Semantic OcTree Mapping and Shannon Mutual Information Computation for Robot Exploration
Semantic OcTree Mapping and Shannon Mutual Information Computation for Robot Exploration
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DOI：
10.1109/tro.2023.3245986
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发表时间：
2021-12
影响因子：
7.8
通讯作者：
Arash Asgharivaskasi;Nikolay A. Atanasov
中科院分区：
文献类型：
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作者：
Arash Asgharivaskasi;Nikolay A. Atanasov
在非结构化和未知环境中自主操作的机器人需要使用流范围和视觉观察进行有效的绘图和探索技术。基于信息的勘探技术，如Cauchy-Schwarz二次互信息和快速Shannon互信息，已经成功地实现了具有距离测量的主动二进制占用映射。然而，当我们设想机器人执行具有语义意义概念的复杂任务时，有必要在测量、地图表示和探索目标中捕获语义。本文提出了用于机器人探索的语义八叉树映射和Shannon互信息计算。我们开发了一种基于八叉树数据结构的贝叶斯多类映射算法，其中每个体素在语义类上保持分类分布。我们利用传感器射线的语义游程编码，导出了多类八元映射与距离-类别测量集之间的Shannon互信息的闭形式高效可计算下界。该边界允许快速评估许多潜在的机器人轨迹，用于自主探索和绘图。我们将我们的方法与最先进的勘探技术进行比较，并将其应用于各种模拟和现实世界的实验中。
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 and fast Shannon mutual information, 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 computation for robot exploration. We develop a Bayesian multiclass 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 multiclass 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.