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

TL;DR

SuperPoint-SLAM3 enhances ORB-SLAM3 by replacing hand-crafted features with deep learning-based features, enforcing uniform keypoints, and adding learned loop closure, significantly improving accuracy on benchmark datasets while maintaining real-time performance.

Contribution

It introduces a modular upgrade to ORB-SLAM3 that integrates deep features, adaptive keypoint selection, and learned loop closure for robust SLAM under challenging conditions.

Findings

Significantly reduces translational and rotational errors on KITTI and EuRoC datasets.

Maintains real-time operation despite deep feature integration.

Demonstrates marked accuracy improvements over traditional ORB-SLAM3.

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.