摘要
同时优化运动和结构的集束调整方法存在对初始值依赖太大,收敛速度慢,收敛发散等数值稳定性低的缺点。本文提出了一种新的用于立体视觉定位的多帧序列运动估计方法,该方法收敛速度快,能收敛到全局最小,可大大减少积累误差。立体视觉定位仿真实验和户外智能车真实实验表明:基于多帧运动估计的实时立体视觉定位算法在计算精度、运行时间、抗噪声、对初始参数的稳定性方面都优于基于集束调整的实时立体视觉定位算法。
Bundle adjustment which simultaneously refines motion and structure has the fault of low numerical stability such as dependence on the initial value, slow convergence speed, and convergence divergence. In this paper, we propose a new multi-frame sequence motion estimation method for stereo visual localization. The method has the fast convergence speed, can converge to the global minimum, and greatly reduce the accumulated error. Stereo visual localization experiments with simulated data and outdoor intelligent vehicle show that our algorithm outperforms bundle adjustment in terms of run-time, accuracy, resistance to noise and dependence on the initial value
出处
《光电工程》
CAS
CSCD
北大核心
2016年第2期89-94,共6页
Opto-Electronic Engineering
基金
国家自然科学基金项目(61370173)
湖州师范学院校级科研项目成果(KX24071)
关键词
实时视觉定位
视觉导航
集束调整
多帧序列运动估计
real-time visual localization
visual navigation
bundle adjustment
multi-frame sequence motion estimation
