CNSv2: 確率的対応符号化ニューラル画像サーボ
CNSv2: Probabilistic Correspondence Encoded Neural Image Servo
キーポイント検出が困難な環境でも、確率的特徴マッチングを組み込んだニューラル制御により高精度かつリアルタイムなビジュアルサーボを実現した。
著者: Anzhe Chen, Hongxiang Yu, Shuxin Li, Yuxi Chen, Zhongxiang Zhou, Wentao Sun, Rong Xiong, Yue Wang
分類: cs.CV, cs.RO
原文アブストラクト
Visual servo based on traditional image matching methods often requires accurate keypoint correspondence for high precision control. However, keypoint detection or matching tends to fail in challenging scenarios with inconsistent illuminations or textureless objects, resulting significant performance degradation. Previous approaches, including our proposed Correspondence encoded Neural image Servo policy (CNS), attempted to alleviate these issues by integrating neural control strategies. While CNS shows certain improvement against error correspondence over conventional image-based controllers, it could not fully resolve the limitations arising from poor keypoint detection and matching. In this paper, we continue to address this problem and propose a new solution: Probabilistic Correspondence Encoded Neural Image Servo (CNSv2). CNSv2 leverages probabilistic feature matching to improve robustness in challenging scenarios. By redesigning the architecture to condition on multimodal feature matching, CNSv2 achieves high precision, improved robustness across diverse scenes and runs in real-time. We validate CNSv2 with simulations and real-world experiments, demonstrating its effectiveness in overcoming the limitations of detector-based methods in visual servo tasks.
