Automated systems require a comprehensive understanding of their surroundings to safely
interact with the environment. By effectively fusing sensory information from multiple sensors …

Seamless Human-Robot Interaction is the ultimate goal of developing service robotic
systems. For this, the robotic agents have to understand their surroundings to better …

The perception system for robotics and autonomous cars relies on the collaboration among
multiple types of sensors to understand the surrounding environment. LiDAR has shown …

Estimating the 6D pose presents a formidable challenge due to the intricate nature of world
objects and the myriad of issues encountered when acquiring data from real-world scenes …

Seamless Human-Robot Interaction is the ultimate goal of developing service robotic
systems. For this, the robotic agents have to understand their surroundings to better …

In this work, we explore how a strategic selection of camera movements can facilitate the
task of 6D multi-object pose estimation in cluttered scenarios while respecting real-world …

In the context of autonomous driving environment perception, multi-modal fusion plays a
pivotal role in enhancing robustness, completeness, and accuracy, thereby extending the …

To enhance the level of autonomy in driving, it is crucial to ensure optimal execution of
critical maneuvers in all situations. However, numerous accidents involving autonomous …

Mobile robotic platforms require a precise understanding about other agents in their
surroundings as well as their respective motion in order to operate safely. Scene flow in …

Deep learning methods for 6D object pose estimation based on RGB and depth (RGB-D)
images have been successfully applied to robotic manipulation and grasping. Among these …