3 accepted papers
Unmanned aerial vehicles (UAVs) require accurate odometry—i.e., estimating the position and velocity of the vehicle over time—as well as high-resolution sensing to safely and effectively operate in complex environments. Traditionally, GPS, cameras, and/or lidar sensors have been used to perform thes…
Current lidar and camera-based solutions for low-cost indoor mobile robots have limitations such as poor performance in visually obscured environments, high computational overhead for data processing, and high costs for lidars. In contrast, mmWave radar sensors offer a cost-effective and lightweight
In this work, we present RadCloud, a novel real-time framework for directly obtaining higher-resolution lidar-like 2D point clouds from low-resolution radar frames on resource-constrained platforms commonly used in unmanned aerial and ground vehicles (UAVs and UGVs, respectively); such point clouds…