8 accepted papers
3D Gaussian splatting (3DGS) has recently demonstrated significant potential in computer vision, enabling high-fidelity 3D scene reconstruction with real-time rendering and fast training times. However, existing methods struggle in large, visually sparse, geometric self-similarity environments due t
End-to-end navigation via deep reinforcement learning has become a key approach for vision-based tasks. However, the sim-to-real gap remains a challenge, especially for aerial robots, where policies trained in simulation often fail in real-world environments. In this work, we propose a novel navigat…
Multi-agent path planning is a critical challenge in robotics, requiring agents to navigate complex environments while avoiding collisions and optimizing travel efficiency. This work addresses the limitations of existing approaches by combining Gaussian belief propagation with path integration and i
We present an accessible robot interface for telemanipulation (A-RIFT), which preserves the haptic channel partially in a zero-additional-cost interface by visual substitution of force feedback (VSFF). This work explores a gap in the literature, resulting from the focus on performance improvements i…
Nowadays, wheeled mobile robots constitute a considerable portion of robots in industrial applications. Generally, regardless of their purpose, these systems are not designed to physically interact with humans, other robots, or the environment. In this study, we present a novel safe autonomous mobil…
In autonomous and mobile robotics, one of the main challenges is the robust on-the-fly perception of the environment, which is often unknown and dynamic, like in autonomous drone racing. In this work, we propose a novel deep neural network-based perception method for racing gate detection – PencilNe
Fast and robust gate perception is of great importance in autonomous drone racing. We propose a convolutional neural network-based gate detector (GateNet1) that concurrently detects gate’s center, distance, and orientation with respect to the drone using only images from a single fish-eye RGB camera…
This work presents an online learning-based control method for improved trajectory tracking of unmanned aerial vehicles using both deep learning and expert knowledge. The proposed method does not require the exact model of the system to be controlled, and it is robust against variations in system dy…