Leveraging structural line features to complement sparse point features has been studied in recent years. However, this approach relies on a Manhattan world assumption and does not incorporate non-structural lines due to the triangulation degeneracy problem and tracking process instability. To address these problems, we propose a general line-based SLAM system that combines points, structural and non-structural lines. First, an efficient line matching algorithm for multi-scale is designed to obtain more accurate matching pairs through a divide-and-conquer approach. In addition, a novel line triangulation strategy utilizing spatial-temporal consistency and degeneracy identification is proposed to improve the quality of line generation in a sliding window. Finally, universal structural constraints based on measurement of the vanishing directions are implemented to complement the information missing from the Plücker line projection in local mapping optimization. Extensive experiments are conducted on the public EuRoC and TUM datasets as well as a self-collected dataset, and the results show that UL-SLAM achieves cutting-edge performance among recent state-of-the-art methods in both accuracy and speed. Ablation experiments also demonstrate that the integration of different line features can improve the robustness and accuracy of a visual SLAM system in challenging scenarios with low texture and weak illumination. Our implementation of the UL-SLAM will be open-sourced to benefit the community (https://github.com/jhch1995/UL-SLAM).Note to Practitioners—This article was motivated by the challenges of visual localization problems in human-made indoor scenes. Visual localization has been widely used in various robotic fields such as self-driving vehicles, augmented reality (AR), and virtual reality (VR). In real-world scenes, the localization accuracy will be significantly decreased because of the sparse and uncertain visual features in low-texture or weak illumination environments, which reduces the robustness of robot tracking. To address this problem, this article proposes a novel universal line-based SLAM system (UL-SLAM) that unifies structural and non-structural constraints within a general framework unrestricted by the strong global Manhattan world assumption. UL-SLAM can not only improve the accuracy of pose estimation due to the proposed methods for the line features but also achieve real-time performance. In addition, for 3D mapping construction, UL-SLAM can also enrich the geometric structure information of the indoor scenes. Extensive experiments are conducted on various indoor datasets for autonomous robots, and the results demonstrate the efficiency, accuracy, and robustness of the proposed system in different complex scenarios.

3D reconstruction has been widely used in autonomous navigation fields of mobile robotics. However, the former research can only provide the basic geometry structure without the capability of open-world scene understanding, limiting advanced tasks like human interaction and visual navigation. Moreover, traditional 3D scene understanding approaches rely on expensive labeled 3D datasets to train a model for a single task with supervision. Thus, geometric reconstruction with zero-shot scene understanding i.e. Open vocabulary 3D Understanding and Reconstruction, is crucial for the future development of mobile robots. In this paper, we propose OpenOcc, a novel framework unifying the 3D scene reconstruction and open vocabulary understanding with neural radiance fields. We model the geometric structure of the scene with occupancy representation and distill the pre-trained open vocabulary model into a 3D language field via volume rendering for zero-shot inference. Furthermore, a novel semantic-aware confidence propagation (SCP) method has been proposed to relieve the issue of language field representation degeneracy caused by inconsistent measurements in distilled features. Experimental results show that our approach achieves competitive performance in 3D scene understanding tasks, especially for small and long-tail objects.

Achieving robust and precise pose estimation in dynamic scenes is a significant research challenge in Visual Simultaneous Localization and Mapping (SLAM). Recent advancements integrating Gaussian Splatting into SLAM systems have proven effective in creating high-quality renderings using explicit 3D Gaussian models, significantly improving environmental reconstruction fidelity. However, these approaches depend on a static environment assumption and face challenges in dynamic environments due to inconsistent observations of geometry and photometry. To address this problem, we propose DG-SLAM, the first robust dynamic visual SLAM system grounded in 3D Gaussians, which provides precise camera pose estimation alongside high-fidelity reconstructions. Specifically, we propose effective strategies, including motion mask generation, adaptive Gaussian point management, and a hybrid camera tracking algorithm to improve the accuracy and robustness of pose estimation. Extensive experiments demonstrate that DG-SLAM delivers state-of-the-art performance in camera pose estimation, map reconstruction, and novel-view synthesis in dynamic scenes, outperforming existing methods meanwhile preserving real-time rendering ability.

Training a classifier based on imbalanced data set is considered a great challenge in classification tasks, as classifiers are often "biased" due to highly skewed data distribution and overlapping borderline between different classes. When the imbalanced data appears in the multi-class classification scenario, the classification difficulty increases exponentially. In this paper, we propose an integrated approach to handle imbalanced multi-class classification by combining the population-based sampling method and a multi-expert classifier. In the implementation, we choose the Ant Colony Optimization to realize the sampling process. As for the classifier, the voting mechanism is applied to intensify the weak classifiers. To test the algorithm’s performance, we choose 10 representative imbalanced multi-class data sets from the UCI Machine Learning Repository. G – mean and mAUC are chosen as the metrics. According to the experimental results, the proposed algorithm dominates in 8 data sets and gets a second place in 1 data set when evaluated by G – mean, and ranks first in 3 data sets and top-3 among the most for mAUC.