TC-LEC: Targetless Calibration for LiDAR-Event Camera Systems

Abstract

LiDAR-event camera integration has shown considerable promise and is gaining traction across various perception applications. Event cameras offer high temporal resolution and wide dynamic range but suffer from noise sensitivity and lack depth information. LiDAR complements these capabilities by providing absolute scale and robustness, yet accurate calibration between the two sensors remains a significant challenge. This paper presents targetless calibration framework for LiDAR–event camera systems that removes dependence on dedicated calibration targets and strong initial assumptions. The method estimates the event camera angular velocity by analyzing the timestamp and spatial changes of per-pixel, enabling precise detection of natural edges. Calibration proceeds in two stages: (i) motion-based initialization, where Canonical Correlation Analysis (CCA) on rotational estimates from the event camera and LiDAR jointly recovers the temporal offset and rotation; (ii) nonlinear refinement of the extrinsics via cross-modal alignment of natural edge features. Experiments on physical platforms and public datasets demonstrate robust performance and high calibration accuracy across diverse scenarios. This work provides a solid foundation for further development and application of LiDAR-event camera fusion.