Efficient Extrinsic Calibration of Multi-Sensor 3D LiDAR Systems for Autonomous Vehicles using Static Objects Information

TL;DR

This paper introduces a fast, structured optimization method for calibrating multi-sensor 3D LiDAR systems in autonomous vehicles using static object information, suitable for real-world scenarios.

Contribution

It presents a novel calibration approach that efficiently combines ground and pole data, improving online applicability over traditional methods.

Findings

Achieves accurate calibration results in urban environments.

Demonstrates efficiency with simulated and real LiDAR data.

Outperforms some existing calibration techniques in speed and accuracy.

Abstract

For an autonomous vehicle, the ability to sense its surroundings and to build an overall representation of the environment by fusing different sensor data streams is fundamental. To this end, the poses of all sensors need to be accurately determined. Traditional calibration methods are based on: 1) using targets specifically designed for calibration purposes in controlled environments, 2) optimizing a quality metric of the point clouds collected while traversing an unknown but static environment, or 3) optimizing the match among per-sensor incremental motion observations along a motion path fulfilling special requirements. In real scenarios, however, the online applicability of these methods can be limited, as they are typically highly dynamic, contain degenerate paths, and require fast computations. In this paper, we propose an approach that tackles some of these challenges by formulating the calibration problem as a joint but structured optimization problem of all sensor calibrations that takes as input a summary of the point cloud information consisting of ground points and pole detections. We demonstrate the efficiency and quality of the results of the proposed approach in a set of experiments with LiDAR simulation and real data from an urban trip.