Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …

In recent years visual place recognition (VPR), ie, the problem of recognizing the location of
images, has received considerable attention from multiple research communities, spanning …

Camera localization involves the estimation of the camera pose of an image from a random
scene. We used a single image or sequence of images or videos as the input. The output …

Visual localization is a fundamental task for various applications including autonomous
driving and robotics. Prior methods focus on extracting large amounts of often redundant …

Existing multi-agent perception algorithms usually select to share deep neural features
extracted from raw sensing data between agents, achieving a trade-off between accuracy …

A novel 3D point cloud learning model for deep LiDAR odometry, named PWCLO-Net, using
hierarchical embedding mask optimization is proposed in this paper. In this model, the …

Scene flow represents the 3D motion of every point in the dynamic environments. Like the
optical flow that represents the motion of pixels in 2D images, 3D motion representation of …

An efficient 3D point cloud learning architecture, named EfficientLO-Net, for LiDAR odometry
is first proposed in this article. In this architecture, the projection-aware representation of the …

Inspired by properties of semantic segmentation, in this paper we investigate how to
leverage robust image segmentation in the context of privacy-preserving visual localization …

Odometry is a crucial technology for the autonomous positioning of intelligent vehicles.
While estimating the odometry from LiDAR and cameras has progressed recently, it remains …