面向机器人探测的语义OcTree映射和Shannon互信息计算
Semantic OcTree Mapping and Shannon Mutual Information Computation for Robot Exploration
摘要：
在非结构化和未知环境中的自主机器人操作需要使用流式范围和视觉观察进行映射和探索的有效技术。基于信息的探测技术，如柯西-施瓦茨二次互信息（CSQMI）和快速香农互信息（FSMI），已经成功地实现了主动二进制占用映射与距离测量。然而，当我们设想机器人执行复杂的任务，指定语义有意义的概念，它是必要的，以捕捉语义的测量，地图表示，和探索的目标。这项工作提出了语义八叉树映射和香农互信息（SSMI）计算机器人探索。我们开发了一个贝叶斯多类映射算法的基础上八叉树数据结构，其中每个体素保持一个分类分布的语义类。我们推导出一个封闭形式的有效计算的香农互信息之间的一个多类八元映射和一组范围类别的测量使用语义行程长度编码的传感器射线的下限。该界限允许快速评估许多潜在的机器人轨迹的自主探索和映射。我们将我们的方法与最先进的探索技术进行比较，并将其应用于各种模拟和真实世界的实验中。
英文摘要：
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.
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