Edged USLAM: Edge-Aware Event-Based SLAM with Learning-Based Depth Priors
This work provides a robust and stable SLAM solution for UAV navigation in challenging illumination and structured environments, which is an incremental improvement for the robotics and autonomous systems community.
The paper introduces Edged USLAM, a hybrid visual-inertial SLAM system designed to overcome limitations of conventional SLAM in challenging conditions by using event cameras. It integrates an edge-aware front-end and a lightweight depth module to enhance feature tracking and motion compensation. While other methods excel in aggressive or extreme HDR, Edged USLAM achieves superior stability and minimal drift in slow or structured trajectories, ensuring accurate localization in real-world UAV flights under difficult illumination.
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.