Task generation for underwater multi-robot inspections without prior knowledge of existing geometry can be achieved and optimized through examination of simultaneous localization and mapping (SLAM) data. By considering hardware parameters and environmental conditions, a set of tasks is generated from SLAM meshes and optimized through expected keypoint scores and distance-based pruning. In-water tests are used to demonstrate the effectiveness of the algorithm and determine the appropriate parameters. These results are compared to simulated Voronoi partitions and boustrophedon patterns for inspection coverage on a model of the test environment. The key benefits of the presented task discovery method include adaptability to unexpected geometry and distributions that maintain coverage while focusing on areas more likely to present defects or damage.

翻译：在缺乏先验几何信息的情况下，通过分析同步定位与建图（SLAM）数据，可实现并优化水下多机器人巡检任务的生成。该方法综合考虑硬件参数与环境条件，从SLAM网格中生成任务集，并通过期望关键点评分与基于距离的剪枝策略进行优化。通过水下实测验证算法有效性并确定最佳参数，将结果与测试环境模型中模拟的Voronoi分割及往复扫描模式的巡检覆盖效果进行对比。所提出的任务发现方法主要优势包括：对意外几何结构的自适应能力，以及在保持覆盖度的同时聚焦于更可能出现缺陷或损伤区域的分布特性。