屋外環境における視覚SLAMのための連続情報モデリングに基づく能動的視点計画
Active View Planning for Visual SLAM in Outdoor Environments Based on Continuous Information Modeling
ジンバルカメラを用いて、特徴分布重み付きフィッシャー情報に基づく連続環境情報モデルを構築し、後退ホライズン最適化により情報量最大化視点を計画することで、屋外視覚SLAMのロバスト性と精度を向上させる手法を提案した。
著者: Zhihao Wang, Haoyao Chen, Shiwu Zhang, Yunjiang Lou
分類: cs.RO
原文アブストラクト
The visual simultaneous localization and mapping(vSLAM) is widely used in GPS-denied and open field environments for ground and surface robots. However, due to the frequent perception failures derived from lacking visual texture or the {swing} of robot view direction on rough terrains, the accuracy and robustness of vSLAM are still to be enhanced. The study develops a novel view planning approach of actively perceiving areas with maximal information to address the mentioned problem; a gimbal camera is used as the main sensor. Firstly, a map representation based on feature distribution-weighted Fisher information is proposed to completely and effectively represent environmental information richness. With the map representation, a continuous environmental information model is further established to convert the discrete information space into a continuous one for numerical optimization in real-time. Subsequently, the receding horizon optimization is utilized to obtain the optimal informative viewpoints with simultaneously considering the robotic perception, exploration and motion cost based on the continuous environmental model. Finally, several simulations and outdoor experiments are performed to verify the improvement of localization robustness and accuracy by the proposed approach.
