Loops are pervasive in robotics problems, appearing in mapping and localization, where one is interested in finding loop closure constraints to better approximate robot poses or other estimated quantities, as well as planning and prediction, where one is interested in the homotopy classes of the space through which a robot is moving. We generalize the standard topological definition of a loop to cases where a trajectory passes close to itself, but doesn't necessarily touch, giving a definition that is more practical for real robotics problems. This relaxation leads to new and useful properties of inexact loops, such as their ability to be partitioned into topologically connected sets closely matching the concept of a "loop closure", and the existence of simple and nonsimple loops. Building from these ideas, we introduce several ways to measure properties and quantities of inexact loops on a trajectory, such as the trajectory's "loop area" and "loop density", and use them to compare strategies for sampling representative inexact loops to build constraints in mapping and localization problems.
翻译:环形是机器人问题中普遍存在的,出现在绘图和定位中,人们感兴趣的是寻找环状封闭限制,以更好地接近机器人外形或其他估计数量,以及规划和预测,人们感兴趣的是机器人移动空间的同质层。我们将环形的标准地形定义概括到轨迹接近自身但不一定触动的情况,给出一个对于真正的机器人问题更实用的定义。这种放松导致不精确环状的新而有用的特性,例如它们能够被分割成与地形相连的组合,与“环状封闭”的概念和简单和非简单环形的存在密切匹配。我们从这些想法出发,提出几种方法来测量轨迹上不精确环形的特性和数量,例如轨迹“环形区域”和“环状密度 ”,并用它们来比较抽样代表不精确环状的战略,以便在绘图和本地化问题上形成制约。