視覚慣性SLAMにおけるSchmidtフィルタと圧縮フィルタの比較
Schmidt or Compressed filtering for Visual-Inertial SLAM?
視覚慣性SLAMの計算コスト削減のため、キーフレームを圧縮形式で保持するCompressed-MSCKFを提案し、Schmidtフィルタと比較評価した論文。
著者: Hongkyoon Byun, Jonghyuk Kim, Fernando Vanegas, Felipe Gonzalez
分類: cs.RO
原文アブストラクト
Visual-inertial SLAM has been studied widely due to the advantage of its lightweight, cost-effectiveness, and rich information compared to other sensors. A multi-state constrained filter (MSCKF) and its Schmidt version have been developed to address the computational cost, which treats keyframes as static nuisance parameters, leading to sub-optimal performance. We propose a new Compressed-MSCKF which can achieve improved accuracy with moderate computational costs. By keeping the information gain with compressed form, it can limit to $\mathcal{O}(L)$ with $L$ being the number of local keyframes. The performance of the proposed system has been evaluated using a MATLAB simulator.
