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

Abstract

Reliable localization is critical for robot navigation, yet many existing systems assume that all viewpoints along a trajectory 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 the core of ActLoc is an attention-based model trained at scale for viewpoint selection. This model encodes a metric map of the scene, along with camera poses used during map construction, and estimates localization accuracy over camera pitch and yaw directions at arbitrary 3D waypoint in space. This per-point accuracy distribution is integrated into the path planning process, allowing the robot to actively choose camera orientation that maximize localization robustness while respecting task and motion constraints. ActLoc achieves state-of-the-art performance in single-viewpoint selection task, and generalizes effectively to full-trajectory planning. It provides a modular enhancement to a wide range of navigation and inspection tasks in structured environments.