Autonomous robot operation in unstructured and unknown environments requires efficient techniques for mapping and exploration using streaming range and visual observations. Information-based exploration techniques, such as Cauchy-Schwarz quadratic mutual information (CSQMI) and fast Shannon mutual information (FSMI), have successfully achieved active binary occupancy mapping with range measurements. However, as we envision robots performing complex tasks specified with semantically meaningful concepts, it is necessary to capture semantics in the measurements, map representation, and exploration objective. This work presents Semantic octree mapping and Shannon Mutual Information (SSMI) computation for robot exploration. We develop a Bayesian multi-class mapping algorithm based on an octree data structure, where each voxel maintains a categorical distribution over semantic classes. We derive a closed-form efficiently-computable lower bound of the Shannon mutual information between a multi-class octomap and a set of range-category measurements using semantic run-length encoding of the sensor rays. The bound allows rapid evaluation of many potential robot trajectories for autonomous exploration and mapping. We compare our method against state-of-the-art exploration techniques and apply it in a variety of simulated and real-world experiments.

翻译：在非结构化未知环境中自主机器人操作需要利用流式距离与视觉观测的高效建图和探索技术。基于信息的探索方法（如柯西-施瓦茨二次互信息（CSQMI）和快速香农互信息（FSMI））已成功实现使用距离测量进行主动二值占据建图。然而，随着我们设想机器人执行需要语义有意义概念定义的复杂任务，必须在测量数据、地图表示和探索目标中捕获语义信息。本文提出用于机器人探索的语义八叉树建图与香农互信息（SSMI）计算方法。我们开发了一种基于八叉树数据结构的贝叶斯多类建图算法，其中每个体素维持语义类别的分类分布。通过传感器射线的语义行程长度编码，我们推导了多类八叉地图与一组距离-类别测量之间的香农互信息的闭式高效可计算下界。该下界能快速评估用于自主探索和建图的多种潜在机器人轨迹。我们将该方法与最先进的探索技术进行对比，并在多种仿真和真实实验中验证其性能。