信頼度を考慮した意味的シーン補完による視覚意味ナビゲーション
SSCNav: Confidence-Aware Semantic Scene Completion for Visual Semantic Navigation
未知環境で対象物体へ移動する視覚意味ナビゲーションのため、部分観測から未観測領域の意味ラベルと信頼度マップを推定するシーン補完モジュールを提案し、ナビゲーション方策の効率を向上させた。
著者: Yiqing Liang, Boyuan Chen, Shuran Song
分類: cs.CV, cs.RO
原文アブストラクト
This paper focuses on visual semantic navigation, the task of producing actions for an active agent to navigate to a specified target object category in an unknown environment. To complete this task, the algorithm should simultaneously locate and navigate to an instance of the category. In comparison to the traditional point goal navigation, this task requires the agent to have a stronger contextual prior to indoor environments. We introduce SSCNav, an algorithm that explicitly models scene priors using a confidence-aware semantic scene completion module to complete the scene and guide the agent's navigation planning. Given a partial observation of the environment, SSCNav first infers a complete scene representation with semantic labels for the unobserved scene together with a confidence map associated with its own prediction. Then, a policy network infers the action from the scene completion result and confidence map. Our experiments demonstrate that the proposed scene completion module improves the efficiency of the downstream navigation policies. Video, code, and data: https://sscnav.cs.columbia.edu/
