Point Wise or Feature Wise? A Benchmark Comparison of Publicly Available Lidar Odometry Algorithms in Urban Canyons

Robust and precise localization is essential for an autonomous system with navigation requirements. Lidar odometry (LO) has been extensively studied in the past decades to realize this goal. Satisfactory accuracy can be achieved in scenarios with abundant environmental features using existing LO algorithms. Unfortunately, the performance of the LO 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 article evaluates an array of popular and extensively studied LO pipelines using the data sets collected in urban canyons of Hong Kong. We present the results in terms of their positioning accuracy and computational efficiency. The three major factors dominating the performance of LO in urban canyons are concluded, including the ego-vehicle dynamic, moving objects, and the degree of urbanization. According to our experiment results, point wise accomplishes better accuracy in urban canyons while feature-wise achieves cost-efficiency and satisfactory positioning accuracy.