2D Floor Plan Segmentation Based on Down-sampling
This work addresses floor plan segmentation for applications like reconstruction and robotics, but it appears incremental as it builds on existing methods with a focus on complexity reduction.
The paper tackles the problem of 2D floor plan segmentation in cluttered environments by proposing a down-sampling approach that reduces computational and implementation complexity, yielding promising results on both robot-generated and benchmark floor plans.
In recent years, floor plan segmentation has gained significant attention due to its wide range of applications in floor plan reconstruction and robotics. In this paper, we propose a novel 2D floor plan segmentation technique based on a down-sampling approach. Our method employs continuous down-sampling on a floor plan to maintain its structural information while reducing its complexity. We demonstrate the effectiveness of our approach by presenting results obtained from both cluttered floor plans generated by a vacuum cleaning robot in unknown environments and a benchmark of floor plans. Our technique considerably reduces the computational and implementation complexity of floor plan segmentation, making it more suitable for real-world applications. Additionally, we discuss the appropriate metric for evaluating segmentation results. Overall, our approach yields promising results for 2D floor plan segmentation in cluttered environments.