Line-based SLAM considering prior distribution of distance and angle of line features in an urban environment

In this paper, we propose a line-based SLAM from an image sequence captured by a camera mounted on a vehicle in consideration with the prior distribution of line features that detected in an urban environments. Since such scenes captured by the vehicle in urban envirounments can be expected to include a lot of line segments detected from road markings and buildings, we employ line segments as features for our SLAM. We use additional prior regarding the line segments so that we can improve the accuracy of the SLAM. We assume that the angle of the vector of the line segments to the vehicle’s direction of travel conform to four-component Gaussian mixture distribution. We define a new cost function considering the prior distribution and optimize the relative camera pose, position, and the 3D line segments by bundle adjustment. The prior distribution is also extended into 2D, the distance and angle of the line segments. In addition, we make digital maps from the detected line segments. Our method increases the accuracy of localization and corrects tilted lines in the digital maps. We apply our method to both the single-camera system and the multi-camera system for demonstrate the accuracy improvement by the prior distribution of distance and angle of line features.