Object detection is a comprehensively studied problem in autonomous driving.
In this paper, we evaluate the use of vision transformers (ViT) as a backbone architecture to generate BEV maps.
We show, by experimental results with a DGPS/IMU reference, that this model provides highly accurate odometry estimates, compared with existing methods.
This challenge served as a medium to investigate the challenges and new methodologies to handle the complexities with perception on fisheye images.
OdoViz is a reactive web-based tool for 3D visualization and processing of autonomous vehicle datasets designed to support common tasks in visual place recognition research.
This paper proposes a method to estimate the pose of a sensor mounted on a vehicle as the vehicle moves through the world, an important topic for autonomous driving systems.
In this paper, we discuss the design and implementation of an automated parking system from the perspective of computer vision algorithms.
Vision-based driver assistance systems is one of the rapidly growing research areas of ITS, due to various factors such as the increased level of safety requirements in automotive, computational power in embedded systems, and desire to get closer to autonomous driving.
Keypoint detection and description is a commonly used building block in computer vision systems particularly for robotics and autonomous driving.
In this paper we present a practical approach for generating an occlusion-free textured 3D map of urban facades by the synergistic use of terrestrial images, 3D point clouds and area-based information.