
SLAM and its reliability estimation in diverse urban scenarios to further
evaluate the relationship between the performance of LiDAR-based SLAM and
scenario conditions. The normal distribution transform (NDT) is employed to
calculate the transformation between frames of point clouds. Then, the LiDAR
odometry is performed based on the calculated continuous transformation. The
state-of-the-art graph-based optimization is used to integrate the LiDAR
odometry measurements to implement optimization. The 3D building models are
generated and the definition of the degree of urbanization based on Skyplot is
proposed. Experiments are implemented in different scenarios with different
degrees of urbanization and traffic conditions. The results show that the
performance of the LiDAR-based SLAM using NDT is strongly related to the
traffic condition and degree of urbanization.
