Towards Accurate Active Camera Localization
This work provides a more accurate active camera localization method, which is significant for robotics and augmented reality applications requiring precise camera poses.
This paper addresses active camera localization by proposing an algorithm with passive and active modules. The passive module optimizes camera pose in continuous space using point-wise correspondences, while the active module plans paths based on scene and camera uncertainty. The algorithm outperforms state-of-the-art Markov Localization and other approaches in fine-scale camera pose accuracy on synthetic and real-world indoor scenes.
In this work, we tackle the problem of active camera localization, which controls the camera movements actively to achieve an accurate camera pose. The past solutions are mostly based on Markov Localization, which reduces the position-wise camera uncertainty for localization. These approaches localize the camera in the discrete pose space and are agnostic to the localization-driven scene property, which restricts the camera pose accuracy in the coarse scale. We propose to overcome these limitations via a novel active camera localization algorithm, composed of a passive and an active localization module. The former optimizes the camera pose in the continuous pose space by establishing point-wise camera-world correspondences. The latter explicitly models the scene and camera uncertainty components to plan the right path for accurate camera pose estimation. We validate our algorithm on the challenging localization scenarios from both synthetic and scanned real-world indoor scenes. Experimental results demonstrate that our algorithm outperforms both the state-of-the-art Markov Localization based approach and other compared approaches on the fine-scale camera pose accuracy. Code and data are released at https://github.com/qhFang/AccurateACL.