ActLoc: Learning to Localize on the Move via Active Viewpoint Selection

• URL: http://arxiv.org/abs/2508.20981v1
• Date: Thu, 28 Aug 2025 16:36:02 GMT
• Title: ActLoc: Learning to Localize on the Move via Active Viewpoint Selection
• Authors: Jiajie Li, Boyang Sun, Luca Di Giammarino, Hermann Blum, Marc Pollefeys,
• Abstract summary: ActLoc is an active viewpoint-aware planning framework for enhancing localization accuracy for general robot navigation tasks.<n>At its core, ActLoc employs a largescale trained attention-based model for viewpoint selection.<n>ActLoc achieves stateof-the-art results on single-viewpoint selection and generalizes effectively to fulltrajectory planning.
• Score: 52.909507162638526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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