Abstract
Autonomous vehicles can achieve accurate localization and real-time road information perception using sensors such as global navigation satellite systems (GNSSs), light detection and ranging (LiDAR), and inertial measurement units (IMUs). With road information, vehicles can navigate autonomously to a given position without traffic accidents. However, most of the research on autonomous vehicles has paid little attention to road profile information, which is a significant reference for vehicles driving on uneven terrain. Most vehicles experience violent vibrations when driving on uneven terrain, which reduce the accuracy and stability of data obtained by LiDAR and IMUs. Vehicles with an active suspension system, on the other hand, can maintain stability on uneven roads, which further guarantees sensor accuracy. In this paper, we propose a novel method for road profile estimation using LiDAR and vehicles with an active suspension system. In the former, 3D laser scanners, IMU, and GPS were used to obtain accurate pose information and real-time cloud data points, which were added to an elevation map. In the latter, the elevation map was further processed by a Kalman filter algorithm to fuse multiple cloud data points at the same cell of the map. The model predictive control (MPC) method is proposed to control the active suspension system to maintain vehicle stability, thus further reducing drifts of LiDAR and IMU data. The proposed method was carried out in outdoor environments, and the experiment results demonstrated its accuracy and effectiveness.
Highlights
In the last three decades, driverless technology has rapidly developed due to people’s willingness to improve living standards, and the need to improve work efficiency
This paper proposed a novel method for road profile estimation using light detection and ranging (LiDAR), inertial measurement units (IMUs)/global positioning systems (GPSs), and a vehicle with an active suspension system
The second was the description of a model predictive control method, which fused the IMU/GPS and obtained an elevation map to adjust the vehicle suspension system to maintain the stability of the vehicle
Summary
In the last three decades, driverless technology has rapidly developed due to people’s willingness to improve living standards, and the need to improve work efficiency. Most studies on driverless vehicles used multi-sensors such as inertial measurement units (IMUs), light detection and ranging (LiDAR), and global navigation satellite systems (GNSSs) to achieve vehicle perception and localization [1,2,3,4,5]. Gao [6] proposed a robust localization system that made better use of the relative merits of LiDAR and global positioning systems (GPSs) to correct the data obtained by inertial navigation system (INSs) selectively in outdoor and indoor environments. Wan [7] proposed a robust and precise localization method that could maintain centimeter-level navigation accuracy in some challenging environments. The proposed system used a Kalman filter (KF) algorithm to fuse the data obtained by physical sensors such as GPS, INS, and LiDAR to achieve high localization accuracy and robustness. Wolcott [8] proposed a robust localization system
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