Traditional visual Simultaneous Localization and Mapping (SLAM) methods based on point features are often limited by strong static assumptions and texture information, resulting in inaccurate camera pose estimation and object localization.To address these challenges, we present SLAM , a novel semantic RGB-D SLAM system that can obtain accurate estimation of the camera pose and the 6DOF pose of other objects, resulting in complete and clean static 3D model mapping in dynamic environments. Our system makes full use of the point, line, and plane features in space to enhance the camera pose estimation accuracy. It combines the traditional geometric method with a deep learning method to detect both known and unknown dynamic objects in the scene. Moreover, our system is designed with a three-mode mapping method, including dense, semi-dense, and sparse, where the mode can be selected according to the needs of different tasks. This makes our visual SLAM system applicable to diverse application areas. Evaluation in the TUM RGB-D and Bonn RGB-D datasets demonstrates that our SLAM system achieves the most advanced localization accuracy and the cleanest static 3D mapping of the scene in dynamic environments, compared to state-of-the-art methods. Specifically, our system achieves a root mean square error (RMSE) of 0.018 m in the highly dynamic TUM w/half sequence, outperforming ORB-SLAM3 (0.231 m) and DRG-SLAM (0.025 m). In the Bonn dataset, our system demonstrates superior performance in 14 out of 18 sequences, with an average RMSE reduction of 27.3% compared to the next best method.

The paper presents a vision-based obstacle avoidance strategy for lightweight self-driving cars that can be run on a CPU-only device using a single RGB-D camera. The method consists of two steps: visual perception and path planning. The visual perception part uses ORBSLAM3 enhanced with optical flow to estimate the car’s poses and extract rich texture information from the scene. In the path planning phase, the proposed method employs a method combining a control Lyapunov function and control barrier function in the form of a quadratic program (CLF-CBF-QP) together with an obstacle shape reconstruction process (SRP) to plan safe and stable trajectories. To validate the performance and robustness of the proposed method, simulation experiments were conducted with a car in various complex indoor environments using the Gazebo simulation environment. The proposed method can effectively avoid obstacles in the scenes. The proposed algorithm outperforms benchmark algorithms in achieving more stable and shorter trajectories across multiple simulated scenes.

This work focuses on proposing a dynamic vSLAM algorithm for dense object detection. Percentage thresholding and elimination of static boxes within the dynamic box are used to retain static keypoints as much as possible.