Resource-aware Online Parameter Adaptation for Computationally-constrained Visual-Inertial Navigation Systems

TL;DR

This paper introduces a resource-aware parameter adaptation method for visual-inertial navigation systems that dynamically adjusts algorithm parameters based on available computational resources, enabling efficient operation on constrained hardware.

Contribution

The paper presents a novel online adaptation approach that modifies visual-inertial odometry parameters in real-time based on resource profiling and motion dynamics, improving efficiency without sacrificing accuracy.

Findings

Achieved comparable navigation performance with significantly reduced computational cost.

Validated on three algorithms: S-MSCKF, VINS-Mono, and OKVIS.

Demonstrated effectiveness on EuRoC dataset.

Abstract

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.