MISO: Multiresolution Submap Optimization for Efficient Globally Consistent Neural Implicit Reconstruction (2504.19104v1)

Abstract: Neural implicit representations have had a significant impact on simultaneous localization and mapping (SLAM) by enabling robots to build continuous, differentiable, and high-fidelity 3D maps from sensor data. However, as the scale and complexity of the environment increase, neural SLAM approaches face renewed challenges in the back-end optimization process to keep up with runtime requirements and maintain global consistency. We introduce MISO, a hierarchical optimization approach that leverages multiresolution submaps to achieve efficient and scalable neural implicit reconstruction. For local SLAM within each submap, we develop a hierarchical optimization scheme with learned initialization that substantially reduces the time needed to optimize the implicit submap features. To correct estimation drift globally, we develop a hierarchical method to align and fuse the multiresolution submaps, leading to substantial acceleration by avoiding the need to decode the full scene geometry. MISO significantly improves computational efficiency and estimation accuracy of neural signed distance function (SDF) SLAM on large-scale real-world benchmarks.

Overview of "MISO: Multiresolution Submap Optimization for Efficient Globally Consistent Neural Implicit Reconstruction"

The paper "MISO: Multiresolution Submap Optimization for Efficient Globally Consistent Neural Implicit Reconstruction" presents a novel approach to enhancing simultaneous localization and mapping (SLAM) systems through neural implicit representations. The key contribution is the introduction of a hierarchical optimization approach termed MISO (Multiresolution Submap Optimization), designed to improve computational efficiency and accuracy in building 3D maps from sensor data using neural signed distance functions (SDF). By leveraging multiresolution submaps, MISO addresses scalability challenges in neural SLAM systems, particularly in large-scale environments, and maintains global consistency across these reconstructions.

Methodology

MISO focuses on two primary aspects: local SLAM optimization within each submap and global alignment and fusion between submaps. Each submap employs a multiresolution feature grid to manage implicit neural features across varying spatial resolutions. This representation significantly aids in disentangling coarse and fine geometric details, thereby enabling efficient reconstructions across large domains.

1. Local SLAM Optimization: MISO utilizes a hierarchical optimization scheme complemented by learned initialization strategies to rapidly optimize the implicit features within submaps. This approach minimizes the time required for feature optimization, promoting efficient scene reconstructions. The local optimization process jointly refines robot trajectory estimates alongside these neural implicit features, ensuring robust data representation despite uncertainties inherent in sensor observations.
2. Global Submap Fusion and Alignment: To achieve global consistency, MISO incorporates a hierarchical alignment strategy that aligns and fuses submaps in the global frame. This method avoids the computational overhead of decoding full scene geometry, focusing instead on aligning submap features directly. This subsection of MISO’s approach significantly accelerates processing by integrating multiresolution feature data across the global environment.

Numerical Results

The paper provides quantitative results demonstrating MISO's superior performance relative to existing SLAM techniques. Notably:

• Computational Efficiency: MISO exhibits marked improvements in processing speed, with optimization tasks completed faster than in comparable models. In tests with real-world datasets such as ScanNet and FastCaMo-Large, MISO achieved efficient reconstructions while maintaining high fidelity to ground truth data.
• Accuracy Improvements: Through refined global alignment and fusion techniques at both coarse and fine levels, MISO shows reduced estimation drift and higher accuracy in scene representation parameters, indicating its robustness to initial alignment errors.

Implications and Future Directions

The implications of MISO's methodology extend to both practical and theoretical domains within robotics and computer vision. Practically, MISO offers an efficient framework for real-time 3D scene reconstruction, applicable in dynamic and large-scale environments. Theoretically, its approach to hierarchical optimization in neural implicit representations proposes a scalable solution, driving future research towards even larger-scale SLAM applications.

In future developments, the expansion of MISO’s capabilities through adaptive feature grids or hybrid neural architectures could further mitigate memory usage concerns inherent in dense representations. Additionally, extending MISO’s application beyond static environments to handle dynamic scenarios may open new opportunities in autonomous system navigation and interaction.

In summary, MISO represents a significant advance in neural implicit SLAM systems, enabling efficient and globally consistent reconstructions that are robust across large and complex environments. It establishes foundational techniques that are likely to inspire continued innovation and refinement in the field of AI-driven spatial mapping and perception.