We propose a scheme called MuNES for single mapping and trajectory planning including elevators and stairs. Optimized multifloor trajectories are important for optimal interfloor movements of robots. However, given two or more options of moving between floors, it is difficult to select the best trajectory because there are no suitable indoor multifloor maps in the existing methods. To solve this problem, MuNES creates a single multifloor map including elevators and stairs by estimating altitude changes based on pressure data. In addition, the proposed method performs floor-based loop detection for faster and more accurate loop closure. The single multifloor map is then voxelized leaving only the parts needed for trajectory planning. An optimal and realistic multifloor trajectory is generated by exploring the voxels using an A* algorithm based on the proposed cost function, which affects realistic factors. We tested this algorithm using data acquired from around a campus and note that a single accurate multifloor map could be created. Furthermore, optimal and realistic multifloor trajectory could be found by selecting the means of motion between floors between elevators and stairs according to factors such as the starting point, ending point, and elevator waiting time. The code and data used in this work are available at https://github.com/donghwijung/MuNES.

翻译：本文提出一种名为MuNES的方案，用于包含电梯与楼梯的单体地图构建与轨迹规划。优化多楼层轨迹对实现机器人最优的跨楼层移动至关重要。然而，当存在两种或多种跨楼层移动方案时，由于现有方法缺乏适用的室内多楼层地图，难以选择最优轨迹。为解决该问题，MuNES通过基于气压数据估算高度变化，创建包含电梯与楼梯的单一多楼层地图。此外，该方法采用基于楼层的闭环检测技术，实现更快且更精准的闭环校正。随后将单一多楼层地图体素化，仅保留轨迹规划所需的区域。基于所提出的代价函数（该函数影响实际因素），利用A*算法探索体素以生成最优且符合实际的多楼层轨迹。我们使用校园采集数据测试该算法，结果表明可构建精准的单一多楼层地图。同时，通过根据起点、终点及电梯等待时间等因素选择电梯或楼梯作为跨楼层运动方式，能够找到最优且符合实际的多楼层轨迹。本工作所用代码与数据见https://github.com/donghwijung/MuNES。