The number and arrangement of sensors on mobile robot dramatically influence its perception capabilities. Ensuring that sensors are mounted in a manner that enables accurate detection, localization, and mapping is essential for the success of downstream control tasks. However, when designing a new robotic platform, researchers and practitioners alike usually mimic standard configurations or maximize simple heuristics like field-of-view (FOV) coverage to decide where to place exteroceptive sensors. In this work, we conduct an information-theoretic investigation of this overlooked element of robotic perception in the context of simultaneous localization and mapping (SLAM). We show how to formalize the sensor arrangement problem as a form of subset selection under the E-optimality performance criterion. While this formulation is NP-hard in general, we show that a combination of greedy sensor selection and fast convex relaxation-based post-hoc verification enables the efficient recovery of certifiably optimal sensor designs in practice. Results from synthetic experiments reveal that sensors placed with OASIS outperform benchmarks in terms of mean squared error of visual SLAM estimates.

翻译：移动机器人上传感器的数量和布置极大地影响其感知能力。确保传感器以能够实现精确检测、定位和地图构建的方式安装，对于下游控制任务的成功至关重要。然而，在设计新型机器人平台时，研究人员和从业者通常模仿标准配置或最大化简单的启发式方法（如视场覆盖）来决定外部传感器的放置位置。在本工作中，我们从信息论角度对同步定位与地图构建（SLAM）中这一被忽视的感知要素进行了研究。我们展示了如何将传感器布置问题形式化为在E-最优性性能准则下的子集选择形式。尽管该问题在一般情况下是NP难的，但我们证明，结合贪婪传感器选择与基于快速凸松弛的后验验证，能够在实践中高效恢复可证明最优的传感器设计。合成实验的结果表明，采用OASIS放置的传感器在视觉SLAM估计的均方误差方面优于基准方法。