Abstract
Simultaneous Localization and Mapping (SLAM) is an essential feature in many applications of mobile vehicles. To solve the problem of poor positioning accuracy, single use of mapping scene, and unclear structural characteristics in indoor and outdoor SLAM, a new framework of tight coupling of dual lidar inertial odometry is proposed in this paper. Firstly, through external calibration and an adaptive timestamp synchronization algorithm, the horizontal and vertical lidar data are fused, which compensates for the narrow vertical field of view (FOV) of the lidar and makes the characteristics of vertical direction more complete in the mapping process. Secondly, the dual lidar data is tightly coupled with an Inertial Measurement Unit (IMU) to eliminate the motion distortion of the dual lidar odometry. Then, the value of the lidar odometry after correcting distortion and the pre-integrated value of IMU are used as constraints to establish a non-linear least-squares objective function. Joint optimization is then performed to obtain the best value of the IMU state values, which will be used to predict the state of IMU at the next time step. Finally, experimental results are presented to verify the effectiveness of the proposed method.
Highlights
Simultaneous localization and mapping (SLAM) require building a map of an unknown environment by a mobile vehicle and simultaneously localizing the vehicle in such a map [1,2,3]
This paper proposes a high-precision SLAM framework based on tight coupling of dual lidar inertial to overcome the above problems
Figure where lidar_201/scan represents the timestamp of the horizontal lidar and lidar_202/scan represents the timestamp of the the timestamp of the horizontal lidar_202/scan represents the timestamp the match its timestamp and realize the simultaneous localization andofmapping o vertical lidar
Summary
Simultaneous localization and mapping (SLAM) require building a map of an unknown environment by a mobile vehicle and simultaneously localizing the vehicle in such a map [1,2,3]. This paper proposes a high-precision SLAM framework based on tight coupling of dual lidar inertial to overcome the above problems. In this framework, the horizontal lidar and the vertical lidar are integrated to broaden the FOV. To improve the positioning accuracy of SLAM in the environment with height information (e.g., stairs), the dual lidar odometry and IMU are tightly coupled. The dual lidar odometry measurement and IMU pre-integration are jointly optimized to obtain more accurate IMU state values, which will be used to eliminate the motion distortion of the dual lidar to improve the localization accuracy. Several practical experiments are carried out to verify the feasibility and effectiveness of the proposed method
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