# Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

arXiv: https://arxiv.org/abs/2001.06175 · HTML: https://mlsanity.com/paper/2001.06175/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2020.

## Built on by (most cited first, 15 of 15)

- [Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups](https://mlsanity.com/paper/2101.04431/) (2021)
- [MOISST: Multimodal Optimization of Implicit Scene for SpatioTemporal calibration](https://mlsanity.com/paper/2303.03056/) (2023)
- [CalibDNN: Multimodal Sensor Calibration for Perception Using Deep Neural Networks](https://mlsanity.com/paper/2103.14793/) (2021)
- [SOAC: Spatio-Temporal Overlap-Aware Multi-Sensor Calibration using Neural Radiance Fields](https://mlsanity.com/paper/2311.15803/) (2023)
- [Calib-Anything: Zero-training LiDAR-Camera Extrinsic Calibration Method Using Segment Anything](https://mlsanity.com/paper/2306.02656/) (2023)
- [SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera](https://mlsanity.com/paper/2207.03704/) (2022)
- [Continuous-Time Spatiotemporal Calibration of a Rolling Shutter Camera-IMU System](https://mlsanity.com/paper/2108.07200/) (2021)
- [CalibRefine: Deep Learning-Based Online Automatic Targetless LiDAR-Camera Calibration with Iterative and Attention-Driven Post-Refinement](https://mlsanity.com/paper/2502.17648/) (2025)
- [TEScalib: Targetless Extrinsic Self-Calibration of LiDAR and Stereo Camera for Automated Driving Vehicles with Uncertainty Analysis](https://mlsanity.com/paper/2202.13847/) (2022)
- [SceneCalib: Automatic Targetless Calibration of Cameras and Lidars in Autonomous Driving](https://mlsanity.com/paper/2304.05530/) (2023)
- [A re-calibration method for object detection with multi-modal alignment bias in autonomous driving](https://mlsanity.com/paper/2405.16848/) (2024)
- [Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation](https://mlsanity.com/paper/2410.06893/) (2024)
- [Iterative Camera-LiDAR Extrinsic Optimization via Surrogate Diffusion](https://mlsanity.com/paper/2411.10936/) (2024)
- [Iterative Camera-LiDAR Extrinsic Optimization via Surrogate Diffusion](https://mlsanity.com/paper/2506.14706/) (2025)
- [Diagnosing Temporal Misalignment in Multichannel Time-Series Classification with Minimum Description Length](https://mlsanity.com/paper/2609.14595/) (2026)

## Abstract (arXiv)

The demand for multimodal sensing systems for robotics is growing due to the increase in robustness, reliability and accuracy offered by these systems. These systems also need to be spatially and temporally co-registered to be effective. In this paper, we propose a targetless and structureless spatiotemporal camera-LiDAR calibration method. Our method combines a closed-form solution with a modified structureless bundle adjustment where the coarse-to-fine approach does not {require} an initial guess on the spatiotemporal parameters. Also, as 3D features (structure) are calculated from triangulation only, there is no need to have a calibration target or to match 2D features with the 3D point cloud which provides flexibility in the calibration process and sensor configuration. We demonstrate the accuracy and robustness of the proposed method through both simulation and real data experiments using multiple sensor payload configurations mounted to hand-held, aerial and legged robot systems. Also, qualitative results are given in the form of a colorized point cloud visualization.