CoSEC: A Coaxial Stereo Event Camera Dataset for Autonomous Driving

TL;DR

The paper introduces CoSEC, a coaxial stereo event camera dataset for autonomous driving, emphasizing reduced spatial misalignment and improved multimodal fusion in challenging conditions like low light and nighttime.

Contribution

It presents a novel coaxial stereo event camera system and dataset, enabling better pixel-level multimodal fusion and alignment for autonomous driving applications.

Findings

Enhanced multimodal fusion performance in nighttime conditions

Reduced spatial misalignment with coaxial device setup

Improved generalization in multimodal perception tasks

Abstract

Conventional frame camera is the mainstream sensor of the autonomous driving scene perception, while it is limited in adverse conditions, such as low light. Event camera with high dynamic range has been applied in assisting frame camera for the multimodal fusion, which relies heavily on the pixel-level spatial alignment between various modalities. Typically, existing multimodal datasets mainly place event and frame cameras in parallel and directly align them spatially via warping operation. However, this parallel strategy is less effective for multimodal fusion, since the large disparity exacerbates spatial misalignment due to the large event-frame baseline. We argue that baseline minimization can reduce alignment error between event and frame cameras. In this work, we introduce hybrid coaxial event-frame devices to build the multimodal system, and propose a coaxial stereo event camera (CoSEC) dataset for autonomous driving. As for the multimodal system, we first utilize the microcontroller to achieve time synchronization, and then spatially calibrate different sensors, where we perform intra- and inter-calibration of stereo coaxial devices. As for the multimodal dataset, we filter LiDAR point clouds to generate depth and optical flow labels using reference depth, which is further improved by fusing aligned event and frame data in nighttime conditions. With the help of the coaxial device, the proposed dataset can promote the all-day pixel-level multimodal fusion. Moreover, we also conduct experiments to demonstrate that the proposed dataset can improve the performance and generalization of the multimodal fusion.