Abstract
For autonomous driving, it is important to obtain precise and high-frequency localization information. This paper proposes a novel method in which the Inertial Measurement Unit (IMU), wheel encoder, and lidar odometry are utilized together to estimate the ego-motion of an unmanned ground vehicle. The IMU is fused with the wheel encoder to obtain the motion prior, and it is involved in three levels of the lidar odometry: Firstly, we use the IMU information to rectify the intra-frame distortion of the lidar scan, which is caused by the vehicle’s own movement; secondly, the IMU provides a better initial guess for the lidar odometry; and thirdly, the IMU is fused with the lidar odometry in an Extended Kalman filter framework. In addition, an efficient method for hand–eye calibration between the IMU and the lidar is proposed. To evaluate the performance of our method, extensive experiments are performed and our system can output stable, accurate, and high-frequency localization results in diverse environment without any prior information.
Highlights
Precise and high-frequency localization is one of the key problems for autonomous vehicles.In recent years, the fusion of Global Navigation Satellite System (GNSS) and Inertial MeasurementUnit (IMU) has been the most popular localization method
The GNSS/Inertial Measurement Unit (IMU) system will fail in some environments where GNSS signals suffer from satellite blockage or multipath propagation [1], such when an autonomous vehicle is driving in a tunnel, in the mountains, or in an environment with electromagnetic interference
A typical lidar odometry algorithm could roughly be divided into three steps: a pre-processing step, which tries to compensate the intra-frame distortion caused by the vehicle’s own movement; an intermediate step, which is the main body of the lidar odometry algorithm, and a last step, which outputs the localization result
Summary
The fusion of Global Navigation Satellite System (GNSS) and Inertial Measurement. Unit (IMU) has been the most popular localization method. The GNSS/IMU system will fail in some environments where GNSS signals suffer from satellite blockage or multipath propagation [1], such when an autonomous vehicle is driving in a tunnel, in the mountains, or in an environment with electromagnetic interference. An alternative localization method is necessary when the GNSS signal is unavailable or is of poor quality. Most autonomous vehicles are equipped with light detection and ranging (lidar) device, which is a promising sensor that could accurately calculate the range measurements of the surroundings. Some recent navigation approaches have begun to use lidar as a complementary sensor for the GNSS/IMU localization system. A typical lidar odometry algorithm could roughly be divided into three steps: a pre-processing step, which tries to compensate the intra-frame distortion caused by the vehicle’s own movement; an intermediate step, which is the main body of the lidar odometry algorithm, and a last step, which outputs the localization result
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