Reinforcement Learning with Frontier-Based Exploration via Autonomous Environment

Understand

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.