Pose graph optimization is a special case of the simultaneous localization and mapping problem where the only variables to be estimated are pose variables and the only measurements are inter-pose constraints. The vast majority of pose graph optimization techniques are vertex based (variables are robot poses), but recent work has parameterized the pose graph optimization problem in a relative fashion (variables are the transformations between poses) that utilizes a minimum cycle basis to maximize the sparsity of the problem. We explore the construction of a cycle basis in an incremental manner while maximizing the sparsity. We validate an algorithm that constructs a sparse cycle basis incrementally and compare its performance with a minimum cycle basis. Additionally, we present an algorithm to approximate the minimum cycle basis of two graphs that are sparsely connected as is common in multi-agent scenarios. Lastly, the relative parameterization of pose graph optimization has been limited to using rigid body transforms on SE(2) or SE(3) as the constraints between poses. We introduce a methodology to allow for the use of lower-degree-of-freedom measurements in the relative pose graph optimization problem. We provide extensive validation of our algorithms on standard benchmarks, simulated datasets, and custom hardware.

翻译：胶片优化是同步定位和绘图问题的一个特殊实例,其中,唯一要估计的变量构成变量,而唯一的测量则是相互制约。绝大多数的表面图形优化技术都是基于顶点的(可变的为机器人配置),但最近的工作以相对的方式对表面图形优化问题进行了参数化(可变的为压力之间的转换),利用最低周期基础最大限度地扩大问题的广度。我们探索以递增的方式构建一个周期基础,同时尽量扩大宽度。我们验证一种算法,该算法以递增的方式构建一个微小的周期基础,并以最低周期为基础比较其性能。此外,我们提出了一个算法,以近似两个与多剂情景中常见的少许连接的图表的最低周期基础。最后,表面图形优化的相对参数化限于使用SE(2)或SE(3)的硬体变形体作为压力之间的制约。我们引入了一种方法,允许在相对面面图优化问题中使用低度自由度测量。我们在标准基准、模量和软度数据设置上对我们的算法进行了广泛的验证。