SuperVINS: A Real-Time Visual-Inertial SLAM Framework for Challenging Imaging Conditions (2407.21348v2)

Abstract: The traditional visual-inertial SLAM system often struggles with stability under low-light or motion-blur conditions, leading to potential lost of trajectory tracking. High accuracy and robustness are essential for the long-term and stable localization capabilities of SLAM systems. Addressing the challenges of enhancing robustness and accuracy in visual-inertial SLAM, this paper propose SuperVINS, a real-time visual-inertial SLAM framework designed for challenging imaging conditions. In contrast to geometric modeling, deep learning features are capable of fully leveraging the implicit information present in images, which is often not captured by geometric features. Therefore, SuperVINS, developed as an enhancement of VINS-Fusion, integrates the deep learning neural network model SuperPoint for feature point extraction and loop closure detection. At the same time, a deep learning neural network LightGlue model for associating feature points is integrated in front-end feature matching. A feature matching enhancement strategy based on the RANSAC algorithm is proposed. The system is allowed to set different masks and RANSAC thresholds for various environments, thereby balancing computational cost and localization accuracy. Additionally, it allows for flexible training of specific SuperPoint bag of words tailored for loop closure detection in particular environments. The system enables real-time localization and mapping. Experimental validation on the well-known EuRoC dataset demonstrates that SuperVINS is comparable to other visual-inertial SLAM system in accuracy and robustness across the most challenging sequences. This paper analyzes the advantages of SuperVINS in terms of accuracy, real-time performance, and robustness. To facilitate knowledge exchange within the field, we have made the code for this paper publicly available.

• The paper presents a SLAM framework that integrates deep learning for enhanced feature extraction, matching, and loop closure.
• It leverages SuperPoint and LightGlue to boost feature detection and refine correspondence, significantly reducing trajectory errors.
• Extensive EuRoC experiments demonstrate that SuperVINS outperforms classical methods in low illumination and rapid motion scenarios.

An Analysis of "SuperVINS: A Visual-Inertial SLAM Framework Integrated with Deep Learning Features"

The paper "SuperVINS: A Visual-Inertial SLAM Framework Integrated Deep Learning Features" presents a compelling evolution of the VINS-Fusion system by integrating advanced deep learning features for enhanced performance in Simultaneous Localization and Mapping (SLAM) tasks. The contributions are significant, addressing both theoretical and practical challenges in SLAM, particularly in scenarios characterized by poor lighting and rapid motion.

At its core, SuperVINS aims to overcome limitations of traditional geometric feature-based SLAM systems, which often fail in complex environments such as those with low illumination or high dynamic motion. The paper argues convincingly that while classical methods like VINS-mono and ORB-SLAM2 set benchmarks in SLAM, their reliance on low-level geometric features inherently constrains their robustness and accuracy in extreme conditions.

System Framework and Methodology

SuperVINS integrates deep learning techniques at multiple stages of the SLAM pipeline—specifically in feature extraction, feature matching, and loop closure detection.

Feature Extraction:

The authors leverage the SuperPoint network to extract feature points and descriptors. SuperPoint, designed to work effectively across various scenes, surpasses traditional geometric approaches by encapsulating higher-level understanding of image features. The network is structured as an encoder-decoder, with a loss function combining both feature point detection and descriptor generation tasks.

Feature Matching:

The feature matching process employs LightGlue, a lightweight feature matching network that applies self-attention and cross-attention mechanisms to refine feature correspondence between images. The key advancement here is the introduction of a RANSAC-based optimization strategy to reduce mismatches, enhancing the robustness of feature matching.

Loop Closure:

SuperVINS enhances loop closure detection using a deep learning-based bag-of-words (BoW) model via DBoW3, which is optimized for real-time performance. This integration, coupled with efficient SuperPoint descriptors, significantly bolsters the localization accuracy by accurately recognizing previously visited locations.

Experimental Validation

The paper demonstrates its improvements through extensive experiments on the EuRoC data set, known for its diversity and complexity. Comparative analysis with VINS-Fusion reveals that SuperVINS excels in several key performance metrics, namely Absolute Trajectory Error (ATE), relative rotation error (RPEr), and relative translation error (RPEt). For instance, in challenging conditions like the MH05 and V203 sequences, SuperVINS achieved a 39.6% and 12.3% improvement in ATE, respectively, over VINS-Fusion. Additionally, in scenarios characterized by rapid motion and low texture, SuperVINS maintained superior tracking robustness where VINS-Fusion experienced failures.

Performance Insights

Position Accuracy:

Out of 11 sequences in the EuRoC dataset, SuperVINS outperformed VINS-Fusion in six sequences. Notably, in high-difficulty environments, the accuracy improvements were more pronounced. This indicates the effectiveness of deep learning features in capturing and preserving crucial image details that traditional methods might overlook.

Robustness:

SuperVINS demonstrated enhanced robustness, managing to maintain tracking even in sequences where VINS-Fusion failed. This robustness can be attributed to the system's ability to adaptively learn and leverage high-level features beyond geometric constraints.

Comparative Evaluation

The paper also included a comparison with other state-of-the-art frameworks like OKVIS, VINS-Mono, and different configurations of VINS-Fusion. SuperVINS showed competitive performance, particularly in sequences with extreme conditions. Although the improvement was not consistent across all sequences, the integration of deep learning offers a promising direction for future SLAM research.

Future Prospects

The implications of this research are twofold. Practically, the integration of deep learning into SLAM systems can lead to more robust and accurate autonomous navigation solutions suitable for a variety of challenging environments. Theoretically, it opens avenues for further exploration into hybrid SLAM systems that balance traditional geometric techniques with contemporary deep learning approaches. Future work could explore optimizing real-time performance and extending the evaluation to more diverse datasets, ensuring generalizability across different operational contexts.

In summary, the paper "SuperVINS: A Visual-Inertial SLAM Framework Integrated Deep Learning Features" marks a significant stride in SLAM research, blending the robustness of deep learning with the precision of traditional algorithms. By addressing both accuracy and robustness in challenging conditions, it presents a formidable SLAM solution that could spur continued advancements and refinements in the field.