Event-based SLAM Benchmark for High-Speed Maneuvers

2026-04-27

AI Summary

This work addresses the limitations of existing event-based SLAM methods under high-speed six-degree-of-freedom motion and the absence of a unified evaluation benchmark. We propose EvSLAM, the first evaluation framework specifically designed for event-based SLAM in highly dynamic scenarios. It introduces a multi-source dataset encompassing diverse platforms, extreme lighting conditions, and well-defined high-speed motion patterns, along with tailored evaluation metrics. Through systematic assessment of state-of-the-art visual and visual-inertial odometry algorithms, our study reveals critical performance bottlenecks of current approaches in real-world high-speed environments. The findings provide both theoretical insights and practical guidance for the development of future event-based SLAM systems with enhanced robustness.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Machine Learning: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution, offering the potential to handle visual tasks in high-speed maneuvering scenarios. Existing event-based approaches, although successful in mitigating motion blur caused by high-speed maneuvers, suffer from many limitations. Some of them highlight a success of pose tracking for a fronto-parallel fast shaking camera closed to the structure, while others assume pure (optionally aggressive) three-degree-of-freedom rotations. The former requires persistent local map visibility within the field of view (FOV), whereas the latter fails to generalize to six-degree-of-freedom (6-DoF) motions where both linear and angular velocities may be large. Consequently, current successes do not fully demonstrate that event-based state estimation under arbitrary aggressive maneuvers is a fully solved problem. To quantitatively assess the extent to which the potential of event cameras has been unlocked, we conduct a thorough analysis of state-of-the-art (SOTA) event-based visual odometry (VO)/visual-inertial odometry (VIO) methods and report shortcomings in current public datasets. Furthermore, we introduce a benchmarking framework for event-based state estimation, called EvSLAM, characterized by sufficient variation in data collection platforms, diverse extreme lighting scenarios, and a wide scope of challenging motion patterns under a clear and rigorous definition of high-speed maneuvers for mobile robots, along with a novel evaluation metric designed to fairly assess the operational limits of event-based solutions. This framework benchmarks state-of-the-art methods, yielding insights into optimal architectures and persistent challenges.

Problem

• event-based SLAM
• high-speed maneuvers
• visual odometry
• 6-DoF motion
• benchmarking

Innovation

• event-based SLAM
• high-speed maneuvers
• benchmarking framework
• visual-inertial odometry
• evaluation metric