SuperPoint-SLAM3: Augmenting ORB-SLAM3 with Deep Features, Adaptive NMS, and Learning-Based Loop Closure (2506.13089v1)

Abstract: Visual simultaneous localization and mapping (SLAM) must remain accurate under extreme viewpoint, scale and illumination variations. The widely adopted ORB-SLAM3 falters in these regimes because it relies on hand-crafted ORB keypoints. We introduce SuperPoint-SLAM3, a drop-in upgrade that (i) replaces ORB with the self-supervised SuperPoint detector--descriptor, (ii) enforces spatially uniform keypoints via adaptive non-maximal suppression (ANMS), and (iii) integrates a lightweight NetVLAD place-recognition head for learning-based loop closure. On the KITTI Odometry benchmark SuperPoint-SLAM3 reduces mean translational error from 4.15% to 0.34% and mean rotational error from 0.0027 deg/m to 0.0010 deg/m. On the EuRoC MAV dataset it roughly halves both errors across every sequence (e.g., V2_03: 1.58% -> 0.79%). These gains confirm that fusing modern deep features with a learned loop-closure module markedly improves ORB-SLAM3 accuracy while preserving its real-time operation. Implementation, pretrained weights and reproducibility scripts are available at https://github.com/shahram95/SuperPointSLAM3.

• The paper integrates deep SuperPoint features to enhance robustness, reducing translational errors from 4.15% to 0.34% and rotational errors from 0.0027 to 0.001 deg/m.
• Adaptive Non-Maximal Suppression is applied to achieve a uniform keypoint distribution, which stabilizes tracking and minimizes drift.
• Experimental evaluations on KITTI and EuRoC datasets validate the enhanced system's superior performance under challenging lighting and motion conditions.

Enhancing Visual SLAM with SuperPoint Features and Adaptive Non-Maximal Suppression

The paper in review introduces a novel integration of deep learning techniques into ORB-SLAM3, a prominent framework in visual Simultaneous Localization and Mapping (SLAM). The enhancement, termed SuperPoint-SLAM3, substitutes ORB features with SuperPoint descriptors. Additional advancements include incorporating Adaptive Non-Maximal Suppression (ANMS) to optimize keypoint distribution and laying groundwork for learning-based loop closure mechanisms.

Contributions and Key Modifications

The authors primarily address the limitations of conventional ORB features, such as vulnerability to significant changes in scale, rotation, and lighting conditions. SuperPoint, a self-supervised neural network, is integrated into ORB-SLAM3, addressing these deficiencies by providing more robust and repeatable keypoints. Key contributions include:

• SuperPoint Feature Integration: This deep learning-based detector and descriptor enhances the ability to withstand geometric and photometric transformations, improving feature matching across diverse environments.
• Adaptive Non-Maximal Suppression (ANMS): By employing ANMS, the SLAM system gains an improved spatial distribution of keypoints, which is critical for maintaining tracking stability and reducing drift over time.
• Evaluation Metrics: Before and after modifications, the paper reports a decrease in translational error from 4.15% to 0.34% and rotational error from 0.0027 to 0.001 degrees/m on the KITTI dataset, validating the efficacy of the proposed adjustments.

Methodological Insights

SuperPoint's integration necessitated significant alterations in the ORB-SLAM3 architecture. Notably, tracking threads were revised to process descriptors as floating-point vectors rather than binary strings, which prompted a shift from Hamming to Euclidean distance for feature matching.

Following SuperPoint extraction, ANMS was applied to ensure uniform keypoint distribution. This process calculated a suppression radius for each keypoint based on its response strength relative to its neighbors, maximizing spatial distribution while retaining focus on high-quality features.

Experimental Validation

The enhancements were validated on the KITTI Odometry and EuRoC MAV datasets. The former showcased substantial improvements in reducing drift and enhancing localization accuracy. ANMS, compounded by robust deep learning descriptors, demonstrated marked improvements in trajectory estimation. Moreover, the EuRoC dataset further highlighted these improvements under challenging conditions such as dynamic lighting and rapid motion.

Implications and Future Directions

This work underscores the potential of leveraging deep learning methods to advance classical SLAM techniques, resulting in significant performance gains in challenging environments. However, the paper highlights several areas for future work:

• Loop Closure Mechanisms: The existing loop closure based on Bag-of-Words (BoW) is incompatible with SuperPoint descriptors, necessitating the exploration of learning-based recognition systems such as NetVLAD for enhanced performance.
• Computational Optimization: The computational demands of SuperPoint necessitate further enhancements for real-time performance, possibly through GPU acceleration or algorithmic optimizations.
• Extended Dataset Evaluations: Future studies could benefit from evaluating this system on multimodal and diverse datasets to better understand its limitations and strengths.

In conclusion, the integration of SuperPoint features and ANMS within ORB-SLAM3 exemplifies a significant step forward in visual SLAM systems. By infusing robust deep learning techniques, the authors have successfully expanded the system's applicability across a wider range of challenging scenarios, paving the way for the next generation of SLAM systems in robotics and computer vision.