構造情報を活用した点・線ビジュアル慣性オドメトリの改善
Leveraging Structural Information to Improve Point Line Visual-Inertial Odometry
人工環境で平行な直線群を構造線として分類し、2パラメータの最小表現とサンプリング点ベースの線マッチングで点・線VIOの精度と効率を向上させた研究。
著者: Bo Xu, Peng Wang, Yijia He, Yu Chen, Yongnan Chen, Ming Zhou
分類: cs.RO
原文アブストラクト
Leveraging line features can help to improve the localization accuracy of point-based monocular Visual-Inertial Odometry (VIO) system, as lines provide additional constraints. Moreover, in an artificial environment, some straight lines are parallel to each other. In this paper, we designed a VIO system based on points and straight lines, which divides straight lines into structural straight lines (that is, straight lines parallel to each other) and non-structural straight lines. In addition, unlike the orthogonal representation using four parameters to represent the 3D straight line, we only used two parameters to minimize the representation of the structural straight line and the non-structural straight line. Furthermore, we designed a straight line matching strategy based on sampling points to improve the efficiency and success rate of straight line matching. The effectiveness of our method is verified on both public datasets of EuRoc and TUM VI benchmark and compared with other state-of-the-art algorithms.
