計算資源制約下の視覚慣性ナビゲーションのためのリソース認識型オンラインパラメータ適応
Resource-aware Online Parameter Adaptation for Computationally-constrained Visual-Inertial Navigation Systems
計算資源が限られた環境でも視覚慣性ナビゲーションを効率的に動かすため、実行前と実行中にリソース状況や運動ダイナミクスに応じてパラメータを適応させる手法を提案し、複数の既存アルゴリズムで有効性を実証した。
著者: Pranay Mathur, Nikhil Khedekar, Kostas Alexis
分類: cs.RO
原文アブストラクト
In this paper, a computational resources-aware parameter adaptation method for visual-inertial navigation systems is proposed with the goal of enabling the improved deployment of such algorithms on computationally constrained systems. Such a capacity can prove critical when employed on ultra-lightweight systems or alongside mission critical computationally expensive processes. To achieve this objective, the algorithm proposes selected changes in the vision front-end and optimization back-end of visual-inertial odometry algorithms, both prior to execution and in real-time based on an online profiling of available resources. The method also utilizes information from the motion dynamics experienced by the system to manipulate parameters online. The general policy is demonstrated on three established algorithms, namely S-MSCKF, VINS-Mono and OKVIS and has been verified experimentally on the EuRoC dataset. The proposed approach achieved comparable performance at a fraction of the original computational cost.
