HERO-SLAM: Hybrid Enhanced Robust Optimization of Neural SLAM

TL;DR

HERO-SLAM introduces a hybrid optimization approach that combines neural implicit fields and feature-metric techniques to improve the robustness and performance of neural SLAM in challenging environments with sparse data or sudden viewpoint changes.

Contribution

The paper proposes a novel hybrid optimization method for neural SLAM that enhances robustness and accuracy in difficult scenarios, outperforming existing implicit field-based methods.

Findings

Superior performance in challenging environments

Enhanced robustness against sparse data and viewpoint changes

Validated on benchmarking datasets

Abstract

Simultaneous Localization and Mapping (SLAM) is a fundamental task in robotics, driving numerous applications such as autonomous driving and virtual reality. Recent progress on neural implicit SLAM has shown encouraging and impressive results. However, the robustness of neural SLAM, particularly in challenging or data-limited situations, remains an unresolved issue. This paper presents HERO-SLAM, a Hybrid Enhanced Robust Optimization method for neural SLAM, which combines the benefits of neural implicit field and feature-metric optimization. This hybrid method optimizes a multi-resolution implicit field and enhances robustness in challenging environments with sudden viewpoint changes or sparse data collection. Our comprehensive experimental results on benchmarking datasets validate the effectiveness of our hybrid approach, demonstrating its superior performance over existing implicit field-based methods in challenging scenarios. HERO-SLAM provides a new pathway to enhance the stability, performance, and applicability of neural SLAM in real-world scenarios. Code is available on the project page: https://hero-slam.github.io.