Energy-Efficient Event-Based Visual–Inertial Odometry: A Representation–Estimation Coupling Perspective
TL;DR
A system-level view is adopted, treating the energy consumption of event-based VIO as an outcome of the interaction between event representation and state-estimation strategy, and examines how information density and computational demand jointly shape system power.
Abstract
Event-based visual-inertial odometry (VIO) is often considered a promising sensing solution for low-power micro-robots because event cameras naturally produce sparse, low-latency measurements. However, an efficient sensor does not automatically lead to an energy-efficient system. This paper adopts a system-level view, treating the energy consumption of event-based VIO as an outcome of the interaction between event representation and state-estimation strategy. it examines how information density and computational demand jointly shape system power. From this analysis, it introduces the principle of representation–estimation coupling consistency. Evidence from a broad range of systems supports this principle. The framework also suggests where future low-power VIO systems are likely to converge: event-driven, semi-continuous, and jointly adaptive architectures under strict power budgets. Finally, it provides design guidance for three power ranges and discuss open problems. This study aims to offer a unified perspective for building energy-efficient perception systems for resource-constrained micro-robots.