Driven by deep learning techniques, perception technology in autonomous driving has
developed rapidly in recent years, enabling vehicles to accurately detect and interpret …

Multi-modal data fusion has gained popularity due to its diverse applications, leading to an
increased demand for external sensor calibration. Despite several proven calibration …

We survey the current state of millimeter-wave (mmWave) radar applications in robotics with
a focus on unique capabilities, and discuss future opportunities based on the state of the art …

Autonomous driving holds great promise in addressing traffic safety concerns by leveraging
artificial intelligence and sensor technology. Multi-Object Tracking plays a critical role in …

Given the initial calibration of multiple sensors, the fine registration between Mobile Laser
Scanning (MLS) point clouds and panoramic images is still challenging due to the …

Advanced driver assistance systems (ADASs) and autonomous vehicles rely on different
types of sensors, such as camera, radar, ultrasonic, and LiDAR, to sense the surrounding …

The development process of high fidelity SLAM systems depends on their validation upon
reliable datasets. Towards this goal, we propose IBISCape, a simulated benchmark that …

Cameras, millimeter-wave radars, and lidars are widely deployed on smart roads to obtain
personalized vehicle trajectories for advanced traffic control and risk avoidance. However …

Robot motion planning methods based on trajectory optimization can efficiently generate
feasible and optimal trajectories by minimizing a suitable cost function, even in high …

Tight integration of low-cost ultrawideband (UWB) ranging sensors with mass-market Global
Navigation Satellite System (GNSS) receivers is gaining attention as a high-accuracy …