TrackAgent: 強化学習による6D物体追跡
TrackAgent: 6D Object Tracking via Reinforcement Learning
物体追跡を強化学習による点群アライメントタスクとして簡素化し、フレーム間の位置合わせとモデルベースの復帰を統合的に学習する手法を提案。
著者: Konstantin Röhrl, Dominik Bauer, Timothy Patten, Markus Vincze
分類: cs.CV, cs.RO
原文アブストラクト
Tracking an object's 6D pose, while either the object itself or the observing camera is moving, is important for many robotics and augmented reality applications. While exploiting temporal priors eases this problem, object-specific knowledge is required to recover when tracking is lost. Under the tight time constraints of the tracking task, RGB(D)-based methods are often conceptionally complex or rely on heuristic motion models. In comparison, we propose to simplify object tracking to a reinforced point cloud (depth only) alignment task. This allows us to train a streamlined approach from scratch with limited amounts of sparse 3D point clouds, compared to the large datasets of diverse RGBD sequences required in previous works. We incorporate temporal frame-to-frame registration with object-based recovery by frame-to-model refinement using a reinforcement learning (RL) agent that jointly solves for both objectives. We also show that the RL agent's uncertainty and a rendering-based mask propagation are effective reinitialization triggers.
