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.

翻译：视觉同步定位与建图（SLAM）必须在极端视角、尺度及光照变化下保持准确性。广泛采用的ORB-SLAM3在这些场景中表现欠佳，因其依赖于手工设计的ORB关键点。本文提出SuperPoint-SLAM3，一种即插即用的升级方案，其核心改进包括：（i）使用自监督的SuperPoint检测器-描述符替代ORB特征，（ii）通过自适应非极大值抑制（ANMS）实现空间分布均匀的关键点提取，（iii）集成轻量级NetVLAD位置识别模块以实现基于学习的回环检测。在KITTI里程计基准测试中，SuperPoint-SLAM3将平均平移误差从4.15%降低至0.34%，平均旋转误差从0.0027度/米减少至0.0010度/米。在EuRoC MAV数据集上，所有序列的误差均缩减约一半（例如V2_03序列：1.58%→0.79%）。这些提升证实了将现代深度特征与学习型回环模块融合，能在保持ORB-SLAM3实时运行的同时显著提高其精度。相关实现、预训练权重及复现脚本已发布于https://github.com/shahram95/SuperPointSLAM3。