PRGFlow: SWAP制約を考慮した統合型深層視覚慣性オドメトリのベンチマーク
PRGFlow: Benchmarking SWAP-Aware Unified Deep Visual Inertial Odometry
深層学習による視覚並進推定を慣性センサと緩く融合し6DoFオドメトリを推定する手法を提案し、アーキテクチャ・損失関数・圧縮手法を比較するベンチマークを提示した。
著者: Nitin J. Sanket, Chahat Deep Singh, Cornelia Fermüller, Yiannis Aloimonos
分類: cs.CV, cs.RO
原文アブストラクト
Odometry on aerial robots has to be of low latency and high robustness whilst also respecting the Size, Weight, Area and Power (SWAP) constraints as demanded by the size of the robot. A combination of visual sensors coupled with Inertial Measurement Units (IMUs) has proven to be the best combination to obtain robust and low latency odometry on resource-constrained aerial robots. Recently, deep learning approaches for Visual Inertial fusion have gained momentum due to their high accuracy and robustness. However, the remarkable advantages of these techniques are their inherent scalability (adaptation to different sized aerial robots) and unification (same method works on different sized aerial robots) by utilizing compression methods and hardware acceleration, which have been lacking from previous approaches. To this end, we present a deep learning approach for visual translation estimation and loosely fuse it with an Inertial sensor for full 6DoF odometry estimation. We also present a detailed benchmark comparing different architectures, loss functions and compression methods to enable scalability. We evaluate our network on the MSCOCO dataset and evaluate the VI fusion on multiple real-flight trajectories.
