# A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces

arXiv: https://arxiv.org/abs/1904.12116 · HTML: https://mlsanity.com/paper/1904.12116/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups](https://mlsanity.com/paper/2101.04431/) (2021)
- [CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road Scene](https://mlsanity.com/paper/2203.03182/) (2022)
- [SensorX2car: Sensors-to-car calibration for autonomous driving in road scenarios](https://mlsanity.com/paper/2301.07279/) (2023)
- [Multi-LiDAR Localization and Mapping Pipeline for Urban Autonomous Driving](https://mlsanity.com/paper/2311.01823/) (2023)
- [Optimal Target Shape for LiDAR Pose Estimation](https://mlsanity.com/paper/2109.01181/) (2021)
- [V2I-Calib: A Novel Calibration Approach for Collaborative Vehicle and Infrastructure LiDAR Systems](https://mlsanity.com/paper/2407.10195/) (2024)
- [Incorporating GNSS Information with LIDAR-Inertial Odometry for Accurate Land-Vehicle Localization](https://mlsanity.com/paper/2503.23199/) (2025)

## Abstract (arXiv)

Multiple lidars are prevalently used on mobile vehicles for rendering a broad view to enhance the performance of localization and perception systems. However, precise calibration of multiple lidars is challenging since the feature correspondences in scan points cannot always provide enough constraints. To address this problem, the existing methods require fixed calibration targets in scenes or rely exclusively on additional sensors. In this paper, we present a novel method that enables automatic lidar calibration without these restrictions. Three linearly independent planar surfaces appearing in surroundings is utilized to find correspondences. Two components are developed to ensure the extrinsic parameters to be found: a closed-form solver for initialization and an optimizer for refinement by minimizing a nonlinear cost function. Simulation and experimental results demonstrate the high accuracy of our calibration approach with the rotation and translation errors smaller than 0.05rad and 0.1m respectively.