点线融合双目定位与建图多维提升方法

Multi-dimensional improving method for point-line fusion stereo SLAM

针对现有点线融合视觉SLAM(simultaneous localization and mapping)方法中线提取和线匹配准确度低导致定位精度下降的问题,从多维度对现有点线融合双目视觉SLAM方法进行了改进研究。通过内部参数调整和长度阈值筛选改进LSD(line segment detection)提取质量;基于几何约束将线特征匹配抽象为稀疏最小化问题,求得最优解即找到线段最佳匹配;基于图像纹理评分和匹配成功特征数量分配点线特征权重,优化了后端点线特征融合策略。相比LSD算法和LBD(line binary descriptor)匹配方法,短线段数量平均下降了67%,匹配准确度平均提高了18%;相较于PL-SLAM,定位误差在EuRoC和KITTI数据集上分别下降了15%和45%左右。实验表明,通过多维度改进点线SLAM方法各模块的性能有效提高了SLAM系统的定位精度。

Due to low line extraction and matching accuracy, existing point-line fusion visual SLAM(simultaneous localization and mapping) methods have low localization accuracy.This paper improved existing point-line fusion stereo visual SLAM method from multiple dimensions.Firstly, this paper improved the LSD(line segment detection) extraction quality through internal parameters adjustment and length threshold filtering.Secondly, on the basis of geometric constraints, it abstracted the line feature matching as a sparse minimization problem to find best match of line segment.Thirdly, on the basis of image texture score and number of successfully matched features, this paper improved back-end point-line feature fusion strategy by assigning point-line feature weights.Compared with LSD algorithm and LBD(line binary descriptor) matching method, the number of short line segments decreased by 67% on average and matching accuracy increased by 18% on average.Compared with PL-SLAM,localization error dropped by about 15% and 45% respectively on the EuRoC and KITTI datasets.Experiments demonstrate that the proposed method enhances localization accuracy of SLAM system effectively by improving various modules performance of point-line SLAM method in multiple dimensions.