基于运动预测的改进ORB-SLAM算法

针对不同运动场景下以固定的点特征提取与匹配策略的ORB-SLAM算法存在系统跟踪定位误差较大的问题,考虑相机自身运动对视觉SLAM系统的影响,提出基于运动预测的改进ORB-SLAM算法.该方法利用上一帧的点特征利用率和匀速运动模型,预测出相邻2帧之间的共视范围,实时动态调整不同运动状态下的点特征提取阈值,在保证系统稳定性的情况下,提高系统的准确性.提出基于运动预测的点特征匹配优化策略,基于匀速运动模型快速确定出共视范围内的有效待匹配点,结合图像金字塔缩小匹配搜索范围,减少大量的无效匹配过程.在TUM数据集上进行对比实验,结果表明,提出的算法不仅实时性好,而且提高了系统的精度.

Improved ORB-SLAM algorithm based on motion prediction

An improved ORB-SLAM algorithm based on motion prediction was proposed by considering the influence of the camera’s own motion on the visual SLAM system aiming at the problem that the ORB-SLAM algorithm with fixed point feature extraction and matching strategy has large tracking and positioning error in different motion scenes. The point feature utilization rate of the previous frame and the uniform motion model were used to predict the mutually visual zone between two adjacent frames. The threshold of point feature extraction under different motion states was dynamically adjusted in real time. Then the accuracy of the system was improved while ensuring the stability of the system. A point feature matching optimization strategy based on motion prediction was proposed. The effective matching points within the mutually visual zone were quickly determined based on the uniform motion model. The matching search range was narrowed by combining the image pyramid in order to reduce many invalid matching processes. The comparison experiments were conducted on the TUM data set. Results show that the proposed algorithm not only has good real-time performance, but also improves the accuracy of the system.