Abstract
Robust and lane-level positioning is essential for autonomous vehicles. As an irreplaceable sensor, Light detection and ranging (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 simultaneous localization and mapping (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. The best performance is achieved in the sparse area with normal traffic and the worse performance is obtained in dense urban area with 3D positioning error (summation of horizontal and vertical) gradients of 0.024 m/s and 0.189 m/s, respectively. The analyzed results can be a comprehensive benchmark for evaluating the performance of standalone NDT-based graph SLAM in diverse scenarios which is significant for multi-sensor fusion of autonomous vehicle.
Highlights
The autonomous vehicle [1,2] is well believed to be the revolutionary technology changing people’s lives in many ways
The results show that the performance of the light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) using normal distribution transform (NDT) is strongly related to the traffic condition and degree of urbanization
The analyzed results can be a comprehensive benchmark for evaluating the performance of standalone NDT-based graph SLAM in diverse scenarios which is significant for multi-sensor fusion of autonomous vehicle
Summary
The autonomous vehicle [1,2] is well believed to be the revolutionary technology changing people’s lives in many ways. The main principle of LiDAR-based SLAM is to continuously track the transformation between successive frames of point clouds In this case, the performance of SLAM relies heavily on the accuracy of the mapping-based transformation. This paper estimates the uncertainty of the LiDAR odometry in terms of the degree of matching, number of iterations and time used for NDT optimization This covariance estimation solution is available in the point cloud library (PCL). This paper evaluates the performance of NDT-based graph SLAM in diverse urban scenarios, with different traffic conditions and degree of urbanization. This paper qualitatively analyzes the relationship between the performance of NDT-based graph SLAM and the traffic conditions and degree of urbanization.
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