发表机构
University at Buffalo; University of Florida（布法罗大学; 佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PIVOT提出一种轻量级迭代方法，在固定平移轨迹上在线优化传感器观察方向以最大化特征可见性，通过SO(3)指数映射实现高效连续优化，保留98.1-99.6%可见性并加速76-85倍，在仿真和真实实验中验证了改进的定位鲁棒性。
AI 中文摘要
机器人感知的一个基本假设是传感器的视场（FoV）相对于机器人本体是固定的。然而，运动解耦传感器，如云台相机和基于MEMS的激光雷达，允许在运行时独立控制感知方向。这种自由度带来了一个计算挑战：在特征密集环境中，如何在线高效地选择有用的观察方向。我们提出了PIVOT，一种轻量级迭代方法，它沿着固定的平移轨迹优化传感器观察方向，以最大化特征可见性。在锥形视场模型下，可见性仅取决于光轴，从而在观察球面$S^2$上产生一个二自由度优化问题。无坐标的$SO(3)$指数映射更新使得无需显式角度参数化或穷举观察球面搜索即可进行高效的连续优化。蒙特卡洛评估保留了98.1--99.6%的暴力搜索可见性，同时实现了76--85倍的加速。真实感仿真和真实世界实验进一步证明了在四足机器人上改进的视觉定位鲁棒性和实用的视点控制。
英文摘要
A fundamental assumption in robotic perception is that the sensor's field of view (FoV) is fixed relative to the robot body. Motion-decoupled sensors, such as gimbal-mounted cameras and MEMS-based LiDARs, instead allow sensing direction to be controlled independently at runtime. This freedom creates a computational challenge: efficiently selecting useful viewing directions online in feature-dense environments. We propose PIVOT, a lightweight iterative method that optimizes sensor viewing direction along a fixed translation trajectory to maximize feature visibility. Under a conical FoV model, visibility depends only on the optical axis, yielding a two-degree-of-freedom optimization on the viewing sphere $S^2$. Coordinate-free $SO(3)$ exponential-map updates enable efficient continuous optimization without explicit angular parameterizations or exhaustive viewing-sphere search. Monte Carlo evaluations retain 98.1--99.6% of brute-force visibility with a 76--85x speedup. Photorealistic simulation and real-world experiments further demonstrate improved visual localization robustness and practical viewpoint control on a quadruped robot.
Comments9 pages, 9 figures, 3 tables. Yuyang Chen and Shekoufeh Sadeghi contributed equally to this work. Submitted to IEEE Robotics and Automation Letters (RA-L)
