Object reconstruction and inspection tasks play a crucial role in various robotics applications. Identifying paths that reveal the most unknown areas of the object is paramount in this context, as it directly affects reconstruction efficiency. Current methods often use sampling based path planning techniques, evaluating views along the path to enhance reconstruction performance. However, these methods are computationally expensive as they require evaluating several candidate views on the path. To this end, we propose a computationally efficient solution that relies on calculating a focus point in the most informative region and having the robot maintain this point in the camera field of view along the path. In this way, object reconstruction related information is incorporated into the whole body control of a mobile manipulator employing a visibility constraint without the need for an additional path planner. We conducted comprehensive and realistic simulations using a large dataset of 114 diverse objects of varying sizes from 57 categories to compare our method with a sampling based planning strategy and a strategy that does not employ informative paths using Bayesian data analysis. Furthermore, to demonstrate the applicability and generality of the proposed approach, we conducted real world experiments with an 8 DoF omnidirectional mobile manipulator and a legged manipulator. Our results suggest that, compared to a sampling based strategy, there is no statistically significant difference in object reconstruction entropy, and there is a 52.3% probability that they are practically equivalent in terms of coverage. In contrast, our method is 6.2 to 19.36 times faster in terms of computation time and reduces the total time the robot spends between views by 13.76% to 27.9%, depending on the camera FoV and model resolution.

翻译：物体重建与检测任务在各类机器人应用中至关重要。识别能够揭示物体最多未知区域的路径在此背景下尤为重要，因为这直接影响重建效率。现有方法通常采用基于采样的路径规划技术，通过评估路径上的视角来提升重建性能。然而，这些方法计算成本高昂，因为需要评估路径上的多个候选视角。为此，我们提出了一种计算高效的解决方案，该方法依赖于在最信息丰富的区域计算聚焦点，并引导机器人沿路径将该点保持在相机视野内。通过这种方式，物体重建相关信息被纳入移动机械臂的全身控制中，利用可见性约束，无需额外路径规划器。我们使用包含来自57个类别、尺寸各异的114个物体的数据集进行了全面且逼真的仿真，结合贝叶斯数据分析，将我们的方法与基于采样的规划策略以及不采用信息路径的策略进行了比较。此外，为展示所提方法的适用性与通用性，我们使用8自由度全向移动机械臂和腿式机械臂进行了真实世界实验。结果表明，与基于采样的策略相比，我们的方法在物体重建熵方面无统计显著差异，且在覆盖率方面有52.3%的概率实际等效。相比之下，我们的方法计算速度快6.2至19.36倍，并根据相机视场角和模型分辨率，将机器人在视角间移动的总时间减少13.76%至27.9%。