Fiducial Marker Splatting for High-Fidelity Robotics Simulations
This work addresses the need for realistic simulations with fiducial markers in robotics, particularly for challenging environments like greenhouses, though it appears incremental as it builds on existing Gaussian Splatting methods.
The paper tackles the problem of incorporating fiducial markers into high-fidelity 3D simulations for robotics, which is hindered by traditional mesh-based and neural rendering methods, by proposing a hybrid framework that combines Gaussian Splatting with structured marker representations, resulting in improved efficiency and pose-estimation accuracy over traditional techniques.
High-fidelity 3D simulation is critical for training mobile robots, but its traditional reliance on mesh-based representations often struggle in complex environments, such as densely packed greenhouses featuring occlusions and repetitive structures. Recent neural rendering methods, like Gaussian Splatting (GS), achieve remarkable visual realism but lack flexibility to incorporate fiducial markers, which are essential for robotic localization and control. We propose a hybrid framework that combines the photorealism of GS with structured marker representations. Our core contribution is a novel algorithm for efficiently generating GS-based fiducial markers (e.g., AprilTags) within cluttered scenes. Experiments show that our approach outperforms traditional image-fitting techniques in both efficiency and pose-estimation accuracy. We further demonstrate the framework's potential in a greenhouse simulation. This agricultural setting serves as a challenging testbed, as its combination of dense foliage, similar-looking elements, and occlusions pushes the limits of perception, thereby highlighting the framework's value for real-world applications.