Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups (2101.04431v2)

Abstract: Most sensor setups for onboard autonomous perception are composed of LiDARs and vision systems, as they provide complementary information that improves the reliability of the different algorithms necessary to obtain a robust scene understanding. However, the effective use of information from different sources requires an accurate calibration between the sensors involved, which usually implies a tedious and burdensome process. We present a method to calibrate the extrinsic parameters of any pair of sensors involving LiDARs, monocular or stereo cameras, of the same or different modalities. The procedure is composed of two stages: first, reference points belonging to a custom calibration target are extracted from the data provided by the sensors to be calibrated, and second, the optimal rigid transformation is found through the registration of both point sets. The proposed approach can handle devices with very different resolutions and poses, as usually found in vehicle setups. In order to assess the performance of the proposed method, a novel evaluation suite built on top of a popular simulation framework is introduced. Experiments on the synthetic environment show that our calibration algorithm significantly outperforms existing methods, whereas real data tests corroborate the results obtained in the evaluation suite. Open-source code is available at https://github.com/beltransen/velo2cam_calibration

• The paper introduces a novel, automatic calibration method that extracts key reference points using a custom fiducial target.
• It efficiently calibrates diverse sensor pairings, including LiDAR-LiDAR, camera-camera, and LiDAR-camera setups.
• Experimental results from simulations and real-world tests demonstrate subcentimeter accuracy and high robustness to noise and distortions.

Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups

The paper focuses on addressing the challenges of automatic extrinsic calibration in sensor setups incorporating LiDARs and cameras, a crucial task for autonomous vehicle perception systems. The proposed method is notable for its ability to handle various sensor combinations, including LiDAR-LiDAR, camera-camera, and cross-modality setups such as LiDAR-camera systems.

Method Overview

The calibration method is structured into two primary stages: reference point extraction and sensor registration. The first stage involves detecting a custom calibration target using sensory data, extracting key reference points based on the target’s features. These reference points are used to determine the optimal transformation that aligns the coordinate systems of the different sensors involved. This technique is robust even when dealing with devices having disparate resolutions and orientations, a common scenario in vehicle-mounted sensory arrays.

Key Contributions

1. modal Flexibility: The method is adaptable to various sensor pairings, offering a broad utility across different automotive sensor configurations.
2. Automatic and Efficient: The approach minimizes human intervention by utilizing a novel fiducial calibration target alongside automated data extraction and processing techniques.
3. Simulation-Based Evaluation: A unique evaluation framework based on synthetic environments is introduced, providing a fair and precise benchmark for calibration algorithms by leveraging perfect ground truth data.
4. Robust to Noise and Distortions: Experiments demonstrate the method's resilience to noise, achieving accurate calibration even under realistically noisy conditions.

Experimental Results

The paper presents strong numerical benchmarks validating the efficacy of the proposed method. Experiments conducted in a controlled simulation environment reveal superior performance compared to existing methods, with notable improvements in both linear and angular accuracy. On average, the calibration errors were reduced significantly when the method's clustering and multi-pose strategies were employed, achieving subcentimeter accuracy with several calibration pattern poses.

Moreover, real-world tests further confirmed the method’s practical applicability, as the calibration results enabled precise sensor data alignment in diverse traffic scenarios. This practical assessment underscores the method's potential for deployment in real autonomous vehicle systems.

Implications for Autonomous Driving

The capability to automatically and accurately calibrate multiple types of sensors is critical for enhancing the perception systems of autonomous vehicles. The proposed method's flexibility and robustness imply a greater reliability in sensor fusion processes, an essential component for environmental understanding and safe vehicle navigation.

Future Directions

The research opens avenues for multiple enhancements, including:

• Automated parameter tuning: Further reducing the need for manual setup and increasing user-friendliness.
• Dynamic recalibration: Adapting the method for use in shifting sensor arrangements, addressing potential miscalibration events during operation.
• Enhanced target detection: Implementing more sophisticated algorithms to improve target isolation and detection in cluttered or dynamic environments.

The development of advanced methods like this one is vital as the field progresses towards fully autonomous systems, ensuring precise and efficient integration of diverse sensory data streams. This paper's contributions represent a substantial step forward in achieving reliable, seamless sensor collaboration in complex automotive platforms.