{"ID":2877281,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.20981","arxiv_id":"2508.20981","title":"ActLoc: Learning to Localize on the Move via Active Viewpoint Selection","abstract":"Reliable localization is critical for robot navigation, yet most existing systems implicitly assume that all viewing directions at a location are equally informative. In practice, localization becomes unreliable when the robot observes unmapped, ambiguous, or uninformative regions. To address this, we present ActLoc, an active viewpoint-aware planning framework for enhancing localization accuracy for general robot navigation tasks. At its core, ActLoc employs a largescale trained attention-based model for viewpoint selection. The model encodes a metric map and the camera poses used during map construction, and predicts localization accuracy across yaw and pitch directions at arbitrary 3D locations. These per-point accuracy distributions are incorporated into a path planner, enabling the robot to actively select camera orientations that maximize localization robustness while respecting task and motion constraints. ActLoc achieves stateof-the-art results on single-viewpoint selection and generalizes effectively to fulltrajectory planning. Its modular design makes it readily applicable to diverse robot navigation and inspection tasks.","short_abstract":"Reliable localization is critical for robot navigation, yet most existing systems implicitly assume that all viewing directions at a location are equally informative. In practice, localization becomes unreliable when the robot observes unmapped, ambiguous, or uninformative regions. To address this, we present ActLoc, a...","url_abs":"https://arxiv.org/abs/2508.20981","url_pdf":"https://arxiv.org/pdf/2508.20981v1","authors":"[\"Jiajie Li\",\"Boyang Sun\",\"Luca Di Giammarino\",\"Hermann Blum\",\"Marc Pollefeys\"]","published":"2025-08-28T16:36:02Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CV\",\"cs.LG\"]","methods":"[]","has_code":false}