Spatio-Temporal Calibration for Omni-Directional Vehicle-Mounted Event Cameras

TL;DR

This paper introduces a novel spatio-temporal calibration method for omni-directional vehicle-mounted event cameras, utilizing kinematic correlations to estimate temporal offsets and extrinsic rotations, outperforming traditional trajectory alignment techniques.

Contribution

The paper proposes a new calibration approach that leverages velocity correlations instead of trajectory alignment, enabling more accurate spatio-temporal calibration of event cameras on vehicles.

Findings

Effective on synthetic data

Outperforms traditional trajectory alignment methods

Works with real vehicle data

Abstract

We present a solution to the problem of spatio-temporal calibration for event cameras mounted on an onmi-directional vehicle. Different from traditional methods that typically determine the camera's pose with respect to the vehicle's body frame using alignment of trajectories, our approach leverages the kinematic correlation of two sets of linear velocity estimates from event data and wheel odometers, respectively. The overall calibration task consists of estimating the underlying temporal offset between the two heterogeneous sensors, and furthermore, recovering the extrinsic rotation that defines the linear relationship between the two sets of velocity estimates. The first sub-problem is formulated as an optimization one, which looks for the optimal temporal offset that maximizes a correlation measurement invariant to arbitrary linear transformation. Once the temporal offset is compensated, the extrinsic rotation can be worked out with an iterative closed-form solver that incrementally registers associated linear velocity estimates. The proposed algorithm is proved effective on both synthetic data and real data, outperforming traditional methods based on alignment of trajectories.