The rapid development of autonomous driving technology has driven continuous innovation
in perception systems, with 4D millimeter-wave (mmWave) radar being one of the key …

The 4D millimeter-Wave (mmWave) radar is a promising technology for vehicle sensing due
to its cost-effectiveness and operability in adverse weather conditions. However, the …

Four-dimensional (4D) radar–visual odometry (4DRVO) integrates complementary
information from 4D radar and cameras, making it an attractive solution for achieving …

Simultaneous Localization and Mapping (SLAM) is moving towards a robust perception age.
However, LiDAR-and visual-SLAM may easily fail in adverse conditions (rain, snow, smoke …

Place Recognition is crucial for unmanned vehicles in terms of localization and mapping.
Recent years have witnessed numerous explorations in the field where 2D cameras and 3D …

This paper provides an in-depth review of deep learning techniques to address the
challenges of odometry and global ego-localization using frequency modulated continuous …

There is a current increase in the development of" 4D" Doppler-capable radar and lidar
range sensors that produce 3D point clouds where all points also have information about the …

Radar offers the advantage of providing additional physical properties related to observed
objects. In this study, we design a physical-enhanced radar-inertial odometry system that …

Localization is a crucial component for the navigation of autonomous vehicles. It
encompasses global localization and place recognition, allowing a system to identify …

LiDAR-and Visual-SLAM are prone to fail in smoke and fog environments, because LiDAR
and visual camera cannot penetrate smoke and fog. Fortunately, both 4D radar (x, y, z …