In order to possess a significant degree of autonomy, a robot must be able to perceive its environment and store a representation of that environment for use in tasks such as localisation, navigation, collision avoidance, and higher decision making. It must do this subject to constraints on memory and processing power typical of the embedded computer systems commonly found on small robotic devices. These constraints are particularly important for flying robots (i.e. unmanned aerial vehicles), for which weight must be minimised. The challenge of storing a detailed map of a large area on a small embedded computer has led to the development of many algorithms that exploit the sparsity of typical maps to create a more memory-efficient representation. In this paper, we demonstrate that the verticality of both natural and man-made structures can be exploited to create a framework that can store occupancy grid maps efficiently, without causing additional computational burden. The new framework achieves an order-of-magnitude reduction in memory footprint relative to widely-used occupancy grid mapping software, while also achieving a slight speed-up in map insertion and access times. We also make available LIDAR scans taken from a hexacopter of an indoor flight arena that can be used to assist in evaluating future mapping and SLAM developments.

为了拥有高度的自主权，机器人必须能够感知其环境并存储该环境的表示，以用于诸如定位，导航，避免碰撞和更高的决策等任务。它必须遵守通常在小型机器人设备上常见的嵌入式计算机系统的内存和处理能力的限制，才能做到这一点。这些限制对于必须最小化重量的飞行机器人（即无人飞行器）尤其重要。将大型区域的详细地图存储在小型嵌入式计算机上的挑战导致许多算法的开发，这些算法利用典型地图的稀疏性来创建内存效率更高的表示形式。在本文中，我们证明了可以利用自然结构和人造结构的垂直性来创建一个框架，该框架可以有效地存储占用网格图，而不会引起额外的计算负担。与广泛使用的占用网格映射软件相比，新框架实现了内存占用量的数量级减少，同时还略微加快了地图插入和访问时间。我们还提供了从室内飞行场的六旋翼飞机上获取的LIDAR扫描，可用于帮助评估未来的制图和SLAM发展。同时也略微加快了地图的插入和访问时间。我们还提供了从室内飞行场的六旋翼飞机上获取的LIDAR扫描，可用于帮助评估未来的制图和SLAM发展。同时也略微加快了地图的插入和访问时间。我们还提供了从室内飞行场的六旋翼飞机上获取的LIDAR扫描，可用于帮助评估未来的制图和SLAM发展。