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 emergence of 3D Gaussian Splatting (3DGS) has recently sparked a renewed wave of dense visual SLAM research. However, current methods face challenges such as sensitivity to artifacts and noise, sub-optimal selection of training viewpoints, and a lack of light global optimization. In this paper, we propose a dense SLAM system that tightly couples 3DGS with ORB features. We design a joint optimization approach for robust tracking and effectively reducing the impact of noise and artifacts. This involves combining novel geometric observations, derived from accumulated transmittance, with ORB features extracted from pixel data. Furthermore, to improve mapping quality, we propose an adaptive Gaussian expansion and regularization method that enables Gaussian primitives to represent the scene compactly. This is coupled with a viewpoint selection strategy based on the hybrid graph to mitigate over-fitting effects and enhance convergence quality. Finally, our approach achieves compact and high-quality scene representations and accurate localization. GSORB-SLAM has been evaluated on different datasets, demonstrating outstanding performance. The code will be available.

Accurate and robust camera tracking in dynamic environments presents a significant challenge for visual SLAM (Simultaneous Localization and Mapping). Recent progress in this field often involves the use of deep learning techniques to generate mask for dynamic objects, which usually require GPUs to operate in real-time (30 fps). Therefore, this paper proposes a novel visual SLAM system for dynamic environments that obtains real-time performance on CPU by incorporating a mask prediction mechanism, which allows the deep learning method and the camera tracking to run entirely in parallel at different frequencies such that neither waits for the result from the other. Based on this, it further introduces a dual-stage optical flow tracking approach and employs a hybrid usage of optical flow and ORB features, which significantly enhance the efficiency and robustness of the system. Compared with state-of-the-art methods, this system maintains high localization accuracy in dynamic environments while achieving a tracking frame rate of 56 fps on a single laptop CPU without any hardware acceleration, thus proving that deep learning methods are still feasible for dynamic SLAM even without GPU support. Based on the available information, this is the first SLAM system to achieve this.

In the proposed study, we describe an approach to improving the computational efficiency and robustness of visual SLAM algorithms on mobile robots with multiple cameras and limited computational power by implementing an intermediate layer between the cameras and the SLAM pipeline. In this layer, the images are classified using a ResNet18-based neural network regarding their applicability to the robot localization. The network is trained on a six-camera dataset collected in the campus of the Skolkovo Institute of Science and Technology (Skoltech). For training, we use the images and ORB features that were successfully matched with subsequent frame of the same camera ("good" keypoints or features). The results have shown that the network is able to accurately determine the optimal images for ORB-SLAM2, and implementing the proposed approach in the SLAM pipeline can help significantly increase the number of images the SLAM algorithm can localize on, and improve the overall robustness of visual SLAM. The experiments on operation time state that the proposed approach is at least 6 times faster compared to using ORB extractor and feature matcher when operated on CPU, and more than 30 times faster when run on GPU. The network evaluation has shown at least 90% accuracy in recognizing images with a big number of "good" ORB keypoints. The use of the proposed approach allowed to maintain a high number of features throughout the dataset by robustly switching from cameras with feature-poor streams.

Learning to safely navigate in unknown environments is an important task for autonomous drones used in surveillance and rescue operations. In recent years, a number of learning-based Simultaneous Localisation and Mapping (SLAM) systems relying on deep neural networks (DNNs) have been proposed for applications where conventional feature descriptors do not perform well. However, such learning-based SLAM systems rely on DNN feature encoders trained offline in typical deep learning settings. This makes them less suited for drones deployed in environments unseen during training, where continual adaptation is paramount. In this paper, we present a new method for learning to SLAM on the fly in unknown environments, by modulating a low-complexity Dictionary Learning and Sparse Coding (DLSC) pipeline with a newly proposed Quadratic Bayesian Surprise (QBS) factor. We experimentally validate our approach with data collected by a drone in a challenging warehouse scenario, where the high number of ambiguous scenes makes visual disambiguation hard.

When there is a suspicious figure reuse case in science, research integrity investigators often find it difficult to rebut authors claiming that "it happened by chance". In other words, when there is a "collision" of image features, it is difficult to justify whether it appears rarely or not. In this article, we provide a method to predict the rarity of an image feature by statistically estimating the chance of it randomly occurring across all scientific imagery. Our method is based on high-dimensional density estimation of ORB features using 7+ million images in the PubMed Open Access Subset dataset. We show that this method can lead to meaningful feedback during research integrity investigations by providing a null hypothesis for scientific image reuse and thus a p-value during deliberations. We apply the model to a sample of increasingly complex imagery and confirm that it produces decreasingly smaller p-values as expected. We discuss applications to research integrity investigations as well as future work.