There is an increasing emphasis on achieving high accuracy and robustness in SLAM systems. The traditional visual-inertial simultaneous localization and mapping (VI-SLAM) system often struggles with stability under low light or dynamic illumination, leading to the potential loss of trajectory tracking. High accuracy and robustness are crucial for ensuring the long-term stability and reliable localization performance of SLAM systems. To address the challenges of improving robustness and accuracy in VI-SLAM, this article proposes SuperVINS, a real-time VI-SLAM framework designed for challenging imaging conditions. In contrast to geometric modeling, deep learning-based features are capable of fully leveraging the implicit information present in images, which is often not captured by geometric features. Therefore, SuperVINS, developed as an enhancement of VINS-Fusion, integrates the deep learning neural network model SuperPoint for feature point extraction and loop closure detection. At the same time, a deep learning neural network LightGlue model for associating feature points is integrated into front-end feature matching. We employed the RANSAC algorithm for matching enhancement to improve robustness against outliers. In addition, SuperVINS enables flexibly integrated environment-specific SuperPoint bag-of-words (BOWs) models for improved loop closure detection. The system enables real-time localization and mapping. Experimental validation on the well-known EuRoC and UMA-VI datasets demonstrates that SuperVINS achieves comparable accuracy and robustness to other state-of-the-art VI-SLAM systems, particularly in the most challenging sequences. This article analyzes the advantages of SuperVINS in terms of accuracy, real-time performance, and robustness. To foster knowledge exchange in the field, we have publicly released the code associated with this article. The code is available at https://github.com/luohongk/SuperVINS.

In order to estimate both camera poses and dynamic object poses, the visual SLAMMOT method combines visual Simultaneous Localization and Mapping (SLAM) with Multiple Object Tracking (MOT). Many visual SLAMMOT methods represent dynamic objects as bounding boxes and point cloud clusters, which ignores the geometric properties of the object surfaces that can provide additional constraints. In this letter, we propose DynaMeshSLAM, a visual SLAMMOT method, which represents dynamic objects as mesh models to leverage intrinsic geometric properties. Firstly, DynaMeshSLAM fuses the mesh projection and the optical flow to achieve multi-level object data association. Secondly, a constrained mesh smoothing method is embedded into the visual SLAMMOT framework to adjust dynamic landmarks depending on both the smoothness of object mesh models and the projection error of mesh vertices. Thirdly, a bundle adjustment solution incorporating the deformation graph optimizes the states of dynamic objects, while ensuring the local rigidity of the smoothed mesh models. Experiments on the KITTI-Tracking dataset demonstrate that our method achieves state-of-the-art performance in both object tracking and object pose estimation.

Visual semantic SLAM integrates geometric measurements with semantic perception, making it widely applicable in autonomous driving and robotics. Semantic-assisted localization and dynamic object perception are two critical tasks in visual semantic SLAM. However, many existing state-of-the-art methods address only one of these tasks in isolation. To address issues of functional limitations and insufficient information utilization in a single framework, we propose a unified visual semantic SLAM framework, SDS-SLAM, which tightly couples static and dynamic semantic information to handle the motion estimation of both the camera and observed objects in driving scenarios. A multi-task network for driving perception is employed to extract semantic information, including drivable areas, lanes, and vehicles. Based on various information obtained, we propose semantic local ground manifolds (SLGMs) to represent the geometric structure and semantic features, enabling the online generation of a lightweight semantic map. Subsequently, we integrate SLGM-based constraints such as lane alignment and planar motion to promote camera and object pose estimation. We evaluated our method on the public KITTI dataset and self-collected real-world data. The results demonstrate that our method effectively perceives both dynamic and static semantic elements in driving scenarios, achieving high accuracy in estimating the poses of the camera and objects.

This paper proposes a unified mathematical framework for inertial measurement unit (IMU) preintegration in inertial-aided navigation system in different frames under different motion condition. The navigation state is precisely discretized as three parts: local increment, global state, and global increment. The global increment can be calculated in different frames such as local geodetic navigation frame and earth-centered-earth-fixed frame. The local increment which is referred as the IMU preintegration can be calculated under different assumptions according to the motion of the agent and the grade of the IMU. Thus, it more accurate and more convenient for online state estimation of inertial-integrated navigation system under different environment. Furthermore, the covariance propagation based on left perturbation is proposed for the first time, which is independent of the inputs of the gyroscope and accelerometer. Finally, we show the monotonicity of the uncertainty for determinant optimality criteria and R\'enyi entropy optimality criteria.