The number and arrangement of sensors on an autonomous 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 mobile 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 further 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：自主移动机器人上传感器的数量与布设方式显著影响其感知能力。确保传感器以能够实现精确检测、定位与建图的方式安装，对于后续控制任务的成功至关重要。然而，在新型机器人平台设计过程中，研究人员与实践者通常模仿标准配置，或通过最大化视场覆盖率等简单启发式策略决定外部传感器的安装位置。本研究从信息论角度，探讨了同步定位与建图（SLAM）背景下移动机器人感知中这一被忽视的元素。我们展示了如何将传感器布设问题形式化为基于E-最优性性能准则的子集选择问题。尽管该形式化方法在一般情况下属于NP-hard问题，但进一步研究表明，通过结合贪心传感器选择策略与基于快速凸松弛的后验验证方法，能够在实践中有条理地恢复可验证的最优传感器设计方案。合成实验结果表明，采用OASIS方法布设的传感器在视觉SLAM估计的均方误差指标上优于基准方案。