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Robust and precise localization is essential for the autonomous system withnavigation requirements. Light detection and ranging (LiDAR) odometry isextensively studied in the past decades to achieve this goal. Satisfactoryaccuracy can be achieved in scenarios with abundant environmental featuresusing existing LiDAR odometry (LO) algorithms. Unfortunately, the performanceof the LiDAR odometry is significantly degraded in urban canyons with numerousdynamic objects and complex environmental structures. Meanwhile, it is stillnot clear from the existing literature which LO algorithms perform well in suchchallenging environments. To fill this gap, this paper evaluates an array ofpopular and extensively studied LO pipelines using the datasets collected inurban canyons of Hong Kong. We present the results in terms of theirpositioning accuracy and computational efficiency. Three major factorsdominating the performance of LO in urban canyons are concluded, including theego-vehicle dynamic, moving objects, and degree of urbanization. According toour experiment results, point-wise achieves better accuracy in urban canyonswhile feature-wise achieves cost-efficiency and satisfactory positioningaccuracy.

Point wise or Feature wise? Benchmark Comparison of Public Available LiDAR Odometry Algorithms in Urban Canyons