Active Simultaneous Localisation and Mapping (SLAM) is a critical problem in autonomous robotics, enabling robots to navigate to new regions while building an accurate model of their surroundings. Visual SLAM is a popular technique that uses virtual elements to enhance the experience. However, existing frontier-based exploration strategies can lead to a non-optimal path in scenarios where there are multiple frontiers with similar distance. This issue can impact the efficiency and accuracy of Visual SLAM, which is crucial for a wide range of robotic applications, such as search and rescue, exploration, and mapping. To address this issue, this research combines both an existing Visual-Graph SLAM known as ExploreORB with reinforcement learning. The proposed algorithm allows the robot to learn and optimize exploration routes through a reward-based system to create an accurate map of the environment with proper frontier selection. Frontier-based exploration is used to detect unexplored areas, while reinforcement learning optimizes the robot's movement by assigning rewards for optimal frontier points. Graph SLAM is then used to integrate the robot's sensory data and build an accurate map of the environment. The proposed algorithm aims to improve the efficiency and accuracy of ExploreORB by optimizing the exploration process of frontiers to build a more accurate map. To evaluate the effectiveness of the proposed approach, experiments will be conducted in various virtual environments using Gazebo, a robot simulation software. Results of these experiments will be compared with existing methods to demonstrate the potential of the proposed approach as an optimal solution for SLAM in autonomous robotics.

翻译：主动同时定位与地图构建（SLAM）是自主机器人领域的关键问题，使机器人能够在构建周围环境精确模型的同时导航至新区域。视觉SLAM是一种利用虚拟元素增强体验的流行技术。然而，现有基于边界探索的策略在存在多个距离相似的边界场景时可能导致非最优路径。这一问题会影响视觉SLAM的效率与精度，而这对搜索救援、探索与地图构建等广泛机器人应用至关重要。为解决此问题，本研究将名为ExploreORB的现有视觉-图SLAM方法与强化学习相结合。所提算法使机器人能够通过基于奖励的系统学习并优化探索路径，通过恰当的边界选择构建精确的环境地图。边界探索用于检测未探索区域，而强化学习通过为最优边界点分配奖励来优化机器人运动。随后采用图SLAM整合机器人传感数据并构建精确的环境地图。该算法旨在通过优化边界探索过程以构建更精确的地图，提升ExploreORB的效率与精度。为评估所提方法的有效性，将使用机器人仿真软件Gazebo在多种虚拟环境中进行实验。实验结果将与现有方法对比，以证明所提方法作为自主机器人SLAM最优解决方案的潜力。