Iterative closest points (ICP) and normal distributions transform (NDT) are popular 3D point cloud registration algorithms, which have been widely used in mapping and 3D reconstruction. These algorithms provide robust methods for self- localizing an autonomous vehicle by registering real-time 3D-scans to a prior map. However, urban and suburban environments are continually changing, resulting in significant differences that impact registration algorithms. These temporal changes occur over varying time-scales, and include dynamic and ephemeral objects (such as parked cars), seasonal changes (vegetation, snow, dust), and human impacts such as construction. It is critical that a self-localization method be robust to these and other real-environment changes. Furthermore, the computational complexity of the algorithm and its stability and ability to process data in real-time when faced with such adverse conditions are important. In this paper, we present an empirical comparison of NDT and ICP and their performances for autonomous vehicle localization through a set of realistic field tests conducted in the state of Michigan over many months spanning the summer, fall, and winter months. The test sites include the campus of Michigan State University and the University of Michigan's MCity Test Facility, which is a professional purpose-built proving ground for testing autonomous vehicles and technologies. Our tests indicate that NDT possesses a better ability to handle realistic adversity conditions such as static and dynamic environmental changes, as well as being more computationally efficient.
3D Scan Registration Based Localization for Autonomous Vehicles - A Comparison of NDT and ICP under Realistic Conditions
2018-08-01
778792 byte
Conference paper
Electronic Resource
English
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