Neuro-Explorer: Efficient and Scalable Exploration Planning via Learned Frontier Regions

We present an efficient and scalable learning-based autonomous exploration system for mobile robots navi-gating unknown indoor environments. Our system incorporates three network models trained to identify the frontier region (FR), to evaluate the detected FR regions based on their proximity to the robot (A∗-Net), and to measure the coverage reward at the FR regions (Viz-Net). Our method employs an active window of the map that moves along with the robot, offering scalable exploration capabilities while maintaining a high rate of exploration coverage owing to the two exploratory measures utilized by A∗-Net (proximity) and Viz-Net (coverage). Consequently, Our system completes over 99% coverage in a large-scale benchmarking world, scaling up to 135m × +80m. In contrast, other state-of-the-art approaches completed only less than 40% of the same world with a 30% slower exploration speed than ours.