eNavi: Event-based Imitation Policies for Low-Light Indoor Mobile Robot Navigation

TL;DR

This paper introduces eNavi, a novel event-based indoor navigation system that leverages asynchronous event streams and RGB data to improve robot control in low-light and fast-motion conditions, validated through a new dataset and multimodal fusion approach.

Contribution

It presents a new real-world dataset, a multimodal data preprocessing pipeline, and a fusion-based navigation policy that enhances robustness in low-light indoor robot navigation.

Findings

Fusion models outperform RGB-only in low-light conditions.

Event data improves robustness and reduces action prediction error.

The system generalizes well across multiple indoor maps and trajectories.

Abstract

Event cameras provide high dynamic range and microsecond-level temporal resolution, making them well-suited for indoor robot navigation, where conventional RGB cameras degrade under fast motion or low-light conditions. Despite advances in event-based perception spanning detection, SLAM, and pose estimation, there remains limited research on end-to-end control policies that exploit the asynchronous nature of event streams. To address this gap, we introduce a real-world indoor person-following dataset collected using a TurtleBot 2 robot, featuring synchronized raw event streams, RGB frames, and expert control actions across multiple indoor maps, trajectories under both normal and low-light conditions. We further build a multimodal data preprocessing pipeline that temporally aligns event and RGB observations while reconstructing ground-truth actions from odometry to support high-quality imitation learning. Building on this dataset, we propose a late-fusion RGB-Event navigation policy that combines dual MobileNet encoders with a transformer-based fusion module trained via behavioral cloning. A systematic evaluation of RGB-only, Event-only, and RGB-Event fusion models across 12 training variations ranging from single-path imitation to general multi-path imitation shows that policies incorporating event data, particularly the fusion model, achieve improved robustness and lower action prediction error, especially in unseen low-light conditions where RGB-only models fail. We release the dataset, synchronization pipeline, and trained models at https://eventbasedvision.github.io/eNavi/