HERO-SLAM: Hybrid Enhanced Robust Optimization of Neural SLAM (2407.18813v1)

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.

• The paper introduces a hybrid method that integrates neural implicit fields with feature-metric optimization to enhance SLAM's robustness under challenging conditions.
• It employs a novel multiscale patch-based loss pipeline alongside state-of-the-art feature extraction to achieve superior depth accuracy and scene completeness.
• Extensive benchmarking on standard datasets shows HERO-SLAM's improved tracking performance and artifact-free 3D reconstructions in low-frequency and abrupt viewpoint scenarios.

HERO-SLAM: Hybrid Enhanced Robust Optimization of Neural SLAM

Introduction

Simultaneous Localization and Mapping (SLAM) remains an essential problem in robotics and computer vision—integral to applications ranging from autonomous driving to virtual reality. Traditional SLAM techniques have seen substantial advancements, yet the introduction of neural implicit field representations has marked a significant evolution in this domain. Despite recent progress, robustness under challenging conditions remains a prominent concern. The paper "HERO-SLAM: Hybrid Enhanced Robust Optimization of Neural SLAM" by Zhe Xin et al. addresses this by introducing HERO-SLAM, an innovative hybrid method that amalgamates neural implicit fields with feature-metric optimization to enhance SLAM robustness.

Key Contributions

The contributions of HERO-SLAM are multifaceted, primarily aiming to improve SLAM robustness in environments with challenging viewpoints and limited data availability. The authors propose:

1. Hybrid Approach: Integrating neural implicit fields with feature-metric optimization to enhance robustness, particularly under sudden viewpoint changes or sparse data conditions.
2. Multiscale Patch-Based Loss: Introducing a novel multiscale patch-based loss pipeline that computes warping among feature points, feature maps, and RGB-D pixels.
3. Comprehensive Benchmarking: Validating the hybrid approach on standard datasets, demonstrating superior performance over existing neural implicit field-based SLAM methods in challenging scenarios.

Methodology

The proposed HERO-SLAM framework has two pivotal components:

1. Neural Implicit Field SLAM: Utilizing multiresolution grids for implicit function representation to encode the 3D scene’s geometry and visual details. The implicit fields are optimized through pixel-wise photometric and depth supervision.
2. Feature-Metric Optimization: Enhancements are introduced via feature-metric methods, such as using SuperPoint for feature extraction and LightGlue for feature matching, which collectively optimize the correspondences between consecutive frames.

Detailed Experiments

Replica Dataset

Experiments on the Replica dataset demonstrate HERO-SLAM's enhanced capabilities in depth accuracy and scene completeness under low-frequency imaging conditions. Notable metrics include:

• Depth L1: HERO-SLAM achieves a mean depth error as low as 1.41 cm, outperforming baselines even under reduced imaging frequencies (i=5).
• Accuracy and Completeness: Similarly, HERO-SLAM reports improved 3D accuracy and completeness ratios, indicating robust reconstruction quality.

TUM RGB-D Dataset

On the TUM RGB-D dataset, known for real-world hand-held sequences with abrupt viewpoint changes, HERO-SLAM exhibits superior tracking performance. Its average trajectory errors are significantly lower compared to Co-SLAM, even when the image frequency is reduced.

ScanNet Dataset

Evaluations on the ScanNet dataset also underscore the robustness and efficiency of HERO-SLAM. Compared to NICE-SLAM and Co-SLAM, HERO-SLAM demonstrates better pose accuracy and smoother, artifact-free reconstructions.

Implications

The practical implications of HERO-SLAM are vast. The hybrid approach substantially enhances the SLAM framework's robustness, making it viable for real-world applications where data acquisition might be sporadic or bandwidth-limited. This advancement opens pathways for more reliable autonomous navigation and detailed 3D reconstructions in variable conditions.

Future Directions

Further research could explore incorporating dynamic scene understanding into HERO-SLAM, enhancing its applicability in environments with moving objects. Additionally, the integration of advanced neural architectures could further improve real-time performance and scalability.

Conclusion

HERO-SLAM represents a significant enhancement in the field of neural SLAM by integrating hybrid optimization approaches. The paper sets forth a robust and scalable paradigm, validated by extensive experimentation, marking a substantial step forward in SLAM robustness and applicability in diverse real-world environments.

For further details, the implementation code is available on the project page.