This work develops a distributed optimization algorithm for multi-robot 3-D semantic mapping using streaming range and visual observations and single-hop communication. Our approach relies on gradient-based optimization of the observation log-likelihood of each robot subject to a map consensus constraint to build a common multi-class map of the environment. This formulation leads to closed-form updates which resemble Bayes rule with one-hop prior averaging. To reduce the amount of information exchanged among the robots, we utilize an octree data structure that compresses the multi-class map distribution using adaptive-resolution.

翻译：本工作开发了一种基于分布式优化的多机器人三维语义地图构建算法，利用流式距离观测与视觉观测以及单跳通信。该方法通过梯度优化每个机器人在地图一致性约束下的观测对数似然函数，构建环境的共享多类别地图。该公式导出了类似贝叶斯规则结合单跳先验平均的闭式更新方程。为减少机器人间信息交换量，我们采用八叉树数据结构，通过自适应分辨率压缩多类别地图分布。