Autonomous exploration of unknown environments using a team of mobile robots demands distributed perception and planning strategies to enable efficient and scalable performance. Ideally, each robot should update its map and plan its motion not only relying on its own observations, but also considering the observations of its peers. Centralized solutions to multi-robot coordination are susceptible to central node failure and require a sophisticated communication infrastructure for reliable operation. Current decentralized active mapping methods consider simplistic robot models with linear-Gaussian observations and Euclidean robot states. In this work, we present a distributed multi-robot mapping and planning method, called Riemannian Optimization for Active Mapping (ROAM). We formulate an optimization problem over a graph with node variables belonging to a Riemannian manifold and a consensus constraint requiring feasible solutions to agree on the node variables. We develop a distributed Riemannian optimization algorithm that relies only on one-hop communication to solve the problem with consensus and optimality guarantees. We show that multi-robot active mapping can be achieved via two applications of our distributed Riemannian optimization over different manifolds: distributed estimation of a 3-D semantic map and distributed planning of SE(3) trajectories that minimize map uncertainty. We demonstrate the performance of ROAM in simulation and real-world experiments using a team of robots with RGB-D cameras.

翻译：利用移动机器人团队自主探索未知环境需要分布式感知与规划策略，以实现高效且可扩展的性能。理想情况下，每个机器人不仅应依赖自身观测更新地图并规划运动，还需考虑同伴的观测信息。多机器人协调的集中式方案易受中央节点故障影响，且需要复杂的通信基础设施以保障可靠运行。当前分布式主动建图方法通常采用带有线性-高斯观测和欧几里得机器人状态的简化模型。本文提出一种名为"主动建图黎曼优化"的分布式多机器人建图与规划方法。我们构建了一个优化问题，其节点变量属于黎曼流形，并引入一致性约束要求可行解在节点变量上达成一致。我们开发了一种仅依赖单跳通信的分布式黎曼优化算法，在保证一致性和最优性的前提下求解该问题。研究表明，通过在不同流形上两次应用分布式黎曼优化即可实现多机器人主动建图：三维语义地图的分布式估计，以及最小化地图不确定性的SE(3)轨迹分布式规划。我们通过搭载RGB-D相机的机器人团队在仿真与实物实验中验证了ROAM的性能。