With the aim of bridging the gap between high quality reconstruction and mobile robot motion planning, we propose an efficient system that leverages the concept of adaptive-resolution volumetric mapping, which naturally integrates with the hierarchical decomposition of space in an octree data structure. Instead of a Truncated Signed Distance Function (TSDF), we adopt mapping of occupancy probabilities in log-odds representation, which allows to represent both surfaces, as well as the entire free, i.e. observed space, as opposed to unobserved space. We introduce a method for choosing resolution -- on the fly -- in real-time by means of a multi-scale max-min pooling of the input depth image. The notion of explicit free space mapping paired with the spatial hierarchy in the data structure, as well as map resolution, allows for collision queries, as needed for robot motion planning, at unprecedented speed. We quantitatively evaluate mapping accuracy, memory, runtime performance, and planning performance showing improvements over the state of the art, particularly in cases requiring high resolution maps.
翻译:为了缩小高质量重建与移动机器人运动规划之间的差距,我们建议建立一个高效系统,利用适应性分辨率体积绘图的概念,自然地将空间分解与奥氏体数据结构的等级分解结合起来。我们采用对正数代表法中的占用概率进行绘图,允许以前所未有的速度代表两个表面,以及整个自由空间,即观测空间,而不是无观测空间。我们采用一种实时选择分辨率的方法,即实时选择分辨率 -- -- 即通过多尺寸的最大输入深度图像集成的方式。明确的自由空间绘图概念与数据结构的空间等级相匹配,以及地图分辨率,允许以前所未有的速度进行机器人运动规划所需的碰撞查询。我们量化地评价绘图的准确性、记忆、运行时性表现以及显示艺术状态改进的绩效规划,特别是在需要高分辨率地图的情况下。