Neural Implicit Event Generator for Motion Tracking

TL;DR

This paper introduces an implicit event generator framework for efficient motion tracking from sparse event data, suitable for resource-constrained mobile robotics, and demonstrates its effectiveness in real-world AR marker tracking.

Contribution

The paper proposes a novel implicit event generator for motion tracking that improves efficiency by directly updating state estimates from sparse data, unlike traditional explicit methods.

Findings

Effective in real-world noisy environments

Suitable for mobile robotics with limited resources

Accurate AR marker tracking demonstrated

Abstract

We present a novel framework of motion tracking from event data using implicit expression. Our framework use pre-trained event generation MLP named implicit event generator (IEG) and does motion tracking by updating its state (position and velocity) based on the difference between the observed event and generated event from the current state estimate. The difference is computed implicitly by the IEG. Unlike the conventional explicit approach, which requires dense computation to evaluate the difference, our implicit approach realizes efficient state update directly from sparse event data. Our sparse algorithm is especially suitable for mobile robotics applications where computational resources and battery life are limited. To verify the effectiveness of our method on real-world data, we applied it to the AR marker tracking application. We have confirmed that our framework works well in real-world environments in the presence of noise and background clutter.