Abstract
This paper aims to select features that contribute most to the pose estimation in VO/VSLAM. Unlike existing feature selection works that are focused on efficiency only, our method significantly improves the accuracy of pose tracking, while introducing little overhead. By studying the impact of feature selection towards least squares pose optimization, we demonstrate the applicability of improving accuracy via good feature selection. To that end, we introduce the Max-logDet metric to guide the feature selection, which is connected to the conditioning of least squares pose optimization problem. We then describe an efficient algorithm for approximately solving the NP-hard Max-logDet problem. Integrating Max-logDet feature selection into a state-of-the-art visual SLAM system leads to accuracy improvements with low overhead, as demonstrated via evaluation on a public benchmark.
Abstract (translated)
本文旨在选择对VO/VSLAM中姿态估计贡献最大的特征。与只注重效率的现有特征选择工作不同，我们的方法显著提高了姿势跟踪的精度，同时引入了很少的开销。通过研究特征选择对最小二乘姿态优化的影响，论证了通过良好的特征选择提高精度的适用性。为此，我们引入了最大logdet度量来指导特征选择，这与最小二乘姿态优化问题的条件处理有关。然后，我们描述了一种求解np-hard-max-logdet问题的有效算法。将max logdet功能选择集成到最先进的视觉冲击系统中，可以以较低的开销提高精度，这一点在公共基准上进行评估就可以证明。
URL
https://arxiv.org/abs/1905.07807
