LiePoseNet: リー群に基づく異種損失関数によるPoseNet学習の大幅な高速化
LiePoseNet: Heterogeneous Loss Function Based on Lie Group for Significant Speed-up of PoseNet Training Process
リー群上の集中ガウス分布に基づく損失関数を導入し、剛体・射影幾何を考慮することで、PoseNetの学習を300エポックから10エポックへと大幅に短縮した。
著者: Mikhail Kurenkov, Ivan Kalinov, Dzmitry Tsetserukou
分類: cs.RO
原文アブストラクト
Visual localization is an essential modern technology for robotics and computer vision. Popular approaches for solving this task are image-based methods. Nowadays, these methods have low accuracy and a long training time. The reasons are the lack of rigid-body and projective geometry awareness, landmark symmetry, and homogeneous error assumption. We propose a heterogeneous loss function based on concentrated Gaussian distribution with the Lie group to overcome these difficulties. Following our experiment, the proposed method allows us to speed up the training process significantly (from 300 to 10 epochs) with acceptable error values.
