Targetless Intrinsics and Extrinsic Calibration of Multiple LiDARs and Cameras with IMU using Continuous-Time Estimation

TL;DR

This paper introduces a novel calibration method for multiple LiDARs and cameras that does not require overlapping views or calibration targets, using continuous-time estimation and bundle adjustment to achieve accurate intrinsic and extrinsic parameters.

Contribution

The proposed pipeline enables simultaneous intrinsic and extrinsic calibration of multiple sensors without overlapping fields of view or calibration boards, leveraging continuous-time estimation and feature matching.

Findings

Successful calibration in texture-rich environments

No need for sensor synchronization triggers

Accurate co-visibility and motion constraint fulfillment

Abstract

Accurate spatiotemporal calibration is a prerequisite for multisensor fusion. However, sensors are typically asynchronous, and there is no overlap between the fields of view of cameras and LiDARs, posing challenges for intrinsic and extrinsic parameter calibration. To address this, we propose a calibration pipeline based on continuous-time and bundle adjustment (BA) capable of simultaneous intrinsic and extrinsic calibration (6 DOF transformation and time offset). We do not require overlapping fields of view or any calibration board. Firstly, we establish data associations between cameras using Structure from Motion (SFM) and perform self-calibration of camera intrinsics. Then, we establish data associations between LiDARs through adaptive voxel map construction, optimizing for extrinsic calibration within the map. Finally, by matching features between the intensity projection of LiDAR maps and camera images, we conduct joint optimization for intrinsic and extrinsic parameters. This pipeline functions in texture-rich structured environments, allowing simultaneous calibration of any number of cameras and LiDARs without the need for intricate sensor synchronization triggers. Experimental results demonstrate our method's ability to fulfill co-visibility and motion constraints between sensors without accumulating errors.