Edged USLAM: Edge-Aware Event-Based SLAM with Learning-Based Depth Priors

TL;DR

Edged USLAM is a hybrid event-based SLAM system that combines edge-aware feature tracking and a lightweight depth module to improve localization robustness under challenging lighting and motion conditions.

Contribution

The paper introduces Edged USLAM, a novel hybrid visual-inertial SLAM system that integrates edge-aware event frame processing and a region-of-interest depth module for enhanced performance.

Findings

Edged USLAM outperforms existing methods in slow or structured trajectories.

Event-only and learning-based methods excel in extreme HDR or aggressive motion.

Edged USLAM provides stable, accurate localization in challenging real-world UAV flights.

Abstract

Conventional visual simultaneous localization and mapping (SLAM) algorithms often fail under rapid motion, low illumination, or abrupt lighting transitions due to motion blur and limited dynamic range. Event cameras mitigate these issues with high temporal resolution and high dynamic range (HDR), but their sparse, asynchronous outputs complicate feature extraction and integration with other sensors; e.g. inertial measurement units (IMUs) and standard cameras. We present Edged USLAM, a hybrid visual-inertial system that extends Ultimate SLAM (USLAM) with an edge-aware front-end and a lightweight depth module. The frontend enhances event frames for robust feature tracking and nonlinear motion compensation, while the depth module provides coarse, region-of-interest (ROI)-based scene depth to improve motion compensation and scale consistency. Evaluations across public benchmarks and real-world unmanned air vehicle (UAV) flights demonstrate that performance varies significantly by scenario. For instance, event-only methods like point-line event-based visual-inertial odometry (PL-EVIO) or learning-based pipelines such as deep event-based visual odometry (DEVO) excel in highly aggressive or extreme HDR conditions. In contrast, Edged USLAM provides superior stability and minimal drift in slow or structured trajectories, ensuring consistently accurate localization on real flights under challenging illumination. These findings highlight the complementary strengths of event-only, learning-based, and hybrid approaches, while positioning Edged USLAM as a robust solution for diverse aerial navigation tasks.