OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving (2205.14087v2)

Abstract: Accurate sensor calibration is a prerequisite for multi-sensor perception and localization systems for autonomous vehicles. The intrinsic parameter calibration of the sensor is to obtain the mapping relationship inside the sensor, and the extrinsic parameter calibration is to transform two or more sensors into a unified spatial coordinate system. Most sensors need to be calibrated after installation to ensure the accuracy of sensor measurements. To this end, we present OpenCalib, a calibration toolbox that contains a rich set of various sensor calibration methods. OpenCalib covers manual calibration tools, automatic calibration tools, factory calibration tools, and online calibration tools for different application scenarios. At the same time, to evaluate the calibration accuracy and subsequently improve the accuracy of the calibration algorithm, we released a corresponding benchmark dataset. This paper introduces various features and calibration methods of this toolbox. To our knowledge, this is the first open-sourced calibration codebase containing the full set of autonomous-driving-related calibration approaches in this area. We wish that the toolbox could be helpful to autonomous driving researchers. We have open-sourced our code on GitHub to benefit the community. Code is available at https://github.com/PJLab-ADG/SensorsCalibration.

• The paper introduces OpenCalib as a toolbox that integrates manual, automatic, and online calibration techniques to align LiDAR, camera, radar, and other sensors in autonomous driving.
• It details diverse methodologies such as target-less calibration, deep learning-based motion approaches, and real-time online tools to enhance sensor fusion and localization.
• A benchmark dataset from the Carla simulator validates the tool’s performance, underscoring its potential to improve both factory and in-operation calibration processes.

OpenCalib: A Comprehensive Multi-Sensor Calibration Toolbox for Autonomous Driving

The paper introduces OpenCalib, a calibration toolbox designed to address the intricate requirements of sensor fusion in autonomous driving systems. Calibration is a foundational element necessary for accurate perception and localization, ensuring that multiple heterogeneous sensors such as LiDAR, cameras, IMUs, GNSS, and radars can operate synergistically within a unified spatial framework. OpenCalib encompasses a range of calibration tools catering to manual, automatic, factory, and online needs, making it a versatile solution for varied application scenarios.

Core Components and Methodologies

1. Manual Calibration Tools: OpenCalib provides user-friendly manual tools for target-less calibration in road scenes. These tools enable precise adjustments of extrinsic parameters, supporting the calibration of LiDAR-to-camera, LiDAR-to-LiDAR, Radar-to-LiDAR, and Radar-to-camera systems.
2. Automatic Calibration: The toolbox incorporates target-based and target-less methods for automatic calibration. Target-based approaches involve specialized patterns like checkerboards, while target-less methods utilize features from the environment for sensor alignment. This versatility accommodates both conventional and innovative calibration scenarios.
3. Motion-Based and Learning Approaches: OpenCalib addresses calibration as a hand-eye problem, integrating deep learning solutions for complex sensor calibration tasks such as LiDAR-camera parameter estimation. While not universally applied across all sensors, these methods signify progress towards automating traditionally manual processes.
4. Factory and Online Calibration: OpenCalib supports production line calibration frameworks, accommodating various calibration boards with high-detection accuracy. Online calibration tools offer real-time calibration capabilities, crucial for maintaining the validity of extrinsic parameters during vehicle operation.
5. Benchmark Dataset: To validate calibration performance, a benchmark dataset has been developed using the Carla simulator. This dataset provides ground truth for evaluating calibration algorithms, offering a controlled environment to test the efficacy of the tool's diverse methodologies.

Implications and Future Directions

OpenCalib represents a significant step towards integrating comprehensive calibration solutions within autonomous vehicles. By supporting a full range of scenarios—from factory setup to on-the-fly adjustments—the toolbox could enhance sensor fusion reliability and, by extension, the overall safety and efficiency of autonomous systems.

The release of OpenCalib as an open-source project aims to foster community collaboration and iterative enhancement. Subsequent versions are expected to incorporate additional state-of-the-art calibration algorithms, potentially expanding its applicability.

Future research might explore the refinement of learning-based methodologies, aiming to enhance accuracy and stability in multi-sensor calibration tasks. Additionally, further development of online calibration techniques could lead to seamless integration within real-time autonomous vehicle environments.

In conclusion, OpenCalib stands as a valuable resource for researchers and practitioners in the field of autonomous driving, addressing both existing challenges and paving the way for new calibration paradigms.