multi_sensor_calibration
A ROS-based calibration tool for estimating extrinsic poses of lidar, radar, and camera sensor setups.
What is multi_sensor_calibration?
Multi Sensor Calibration is an open-source ROS-based toolbox for extrinsic calibration of multi-sensor setups, including lidars, radars, and cameras. It solves the problem of accurately determining the relative poses between different sensors, which is crucial for coherent data fusion in autonomous systems. The tool uses calibration board detections and optimization algorithms to compute sensor transformations.
Robotics engineers and researchers working on autonomous vehicles, drones, or any robotic platform with multi-sensor perception systems requiring precise calibration.
It provides a unified, modular solution for calibrating heterogeneous sensor types (lidar, radar, camera) within the ROS ecosystem, offering robust optimization methods and visualization tools not typically found in proprietary calibration suites.
Overview
Multi Sensor Calibration is a toolbox for performing extrinsic calibration of multi-sensor systems, enabling accurate estimation of relative poses between lidars, radars, and cameras. It is essential for autonomous vehicles and robotics, where precise sensor alignment is critical for reliable perception and data fusion.
Key Features
- Multi-Modality Support — Calibrates lidar, radar, and monocular/stereo camera sensors together.
- ROS Integration — Built as a ROS package with detector, accumulator, and optimizer nodes for seamless workflow.
- Fully Connected Pose Estimation (FCPE) — Joint optimization method for accurate sensor pose estimation.
- Outlier Handling — Capability to ignore faulty detections to improve calibration robustness.
- URDF Integration — Updates sensor poses in URDF files for use in robot descriptions.
- Visualization Tools — Provides plots and RViz markers to visualize calibration results and detections.
Philosophy
The project emphasizes a modular, sensor-agnostic approach, allowing calibration of diverse sensor setups with partial or full overlapping fields of view, while maintaining accuracy through robust optimization techniques.
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