PIVOT: Perception-aware Independent Viewpoint Online Optimization

2026-09-16

AI Summary

本文提出PIVOT方法，通过优化传感器视角来提高特征可见性，解决了运动解耦传感器在特征密集环境中高效选择视角的问题。

Abstract

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.

Problem

• sensor's field of view
• motion-decoupled sensors
• viewing direction
• feature-dense environments
• online optimization

Innovation

• PIVOT
• sensor viewing direction optimization
• conical FoV model
• coordinate-free SO(3) exponential-map updates
• Monte Carlo evaluations