Reliable localization is an essential capability for marine robots navigating in GPS-denied environments. SLAM, commonly used to mitigate dead reckoning errors, still fails in feature-sparse environments or with limited-range sensors. Pose estimation can be improved by incorporating the uncertainty prediction of future poses into the planning process and choosing actions that reduce uncertainty. However, performing belief propagation is computationally costly, especially when operating in large-scale environments. This work proposes a computationally efficient planning under uncertainty frame-work suitable for large-scale, feature-sparse environments. Our strategy leverages SLAM graph and occupancy map data obtained from a prior exploration phase to create a virtual map, describing the uncertainty of each map cell using a multivariate Gaussian. The virtual map is then used as a cost map in the planning phase, and performing belief propagation at each step is avoided. A receding horizon planning strategy is implemented, managing a goal-reaching and uncertainty-reduction tradeoff. Simulation experiments in a realistic underwater environment validate this approach. Experimental comparisons against a full belief propagation approach and a standard shortest-distance approach are conducted.

翻译：可靠定位是海洋机器人在无GPS环境中导航的关键能力。常用于抑制航位推算误差的SLAM技术在特征稀疏环境或传感器量程受限时仍会失效。通过将未来位姿的不确定性预测纳入规划过程并选择降低不确定性的动作，可改善位姿估计。然而，信念传播的计算成本较高，尤其在大型环境中运行时更为突出。本文提出一种适用于大规模特征稀疏环境的高计算效率不确定性规划框架。该策略利用前期探索阶段获得的SLAM图与占据栅格数据创建虚拟地图，通过多元高斯分布描述每个地图单元的不确定性。该虚拟地图在规划阶段用作代价地图，从而避免在每个步骤执行信念传播。采用后退时域规划策略，实现目标到达与不确定性降低的权衡。在逼真水下环境中的仿真实验验证了该方法，并与完整信念传播方法及标准最短距离方法进行了对比实验。