NAR-*ICP: Neural Execution of Classical ICP-based Pointcloud Registration Algorithms

2024-10-14

arXiv.org

AI Summary

Neural networks suffer from poor interpretability in safety-critical robotic applications, while classical algorithms—such as Iterative Closest Point (ICP)—exhibit limited flexibility and robustness. Method: This paper discretizes intermediate steps of ICP point cloud registration into a learnable neural execution process and, for the first time, embeds it within the Neural Algorithmic Reasoning (NAR) framework. Leveraging graph neural networks (GNNs), the approach structurally models algorithmic steps and enables end-to-end optimization. We further extend the CLRS benchmark to robot perception tasks. Contribution/Results: The neuralized ICP consistently outperforms traditional ICP variants and state-of-the-art learning-based methods across diverse real-world and synthetic multi-source datasets. It achieves significant improvements in noise robustness, cross-scenario generalization, and system integrability—remarkably, even surpassing the performance of the classical ICP algorithm it emulates.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This study explores the intersection of neural networks and classical robotics algorithms through the Neural Algorithmic Reasoning (NAR) framework, allowing to train neural networks to effectively reason like classical robotics algorithms by learning to execute them. Algorithms are integral to robotics and safety-critical applications due to their predictable and consistent performance through logical and mathematical principles. In contrast, while neural networks are highly adaptable, handling complex, high-dimensional data and generalising across tasks, they often lack interpretability and transparency in their internal computations. We propose a Graph Neural Network (GNN)-based learning framework, NAR-*ICP, which learns the intermediate algorithmic steps of classical ICP-based pointcloud registration algorithms, and extend the CLRS Algorithmic Reasoning Benchmark with classical robotics perception algorithms. We evaluate our approach across diverse datasets, from real-world to synthetic, demonstrating its flexibility in handling complex and noisy inputs, along with its potential to be used as part of a larger learning system. Our results indicate that our method achieves superior performance across all benchmarks and datasets, consistently surpassing even the algorithms it has been trained on, further demonstrating its ability to generalise beyond the capabilities of traditional algorithms.

Problem

• Bridging neural networks with classical robotics algorithms
• Learning intermediate computations of ICP-based registration
• Enhancing interpretability and performance in pointcloud registration

Innovation

• Neural networks execute classical ICP registration algorithms
• Graph Neural Network learns intermediate computations of ICP
• Method generalizes beyond traditional algorithm capabilities