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Schmidt or Compressed filtering for Visual-Inertial SLAM?

2021-09-29 07:11:09
Hongkyoon Byun, Jonghyuk Kim, Fernando Vanegas, Felipe Gonzalez
arXiv_RO
     
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2109.14229

PDF

https://arxiv.org/pdf/2109.14229.pdf

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