中文摘要：

同时优化运动和结构的集柬调整方法存在对初始值依赖太大，收敛速魔陵，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。

英文摘要：

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