変化する環境における平面移動ロボットのロバストな自己位置推定：対応点と深度の密度に着目して
Robust localization for planar moving robot in changing environment: A perspective on density of correspondence and depth
深度の有無が混在する対応点を最大限活用し、1組の3D-2D対応と1組の2D-2D対応からカメラ姿勢を推定する最小解法を提案。環境特性に応じて解法を自動選択する仕組みも導入し、テクスチャの乏しい屋内環境でのロバストな視覚自己位置推定を実現した。
著者: Yanmei Jiao, Lilu Liu, Bo Fu, Xiaqing Ding, Minhang Wang, Yue Wang, Rong Xiong
分類: cs.RO
原文アブストラクト
Visual localization for planar moving robot is important to various indoor service robotic applications. To handle the textureless areas and frequent human activities in indoor environments, a novel robust visual localization algorithm which leverages dense correspondence and sparse depth for planar moving robot is proposed. The key component is a minimal solution which computes the absolute camera pose with one 3D-2D correspondence and one 2D-2D correspondence. The advantages are obvious in two aspects. First, the robustness is enhanced as the sample set for pose estimation is maximal by utilizing all correspondences with or without depth. Second, no extra effort for dense map construction is required to exploit dense correspondences for handling textureless and repetitive texture scenes. That is meaningful as building a dense map is computational expensive especially in large scale. Moreover, a probabilistic analysis among different solutions is presented and an automatic solution selection mechanism is designed to maximize the success rate by selecting appropriate solutions in different environmental characteristics. Finally, a complete visual localization pipeline considering situations from the perspective of correspondence and depth density is summarized and validated on both simulation and public real-world indoor localization dataset. The code is released on github.
