摘要
针对图像纹理较为单一及相对模糊时仅仅依靠点特征难以实现精确位姿估计的问题,采用分散模块化技术提出了一种基于点线特征的视觉SLAM(同时定位与地图创建)算法.首先,提取相机采集环境中的点特征及线特征,并根据帧间特征匹配进行跟踪;随后,采用改进的NICP(normal iterative closest point)算法与关键帧匹配策略构建里程计系统.在此基础上,引入基于点线特征词典的闭环检测与GTSAM(Georgia Tech smoothing and mapping library)图优化方法获取具有全局一致性位姿的3维点云地图.以机器人技术中间件构筑系统框架,在提高系统实时性的同时增强功能模块的可扩展性与灵活性.标准数据集与实际实验室场景下的实验结果验证了所提方法的可行性和有效性.
When the image texture is simple and relatively indistinct, it is difficult to implement pose estimation based on point features. For this problem, a visual SLAM(simultaneous localization and mapping) algorithm based on point-line features is proposed by using the distributed modular technology. Firstly, the point and line features in the environment captured by the camera are extracted and tracked according to the inter-frame feature matching. Then, the improved NICP(normal iterative closest point) algorithm and the key frame matching strategy are used to build the odometer system. Based on this, the loop detection based on point-line feature dictionary and the graph optimization method of GTSAM(Georgia Tech smoothing and mapping library) are introduced to obtain 3D point cloud map with globally consistent poses. A system framework is developed with robot technology middleware to enhance the scalability and flexibility of the functional modules while improving the real-time performance of the system. The experimental results on the standard datasets and in laboratory scenes verify the feasibility and effectiveness of the proposed method.
作者
贾松敏
丁明超
张国梁
JIA Songmin;DING Mingchao;ZHANG Guoliang(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)
出处
《机器人》
EI
CSCD
北大核心
2019年第3期384-391,共8页
Robot
基金
国家自然科学基金(61175087,61703012,81471770)
北京工业大学2017智能制造领域大科研推进计划(040000546317552)
北京市自然科学基金(4182010)
关键词
机器人技术中间件
点线特征
NICP算法
图优化
3维地图
RTM(robot technology middleware)
point-line feature
NICP(normal iterative closest point) algorithm
graph optimization
3D map
