[
    {
        "id": "osp-26558",
        "type": "article-journal",
        "title": "PIVOT: Perception-aware Independent Viewpoint Online Optimization",
        "author": [
            {
                "family": "Chen",
                "given": "Yuyang"
            },
            {
                "family": "Sadeghi",
                "given": "Shekoufeh"
            },
            {
                "family": "Adhivarahan",
                "given": "Charuvahan"
            },
            {
                "family": "Lemos",
                "given": "Elton"
            },
            {
                "family": "Wang",
                "given": "Chen"
            },
            {
                "family": "Koppal",
                "given": "Sanjeev J."
            },
            {
                "family": "Dantu",
                "given": "Karthik"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/pivot-perception-aware-independent-viewpoint-online-optimization",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]