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

Jiajie Li, Boyang Sun, Luca Di Giammarino, Hermann Blum, Marc Pollefeys

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 large-scale 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 state-of-the-art results on single-viewpoint selection and generalizes effectively to full-trajectory planning. Its modular design makes it readily applicable to diverse robot navigation and inspection tasks.

Resources

[arxiv]

Bibtex

```bibtex @inproceedings{li2025actloc, title={ActLoc: Learning to Localize on the Move via Active Viewpoint Selection}, author={Li, Jiajie and Sun, Boyang and Di Giammarino, Luca and Blum, Hermann and Pollefeys, Marc}, booktitle={Proceedings of the Conference on Robot Learning (CoRL)}, year={2025} }