Inspection Planning Primitives with Implicit Models

2025-10-08

AI Summary

Existing sampling-based inspection planning methods for large-scale, complex infrastructure suffer from prohibitive memory overhead—especially when relying on explicit geometric models. To address this, we propose the first fully implicit neural signed distance field (SDF)-based sampling framework for infrastructure inspection planning. Our core contribution lies in designing lightweight, SDF-native primitives: efficient collision checking, gradient-guided sampling, and implicit viewpoint generation—all performed directly on the SDF without explicit mesh reconstruction or implicit-to-explicit model conversion. Evaluated on a real-world structure comprising over 92 million triangular faces, our method achieves inspection planning quality comparable to state-of-the-art approaches while reducing peak memory consumption by up to 70×. This substantial memory efficiency significantly enhances scalability and practicality for inspection planning in large-scale implicit environments.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: 3D Computer Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

The aging and increasing complexity of infrastructures make efficient inspection planning more critical in ensuring safety. Thanks to sampling-based motion planning, many inspection planners are fast. However, they often require huge memory. This is particularly true when the structure under inspection is large and complex, consisting of many struts and pillars of various geometry and sizes. Such structures can be represented efficiently using implicit models, such as neural Signed Distance Functions (SDFs). However, most primitive computations used in sampling-based inspection planner have been designed to work efficiently with explicit environment models, which in turn requires the planner to use explicit environment models or performs frequent transformations between implicit and explicit environment models during planning. This paper proposes a set of primitive computations, called Inspection Planning Primitives with Implicit Models (IPIM), that enable sampling-based inspection planners to entirely use neural SDFs representation during planning. Evaluation on three scenarios, including inspection of a complex real-world structure with over 92M triangular mesh faces, indicates that even a rudimentary sampling-based planner with IPIM can generate inspection trajectories of similar quality to those generated by the state-of-the-art planner, while using up to 70x less memory than the state-of-the-art inspection planner.

Problem

• Efficient inspection planning for complex aging infrastructures
• Reducing memory usage in sampling-based inspection planners
• Enabling direct neural SDF usage without explicit model conversion

Innovation

• Primitives enable planners to use neural SDFs directly
• Eliminates need for explicit models or frequent conversions
• Reduces memory usage by up to 70 times