机器人室内环境自主探索与地图构建方法

提出了一种基于多传感器融合的移动机器人自主探索与地图构建方法。首先,提出了一种基于激光、RGB-D相机和惯性测量单元的实时2D栅格地图和3D点云地图构建方法,采用RGB-D相机和惯性测量单元融合的方法,提升了位姿输出的频率,保证了采用激光数据和RGB-D数据进行建图的稳定性。其次,提出一种基于可通行区域的探索目标点快速提取方法,以补充原有的方法在复杂室内环境下提取探索目标点失效的问题。最后,通过改进的避障模块实现机器人的运动控制,使得机器人安全地探索目标。同时,本系统采用ROS的分布式架构,将整体算法合理地分配到服务端和客户端,降低了系统实现的硬件要求。不仅如此,设计了多目标导航界面,使人机交互更加方便和满足多任务部署。实验结果验证了方法的有效性。

A level set based segmentation approach for point-sampled surfaces

Segmenting a complex 3D surface model into some visually meaningful sub-parts is one of the fundamental problems in digital geometry processing. In this paper, a novel segmentation approach of point-sampled surfaces is proposed, which is based on the level set evolution scheme. To segment the model so as to align the patch boundaries with high curvature zones, the driven speed function for the zero level set inside narrow band is defined by the extended curvature field, which approaches zero speed as the propagating front approaches high curvature zone. The effectiveness of the proposed approach is demonstrated by our ex- perimental results. Furthermore, two applications of model segmentation are illustrated, such as piecewise parameterization and local editing for point-sampled geometry.