SegLocNet: Multimodal Localization Network for Autonomous Driving via Bird's-Eye-View Segmentation
This addresses localization challenges in autonomous driving, offering a solution that balances accuracy and coverage without relying on costly HD maps, though it appears incremental as it builds on existing segmentation and matching techniques.
The paper tackles the problem of robust localization for autonomous driving by proposing SegLocNet, a multimodal network that uses bird's-eye-view semantic segmentation to estimate vehicle pose without GNSS, achieving state-of-the-art performance on nuScenes and Argoverse datasets.
Robust and accurate localization is critical for autonomous driving. Traditional GNSS-based localization methods suffer from signal occlusion and multipath effects in urban environments. Meanwhile, methods relying on high-definition (HD) maps are constrained by the high costs associated with the construction and maintenance of HD maps. Standard-definition (SD) maps-based methods, on the other hand, often exhibit unsatisfactory performance or poor generalization ability due to overfitting. To address these challenges, we propose SegLocNet, a multimodal GNSS-free localization network that achieves precise localization using bird's-eye-view (BEV) semantic segmentation. SegLocNet employs a BEV segmentation network to generate semantic maps from multiple sensor inputs, followed by an exhaustive matching process to estimate the vehicle's ego pose. This approach avoids the limitations of regression-based pose estimation and maintains high interpretability and generalization. By introducing a unified map representation, our method can be applied to both HD and SD maps without any modifications to the network architecture, thereby balancing localization accuracy and area coverage. Extensive experiments on the nuScenes and Argoverse datasets demonstrate that our method outperforms the current state-of-the-art methods, and that our method can accurately estimate the ego pose in urban environments without relying on GNSS, while maintaining strong generalization ability. Our code and pre-trained model will be released publicly.