6-DOF Vehicle Pose Estimation Considering Lidar Odometry Initial Condition

Abstract

Precise localization is essential for reliable autonomous driving. Traditionally, many systems have turned to lane level map matching techniques utilizing High Definition Maps (HD-Maps). However, it is necessary to have considerate efforts tied to the continuous availability and timeliness of these HD-Maps. Taking this into account, this paper explores an alternative approach, focusing on landmark-based odometry and a filtering-based technique. The proposed localization algorithm is integrated with the GPS and the odometry in a loosely coupled approach. Because it is important to obtain an initial pose in order to utilize the odometry, the initial odometry rotation is estimated from the filtering method, as an alternative to optimization methods, to improve the global consistency in localization. In addition, because specific landmark-based odometry, like lidar odometry, can become vulnerable in geometrically repetitive scenarios such as in tunnels, this study considers the change in vehicle speed and updates the landmark-based odometry, aiming to address the limitations. Through experimental tests in normal scenarios and challenging scenarios like tunnels, it is demonstrated that our method offers certain benefits over the existing techniques.

Keywords

• Localization
• pose estimation
• landmark-based odometry
• Kalman filter
• initialization