基于改进ORB的视觉里程计算法

Visual odometry calculation method based on improved ORB

ORB-SLAM 2因高斯金字塔离散、量化尺度的因素,易造成尺度量化误差.针对这一缺陷,提出孪生过滤器算法.通过在对数极坐标系下进行描述子构造,减少其量化误差,并在同层金字塔中构造笛卡尔坐标系下孪生描述子,利用该描述子距离实现过滤,从而提高特征点的尺度不变性,增强其匹配准确率.同时,针对ORB-SLAM 2的四叉树算法过度追求离散度而忽略特征点质量问题这一情况,提出深度有限四叉树算法.利用特征点提取阈值以及特征点所在金字塔层进行自适应深度阈值设置,减小弱特征点区域的划分次数,从而减少弱特征点提取数目.实验表明,所提出算法能够有效提高特征点离散度、正确匹配特征点数目和匹配精度,具有更高的轨迹精度.

ORB-SLAM 2 is prone to scale quantization error due to discrete Gaussian pyramid and quantization scale.Aiming at this defect,a twin filter algorithm is proposed.The quantization error is reduced by constructing descriptors in the logarithmic polar coordinate system,and twin descriptors in the Cartesian coordinate system are constructed in the same pyramid,and the distance of the descriptors is used as the filter to improve the scale invariance of feature points and enhance their matching accuracy.At the same time,aiming at the situation that the qurdtree algorithm of ORB-SLAM 2 excessively pursues the discreteness but neglects the feature point quality problem,this paper proposes the quadtree algorithm with limited depth.The feature point extraction threshold and the pyramid layer where the feature points are located are used to set the adaptive depth threshold to reduce the division times of weak feature point regions and thus reduce the number of weak feature points extraction.Experiments show that the proposed algorithm can effectively improve the dispersion of feature points,the number of correctly matched feature points and the matching accuracy,and has higher trajectory accuracy.