Infrastructure-based vehicle localization, in comparison to single-agent approaches, offers several advantages including reduced system cost, extended perception range, enhanced data fusion capabilities, and energy savings. Many conventional approaches impose limitations on the types of objects due to the need for specific object-end modifications, such as applying perceptual markers like color-labeled plates and reflective balls. LiDAR presents a solution in terms of object arbitrariness, as it addresses the challenges of feature-free object modeling and continuous registration. However, achieving complete environmental coverage with LiDAR remains prohibitively expensive, particularly in extensive areas. Hence, this study proposes a cross-modal localization approach using adaptive modeling, employing LiDAR for object modeling and cost-effective sensor cameras for object tracking through image-point-cloud registration. Accurate correspondence between the model and observation can be estimated in real-time. The experiments are conducted in a typical scenario that requires adaptive modeling: Autonomous Valet Parking (AVP). Results demonstrate that the proposed system achieves comparable performance with significantly reduced system costs, highlighting its potential for large-scale deployment.

The installation of a multi-layer 3D LiDAR atop the vehicle is a widely adopted hardware configuration for map-matching-based localization in intelligent driving. By offering a comprehensive 360° horizontal Field of View (FoV), this setup aims to achieve precise matching outcomes through the imposition of substantial geometric constraints against dynamic interferences and structural degradation. However, several factors limit its environmental adaptability, such as sparse point cloud density at distances, insufficient maximum sensing range, and notably, the restricted beam elevation angle, limiting the perception of the environment beyond obstacles. The rapid advancement of non-repetitive scanning LiDARs shows promise in mitigating such limitations. Nevertheless, their narrow FoV remains a challenge to overcome. In this study, we propose a solution by mounting such one single LiDAR on a two-axis rotating gimbal, enabling the vehicle to surpass the ranges and vertical FoV limitations of traditional setups actively. The corresponding gimbal motion strategy has been designed to automatically focus on the environment component with the most robust geometric constraints. Experimental results validate that the proposed method achieves superior robustness under high dynamic interference while delivering sufficient performance under standard conditions.

Dynamic environments are challenging for anchor-free mapping using lidar in intelligent driving. This study imitates anchor-based approaches such as magnetic nails by applying novel Static Confidence Criteria (SCC) to the point-cloud semantic candidates to ensure their robustness. We name such verified features Pseudo-Anchors (P-A) as they hold similar properties to the anchor nodes: The P-A nodes are improbably formed by dynamic objects, and nodes’ blockage state can be immediately noticed once they are occluded. Another major challenge for mapping is improving large-scale global performance without sacrificing local consistency. Unrecognized GNSS pose drift may deteriorate local trajectory accuracy through post-processing such as graph optimization. In this study, we use the road network to provide the intersection information as a prior so that the GNSS can be better regarded as a reliable anchor factor. Three experiments are designed for this study. The first is ablations to verify the P-A concept; The second proves that the P-A-based lidar odometry outperformed the LOAM-based mainstream methods in highly dynamic scenarios; The third shows that our usage of the GNSS strengthens large-scale maps’ global consistency while causing less deterioration towards the local one. As a knowledge-based method, the P-A concept shows a high deployment efficiency, indicating the potential for migration to other features or even other sensors.

Feature descriptors, as abstraction of the critical information in the lidar point clouds, are often used in global pose initialization in large-area to provide a pose reference for intelligent driving system. The current state-of-the-art method Scan Context descriptor is generated based on points’ maximum height and, therefore, designed especially for outdoor scenarios without a ceiling. This study proposes a generic descriptor for both outdoor and indoor scenarios based on the point cloud’s Gridded Gaussian Distribution (GGD). Wasserstein distance is introduced to this field to evaluate the proposed GGD descriptors’ matching performance because it not only has a solid mathematical foundation in comparing two Gaussian distributions but also shows excellent time efficiency via a straightforward analytical solution without enumeration or iteration. We construct a multi-step error function to initialize the vehicle pose using conventional cosine similarity, and Wasserstein distance. Two experiments are designed for this study. The first experiment compares the pose initialization performances under various multi-frame superimposition distance in space to find an efficient GGD descriptor extraction setting. The second experiment verifies that the proposed method achieves a better pose initialization success rate than the mainstream methods.