クォータニオン平均と深層マルチヘッドネットワークによる回転の確率的回帰
Probabilistic Regression of Rotations using Quaternion Averaging and a Deep Multi-Headed Network
深層回帰モデルから回転の確率的推定を得る手法を提案し、マルチヘッド構造HydraNetで不確実性を較正、SO(3)上のクォータニオン回帰と回転平均で3次元共分散を算出、ステレオ視覚オドメトリと融合しKITTIで位置推定を改善した。
著者: Valentin Peretroukhin, Brandon Wagstaff, Matthew Giamou, Jonathan Kelly
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
Accurate estimates of rotation are crucial to vision-based motion estimation in augmented reality and robotics. In this work, we present a method to extract probabilistic estimates of rotation from deep regression models. First, we build on prior work and argue that a multi-headed network structure we name HydraNet provides better calibrated uncertainty estimates than methods that rely on stochastic forward passes. Second, we extend HydraNet to targets that belong to the rotation group, SO(3), by regressing unit quaternions and using the tools of rotation averaging and uncertainty injection onto the manifold to produce three-dimensional covariances. Finally, we present results and analysis on a synthetic dataset, learn consistent orientation estimates on the 7-Scenes dataset, and show how we can use our learned covariances to fuse deep estimates of relative orientation with classical stereo visual odometry to improve localization on the KITTI dataset.
