Rao-Blackwellized滤波器实现机器人同时定位和地图创建

Rao-blackwellized particle filters for robot simultaneous localization and mapping

为了实现移动机器人仅依靠单目视觉和里程计创建可靠地图,本文提出了基于Rao-Blackwellized粒子滤波器的同时定位和地图创建方法.文中建立了鲁棒的运动模型和感知模型;通过有效的尺度不变特征变换方法提取环境特征,并采用基于KD-Tree的高维特征点最近邻快速搜索算法实现特征匹配;通过对匹配对的三维重建创建了密集的空间三维自然路标.实际实验表明本文方法能创建较高精度的地图.

For building the robust map of mobile robot only with monocular vision and odometer, simultaneous localization and mapping （SLAM） is implemented efficiently with the Rao-Blackwellized particle filters （RBPF）. The robust motion model and observation model was constructed. Scale Invariant Feature Transform （SIFT） was used to extract image features served as the nature landmarks, and a fast nearest neighbor search algorithm for matching multi-dimensional features in a KD-Tree was presented to implement feature matching. The map representation was executed with dense 3D natural landmarks reconstructed with the matching pairs. Experiments on the real robot show that the proposed method is of high precision.