Deep Perspective Transformation Based Vehicle Localization on Bird's Eye View
This provides a cost-effective solution for environmental perception in autonomous driving, though it is incremental as it builds on existing perspective-to-BEV transformation methods.
The paper tackles vehicle localization for self-driving cars by generating top-down bird's-eye-view maps from perspective RGB images, enabling distance and direction extraction of surrounding vehicles, and introduces a new synthesized dataset and architecture for this task.
An accurate understanding of a self-driving vehicle's surrounding environment is crucial for its navigation system. To enhance the effectiveness of existing algorithms and facilitate further research, it is essential to provide comprehensive data to the routing system. Traditional approaches rely on installing multiple sensors to simulate the environment, leading to high costs and complexity. In this paper, we propose an alternative solution by generating a top-down representation of the scene, enabling the extraction of distances and directions of other cars relative to the ego vehicle. We introduce a new synthesized dataset that offers extensive information about the ego vehicle and its environment in each frame, providing valuable resources for similar downstream tasks. Additionally, we present an architecture that transforms perspective view RGB images into bird's-eye-view maps with segmented surrounding vehicles. This approach offers an efficient and cost-effective method for capturing crucial environmental information for self-driving cars. Code and dataset are available at https://github.com/IPM-HPC/Perspective-BEV-Transformer.