# CaLiV: LiDAR-to-Vehicle Calibration of Arbitrary Sensor Setups

arXiv: https://arxiv.org/abs/2504.01987 · HTML: https://mlsanity.com/paper/2504.01987/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [EDGAR: An Autonomous Driving Research Platform -- From Feature Development to Real-World Application](https://mlsanity.com/paper/2309.15492/) (2023)
- [Targetless Extrinsic Calibration of Multiple Small FoV LiDARs and Cameras using Adaptive Voxelization](https://mlsanity.com/paper/2109.06550/) (2021)
- [Hand-Eye Calibration](https://mlsanity.com/paper/2311.12655/) (2023)
- [A decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs](https://mlsanity.com/paper/2007.01483/) (2020)
- [CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road Scene](https://mlsanity.com/paper/2203.03182/) (2022)
- [Automatic Calibration of Multiple 3D LiDARs in Urban Environments](https://mlsanity.com/paper/1905.04912/) (2019)
- [Observability-aware online multi-lidar extrinsic calibration](https://mlsanity.com/paper/2212.09579/) (2022)
- [Multi-LiCa: A Motion and Targetless Multi LiDAR-to-LiDAR Calibration Framework](https://mlsanity.com/paper/2501.11088/) (2025)
- [Extrinsic Calibration and Verification of Multiple Non-overlapping Field of View Lidar Sensors](https://mlsanity.com/paper/2205.07098/) (2022)
- [A Target-Based Extrinsic Calibration Framework for Non-Overlapping Camera-Lidar Systems Using a Motion Capture System](https://mlsanity.com/paper/2303.10729/) (2023)
- [Extrinsic Calibration of Multiple LiDARs for a Mobile Robot based on Floor Plane And Object Segmentation](https://mlsanity.com/paper/2403.14161/) (2024)
- [GMMCalib: Extrinsic Calibration of LiDAR Sensors using GMM-based Joint Registration](https://mlsanity.com/paper/2404.03427/) (2024)

## Built on by (most cited first, 2 of 2)

- [FlowCalib: LiDAR-to-Vehicle Miscalibration Detection using Scene Flows](https://mlsanity.com/paper/2601.23107/) (2026)
- [Mind the Hitch: Dynamic Calibration and Articulated Perception for Autonomous Trucks](https://mlsanity.com/paper/2603.23711/) (2026)

## Abstract (arXiv)

In autonomous systems, sensor calibration is essential for safe and efficient navigation in dynamic environments. Accurate calibration is a prerequisite for reliable perception and planning tasks such as object detection and obstacle avoidance. Many existing LiDAR calibration methods require overlapping fields of view, while others use external sensing devices or postulate a feature-rich environment. In addition, Sensor-to-Vehicle calibration is not supported by the vast majority of calibration algorithms. In this work, we propose a novel target-based technique for extrinsic Sensor-to-Sensor and Sensor-to-Vehicle calibration of multi-LiDAR systems called CaLiV. This algorithm works for non-overlapping fields of view and does not require any external sensing devices. First, we apply motion to produce field of view overlaps and utilize a simple Unscented Kalman Filter to obtain vehicle poses. Then, we use the Gaussian mixture model-based registration framework GMMCalib to align the point clouds in a common calibration frame. Finally, we reduce the task of recovering the sensor extrinsics to a minimization problem. We show that both translational and rotational Sensor-to-Sensor errors can be solved accurately by our method. In addition, all Sensor-to-Vehicle rotation angles can also be calibrated with high accuracy. We validate the simulation results in real-world experiments. The code is open-source and available on https://github.com/TUMFTM/CaLiV.