# Improvements to Target-Based 3D LiDAR to Camera Calibration

arXiv: https://arxiv.org/abs/1910.03126 · HTML: https://mlsanity.com/paper/1910.03126/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2019.

## Built on by (most cited first, 9 of 9)

- [LCE-Calib: Automatic LiDAR-Frame/Event Camera Extrinsic Calibration With A Globally Optimal Solution](https://mlsanity.com/paper/2303.09825/) (2023)
- [Optimising the selection of samples for robust lidar camera calibration](https://mlsanity.com/paper/2103.12287/) (2021)
- [Robust Long-Range Perception Against Sensor Misalignment in Autonomous Vehicles](https://mlsanity.com/paper/2408.11196/) (2024)
- [Intensity Image-based LiDAR Fiducial Marker System](https://mlsanity.com/paper/2203.01816/) (2022)
- [Physics and semantic informed multi-sensor calibration via optimization theory and self-supervised learning](https://mlsanity.com/paper/2206.02856/) (2022)
- [BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View Representations](https://mlsanity.com/paper/2506.02587/) (2025)
- [L-PR: Exploiting LiDAR Fiducial Marker for Unordered Low Overlap Multiview Point Cloud Registration](https://mlsanity.com/paper/2406.03298/) (2024)
- [Optimal Target Shape for LiDAR Pose Estimation](https://mlsanity.com/paper/2109.01181/) (2021)
- [Multi-View In-Cabin Monitoring System for Public Transport Vehicles](https://mlsanity.com/paper/2606.11739/) (2026)

## Abstract (arXiv)

The homogeneous transformation between a LiDAR and monocular camera is required for sensor fusion tasks, such as SLAM. While determining such a transformation is not considered glamorous in any sense of the word, it is nonetheless crucial for many modern autonomous systems. Indeed, an error of a few degrees in rotation or a few percent in translation can lead to 20 cm translation errors at a distance of 5 m when overlaying a LiDAR image on a camera image. The biggest impediments to determining the transformation accurately are the relative sparsity of LiDAR point clouds and systematic errors in their distance measurements. This paper proposes (1) the use of targets of known dimension and geometry to ameliorate target pose estimation in face of the quantization and systematic errors inherent in a LiDAR image of a target, and (2) a fitting method for the LiDAR to monocular camera transformation that fundamentally assumes the camera image data is the most accurate information in one's possession.