Accurate Location in Dynamic Traffic Environment Using Semantic Information and Probabilistic Data Association

High-accurate and real-time localization is the fundamental and challenging task for autonomous driving in a dynamic traffic environment. This paper presents a coordinated positioning strategy that is composed of semantic information and probabilistic data association, which improves the accuracy of SLAM in dynamic traffic settings. First, the improved semantic segmentation network, building on Fast-SCNN, uses the Res2net module instead of the Bottleneck in the global feature extraction to further explore the multi-scale granular features. It achieves the balance between segmentation accuracy and inference speed, leading to consistent performance gains on the coordinated localization task of this paper. Second, a novel scene descriptor combining geometric, semantic, and distributional information is proposed. These descriptors are made up of significant features and their surroundings, which may be unique to a traffic scene, and are used to improve data association quality. Finally, a probabilistic data association is created to find the best estimate using a maximum measurement expectation model. This approach assigns semantic labels to landmarks observed in the environment and is used to correct false negatives in data association. We have evaluated our system with ORB-SLAM2 and DynaSLAM, the most advanced algorithms, to demonstrate its advantages. On the KITTI dataset, the results reveal that our approach outperforms other methods in dynamic traffic situations, especially in highly dynamic scenes, with sub-meter average accuracy.