FAST-LIEO2: Fast and Tightly-Coupled LiDAR-Inertial-Event Odometry

Abstract

In geometrically degenerate environments such as corridors and tunnels, LiDAR–Inertial odometry often drifts due to insufficient constraints, while conventional cameras can provide unreliable visual constraints under HDR lighting and severe motion blur. Event cameras offer microsecond-level temporal resolution and a high dynamic range, providing an additional information source for state estimation under challenging illumination and fast motion. In this work, we propose FAST-LIEO2 (and FAST-LIEVO2 with optional RGB fusion), a tightly-coupled LiDAR–inertial–event(–RGB) odometry framework that fuses LiDAR, event, and optional visual measurements via a sequentially updated error-state iterated Kalman filter (ESIKF) for efficient and stable multi-modal state estimation. To mitigate over-accumulation in highly active regions while preserving structure in low-activity areas, we design an activity-adaptive smoothed time surface (AS-TS) that adjusts the temporal decay parameter based on event activity. We further maintain two event submaps and design a coarse-to-fine hierarchical event update strategy, performing frame-to-frame geometric alignment followed by frame-to-map edge alignment, to improve prior pose correction and enlarge the convergence basin. Experiments on the public benchmark datasets and a self-collected dataset show that FAST-LIEO2 outperforms the compared multi-sensor SLAM baselines in accuracy and robustness across diverse structural and illumination conditions, while achieving strong real-time performance.