关键词: 自动驾驶, 语义分割, 相机位姿优化, 行人意图预测

Abstract: Visual simultaneous localization and mapping (VSLAM) has found extensive applications in the field of autonomous driving. However, conventional algorithms lack semantic information and are incapable of inferring or predicting pedestrians’ behaviors or intentions within a scene. This paper introduces an effective semantic VSLAM method that employs a semantic segmentation algorithm based on dense prediction transformer (DPT) to acquire segmentation masks for potential dynamic targets, enabling dynamic feature removal. Given that the majority of dynamic objects in autonomous driving scenarios are pedestrians and vehicles, in order to both reintegrate static points from potential dynamic targets and re-detect dynamic objects, a geometric constraint is employed to jointly optimize camera poses while predicting pedestrian intentions. To accurately forecast whether pedestrians are crossing the road, a dual-stream, spatiotemporal adaptive graph convolutional neural network is built using human skeletal information to predict pedestrian jaywalking intentions. The results validated on the KITTI dataset indicate that the proposed approach, in comparison to the ORB-SLAM3 algorithm, has a certain reduction in absolute trajectory estimation errors, demonstrating superior precision compared to algorithms of similar nature. This method holds the potential to furnish autonomous driving systems with richer semantic information, thereby enhancing the accomplishment of autonomous driving tasks.

Key words: autonomous driving, semantic segmentation, camera pose optimization, pedestrian intention prediction