The Surface Edge Explorer (SEE): A measurement-direct approach to next best view planning

Abstract: High-quality observations of the real world are crucial for a variety of applications, including producing 3D printed replicas of small-scale scenes and conducting inspections of large-scale infrastructure. These 3D observations are commonly obtained by combining multiple sensor measurements from different views. Guiding the selection of suitable views is known as the NBV planning problem. Most NBV approaches reason about measurements using rigid data structures (e.g., surface meshes or voxel grids). This simplifies next best view selection but can be computationally expensive, reduces real-world fidelity, and couples the selection of a next best view with the final data processing. This paper presents the Surface Edge Explorer, a NBV approach that selects new observations directly from previous sensor measurements without requiring rigid data structures. SEE uses measurement density to propose next best views that increase coverage of insufficiently observed surfaces while avoiding potential occlusions. Statistical results from simulated experiments show that SEE can attain similar or better surface coverage with less observation time and travel distance than evaluated volumetric approaches on both small- and large-scale scenes. Real-world experiments demonstrate SEE autonomously observing a deer statue using a 3D sensor affixed to a robotic arm.

• The paper introduces a measurement-direct NBV planning method that leverages sensor density to classify core, frontier, and outlier points.
• It employs local surface geometry and a frontier visibility graph to propose and refine views, effectively reducing occlusions.
• Empirical tests on simulated and real-world platforms show enhanced surface coverage with reduced travel distances and observation times.

The Surface Edge Explorer (SEE): A Measurement-Direct Approach to Next Best View Planning

The paper under discussion presents the Surface Edge Explorer (SEE), an innovative approach to addressing the Next Best View (NBV) planning problem in 3D scene observation. SEE distinguishes itself by eliminating reliance on traditional rigid data structures like voxel grids or surface meshes, which are commonly used in NBV approaches but are computationally expensive and can degrade the fidelity of observations.

Key Contributions and Methodology

SEE leverages a measurement-direct approach where NBV decisions are made based on the actual density of sensor measurements. This is a departure from model-free approaches that utilize structured representations, which often result in decreased observation fidelity and increased computational demands. The core methodology involves classifying sensor measurements into core, frontier, and outlier points based on neighborhood density. This classification allows SEE to identify insufficiently observed surfaces and propose views that enhance coverage while minimizing occlusions.

The algorithm proceeds by proposing views based on the local surface geometry around frontier points, while proactively refining these views to avoid occlusions. This is quantitatively supported by a frontier visibility graph, which encodes the visibility relationships between proposed views and frontier points, facilitating efficient NBV selection. Notably, frontier handling is both proactive and reactive, adjusting failed views dynamically to overcome unknown occlusions or surface discontinuities.

Empirical Evaluation

SEE’s performance was rigorously evaluated through simulated experiments using both small and large-scale 3D models, demonstrating significant improvements in observation efficiency. The simulated experiments compared SEE to seven volumetric NBV approaches, showing that SEE consistently achieved similar or better surface coverage with reduced travel distances and observation times. Particularly noteworthy was SEE's performance in obtaining high-quality observations of models with complex geometries, where traditional approaches struggle with visibility and coverage.

Additionally, real-world experiments on a UR10 robotic arm platform further validated SEE’s effectiveness in practical settings. Despite the challenges posed by sensor noise and varied scene textures and geometries, SEE successfully captured accurate observations of a deer statue, underscoring its applicability to real-world scenarios.

Practical and Theoretical Implications

Practically, SEE's ability to efficiently propose and select views without constructing or maintaining computationally intensive data structures like voxel grids has implications for applications ranging from industrial inspection to heritage preservation. Theoretically, the approach challenges existing paradigms in NBV planning by illustrating the potential benefits of direct measurement-based decision-making, which could influence future developments in this domain.

Future Directions

Given the promising results of SEE, future research could explore extending its methodology to other autonomous platforms, such as unmanned aerial vehicles or ground robots in more dynamic environments. Additionally, integrating more advanced sensor fusion techniques and machine learning for real-time adaptation to changing environmental conditions could further enhance its robustness and applicability.

In conclusion, SEE’s novel measurement-direct approach contributes significantly to the field of NBV planning by offering an efficient, high-fidelity solution that bypasses the limitations of traditional volumetric and surface-based methods. Its empirical success suggests a compelling framework for future investigation and development in 3D observation strategies.