Vectorized reconstruction and topological cognition of road structures are crucial for autonomous vehicles to handle complex scenes. Traditional frameworks rely heavily on high-definition (HD) maps, which place significant demands on storage, computation, and manual labor. To overcome these limitations, we introduce a lightweight Road Cognition and Automated Labeling (RCAL) system. It leverages lightweight road data captured from mass-produced vehicles to vectorize road elements and cognize their topology. RCAL compiles multi-trip data on cloud servers for enhanced accuracy and coverage, addressing the limitations of single-trip data. In the field of element extraction, we proposed a pivotal point priority sampling strategy that can balance the contradiction between road scale and processing efficiency. Additionally, traffic flow is utilized to enhance the accuracy of road topology cognition. With its impressive automation, reliability, and efficiency, RCAL stands as an advanced solution in the field. Our evaluations on the intersection dataset from the real world confirm that RCAL not only achieves comparable precision to traditional HD map labeling systems but also substantially reducing resource costs.

Highly accurate and robust vectorized reconstruction of road structures is crucial for autonomous vehicles. Traditional LiDAR-based methods require multiple processes and are often expensive, time-consuming, labor-intensive, and cumbersome. In this paper, we propose a lightweight crowdsourced road structure reconstruction system (termed CSR) that relies solely on online perceived semantic elements. Ambiguities and perceptual errors of semantic features and Global Navigation Satellite System (GNSS) global pose errors constitute the predominant challenge in achieving alignment across multi-trip data. To this end, a robust two-phased coarse-to-fine multi-trip alignment method is performed considering local geometric consistency, global topology consistency, intra-trip temporal consistency, and inter-trip consistency. Further, we introduce an incremental pose graph optimization framework with adaptive weight tuning ability to integrate pre-built road structures, currently perceived multi-trip semantic features, odometry, and GNSS, enabling accurate and robust incremental road structure reconstruction. CSR is highly automated, efficient, and scalable for large-scale autonomous driving scenarios, significantly expediting road structure production. We quantitatively and qualitatively validate the reconstruction performance of CSR in real-world scenes. CSR achieves centimeter-level accuracy commensurate with established LiDAR-based methods, concurrently boosting efficiency and reducing resource expenditure.

Highly accurate and robust relocalization or localization initialization ability is of great importance for autonomous vehicles (AVs). Traditional GNSS-based methods are not reliable enough in occlusion and multipath conditions. In this paper we propose a novel long-term semantic relocalization algorithm based on HD map and semantic features which are compact in representation. Semantic features appear widely on urban roads, and are robust to illumination, weather, view-point and appearance changes. Repeated structures, missed and false detections make data association (DA) highly ambiguous. To this end, a robust semantic feature matching method based on a new local semantic descriptor which encodes the spatial and normal relationship between semantic features is performed. Further, we introduce an accurate, efficient, yet simple outlier removal method which works by assessing the local and global geometric consistencies and temporal consistency of semantic matching pairs. The experimental results on our urban dataset demonstrate that our approach performs better in accuracy and robustness compared with the current state-of-the-art methods.

Highly accurate and robust localization ability is of great importance for autonomous vehicles (AVs) in urban scenarios. Traditional vision-based methods suffer from lost due to illumination, weather, viewing and appearance changes. In this paper we propose a novel visual semantic localization algorithm based on HD map and semantic features which are compact in representation. Semantic features are widely appeared on urban roads, and are robust to illumination, weather, viewing and appearance changes. The repeated structures, missed detections and false detections make data association (DA) highly ambiguous. To this end, a robust DA method considering local structural consistency, global pattern consistency and temporal consistency is performed. Further, we introduce a sliding window factor graph optimization framework to fuse association and odometry measurements without the requirements of high-precision absolute height information for map features.We evaluate the proposed localization framework on both simulated and real urban road. The experiments show that the proposed approach is able to achieve highly accurate localization with a mean longitudinal error of 0.43m, a mean lateral error of 0.12m and a mean yaw angle error of 0.11°.

Robust visual localization in traffic scenes is a fundamental problem for self-driving vehicles. However, it is still challenging to achieve accurate localization performance because of drastic viewpoint and illumination changes. To address the issues, we design a novel monocular localization framework based on a light-weight prior map, called BSP-MonoLoc, which leverages the 2D semantic primitives from the monocular images and the 3D basic semantic primitives from the prior map. These primitives are commonly available but lack of distinctive signature. To effectively make associations between the 2D and 3D primitives and refine the vehicle’s pose, we adopt an iterative optimization method, where an efficient hierarchical sample strategy is designed to give a good initial prediction for the associations and the pose. Experimental results on the KAIST dataset and our dataset demonstrate the proposed method can achieve high localization accuracy and run at a real-time performance.