ActiveGS: Active Scene Reconstruction Using Gaussian Splatting (2412.17769v2)

Abstract: Robotics applications often rely on scene reconstructions to enable downstream tasks. In this work, we tackle the challenge of actively building an accurate map of an unknown scene using an RGB-D camera on a mobile platform. We propose a hybrid map representation that combines a Gaussian splatting map with a coarse voxel map, leveraging the strengths of both representations: the high-fidelity scene reconstruction capabilities of Gaussian splatting and the spatial modelling strengths of the voxel map. At the core of our framework is an effective confidence modelling technique for the Gaussian splatting map to identify under-reconstructed areas, while utilising spatial information from the voxel map to target unexplored areas and assist in collision-free path planning. By actively collecting scene information in under-reconstructed and unexplored areas for map updates, our approach achieves superior Gaussian splatting reconstruction results compared to state-of-the-art approaches. Additionally, we demonstrate the real-world applicability of our framework using an unmanned aerial vehicle.

• The paper introduces a hybrid mapping framework that fuses Gaussian splatting and voxel mapping to capture both fine details and spatial occupancy.
• It employs confidence modeling to guide active view planning, optimizing data collection in under-explored areas.
• Real-world trials with UAVs demonstrate superior PSNR and mesh quality, ensuring efficient and robust scene reconstruction.

Insightful Overview of "ActiveGS: Active Scene Reconstruction using Gaussian Splatting"

The paper "ActiveGS: Active Scene Reconstruction using Gaussian Splatting" presents a novel framework for active scene reconstruction, designed to enhance the capabilities of robotic systems in mapping unknown environments with higher fidelity and efficiency. This framework combines Gaussian splatting (GS) with conventional voxel mapping, leveraging the strengths of both representations to produce high-quality and spatially accurate reconstructions.

Summary of Contributions

The primary contribution of this work is the hybrid mapping framework that combines a Gaussian splatting map, which excels in capturing fine-grained geometric details, with a voxel map that effectively models spatial occupancy. This dual representation provides a comprehensive approach to scene reconstruction, addressing both the need for high-resolution details and efficient spatial exploration. The paper introduces several key innovations:

1. Confidence Modelling Technique: A unique approach to determine confidence levels of Gaussian primitives based on historical viewpoint distribution. This technique allows the identification of under-reconstructed areas, facilitating targeted data acquisition and ensuring high reconstruction fidelity.
2. Active View Planning: The method employs a dynamic view planning strategy informed by confidence modelling, enabling adaptive exploration and efficient data collection. The framework actively selects new viewpoints, optimizing both exploration of unknown spaces and detailed inspection of low-confidence regions.
3. Real-world Validation: Beyond simulation, the framework's applicability is validated using an unmanned aerial vehicle, demonstrating its effectiveness in a tangible real-world scenario.

Key Experimentation Results

The experimental evaluation focuses on both synthetic simulation environments and real-world scenarios. In simulations, ActiveGS is benchmarked against state-of-the-art approaches, including NeRF-based and other GS-based active scene reconstruction methods. Notably, ActiveGS consistently outperforms these baselines in both rendering quality (evaluated through PSNR) and mesh quality (using completeness ratio), affirming its superior reconstruction capabilities.

• Rendering and Mesh Quality: ActiveGS demonstrates significant improvements in PSNR and mesh completeness ratio across diverse environments. This indicates that it not only captures detailed scene information but also converts it into more accurate three-dimensional representations.
• Performance Efficiency: The framework efficiently utilizes mission time, showing robustness in planning and mapping. The explicit modelling of Gaussian primitive confidence enables lightweight computational processing, offering a practical approach for real-time applications on resource-constrained platforms.

Implications and Speculations on Future Directions

The success of ActiveGS in real-world trials suggests its potential application across various domains, from autonomous navigation in unknown terrains to complex architectural reconstruction tasks. The dual-representation method paves the way for more adaptable and intelligent robotic systems capable of both exploration and exploitation tasks.

Future developments could focus on integrating uncertainty handling and localization errors into the framework, enhancing its robustness in dynamic or unpredictable environments. Moreover, advances may explore further optimization of the GS and voxel integration to streamline online processing and extend applicability to larger-scale scenes.

In conclusion, ActiveGS offers a promising enhancement to current scene reconstruction methodologies, equipping autonomous systems with refined tools for high-fidelity mapping. By addressing computational efficiency and reconstruction quality harmoniously, this research marks a step forward in the evolution of autonomous robotic exploration technologies.