An Effective Camera-to-Lidar Spatiotemporal Calibration Based on a Simple Calibration Target

The data fusion of a 3-D light detection and ranging (LIDAR) point cloud and a camera image during the creation of a 3-D map is important because it enables more efficient object classification by autonomous mobile robots and facilitates the construction of a fine 3-D model. The principle behind data fusion is the accurate estimation of the LIDAR–camera’s external parameters through extrinsic calibration. Although several studies have proposed the use of multiple calibration targets or poses for precise extrinsic calibration, no study has clearly defined the relationship between the target positions and the data fusion accuracy. Here, we strictly investigated the effects of the deployment of calibration targets on data fusion and proposed the key factors to consider in the deployment of the targets in extrinsic calibration. Thereafter, we applied a probability method to perform a global and robust sampling of the camera external parameters. Subsequently, we proposed an evaluation method for the parameters, which utilizes the color ratio of the 3-D colored point cloud map. The derived probability density confirmed the good performance of the deployment method in estimating the camera external parameters. Additionally, the evaluation quantitatively confirmed the effectiveness of our deployments of the calibration targets in achieving high-accuracy data fusion compared with the results obtained using the previous methods.

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Probability-Based LIDAR–Camera Calibration Considering Target Positions and Parameter Evaluation Using a Data Fusion Map

Yamada,

Yaguchi

2024

“…Furthermore, the four edges of the checkerboard are estimated and aligned to compute the relative offset between the two sensors. Grammatikopoulos et al use a custom-made retro-reflective target paired with an AprilTag [19] fiducial marker to establish correspondence at the center of the target [20]. The relative pose is optimized by solving a Perspective-n-Points (PnP) problem.…”

Section: Related Workmentioning

confidence: 99%

Ca2Lib: Simple and Accurate LiDAR-RGB Calibration Using Small Common Markers

Giacomini,

Brizi,

Di Giammarino

et al. 2024

Modern visual perception techniques often rely on multiple heterogeneous sensors to achieve accurate and robust estimates. Knowledge of their relative positions is a mandatory prerequisite to accomplish sensor fusion. Typically, this result is obtained through a calibration procedure that correlates the sensors’ measurements. In this context, we focus on LiDAR and RGB sensors that exhibit complementary capabilities. Given the sparsity of LiDAR measurements, current state-of-the-art calibration techniques often rely on complex or large calibration targets to resolve the relative pose estimation. As such, the geometric properties of the targets may hinder the calibration procedure in those cases where an ad hoc environment cannot be guaranteed. This paper addresses the problem of LiDAR-RGB calibration using common calibration patterns (i.e., A3 chessboard) with minimal human intervention. Our approach exploits the flatness of the target to find associations between the sensors’ measurements, leading to robust features and retrieval of the solution through nonlinear optimization. The results of quantitative and comparative experiments with other state-of-the-art approaches show that our simple schema performs on par or better than existing methods that rely on complex calibration targets.

“…Grammatikopoulos et al. [ 10 ] presented a highly reflective planar target that could be easily detected in LiDAR along with a visual marker to establish the image correspondence. Ou et al.…”

Section: Related Workmentioning

confidence: 99%

“…Pusztai et al [9] employed a cardboard box for camera-LiDAR calibration and exploited the unique property of three perpendicular planes to efficiently detect the target in the LiDAR point cloud, but this method still required some points to be manually selected in the camera images. Grammatikopoulos et al [10] presented a highly reflective planar target that could be easily detected in LiDAR along with a visual marker to establish the image correspondence. Ou et al [11] presented a highly reflective calibration target that was detectable in both cameras and LiDAR sensors.…”

Section: Related Workmentioning

confidence: 99%

Joint Calibration of a Multimodal Sensor System for Autonomous Vehicles

Muhovič

Perš

2023

Multimodal sensor systems require precise calibration if they are to be used in the field. Due to the difficulty of obtaining the corresponding features from different modalities, the calibration of such systems is an open problem. We present a systematic approach for calibrating a set of cameras with different modalities (RGB, thermal, polarization, and dual-spectrum near infrared) with regard to a LiDAR sensor using a planar calibration target. Firstly, a method for calibrating a single camera with regard to the LiDAR sensor is proposed. The method is usable with any modality, as long as the calibration pattern is detected. A methodology for establishing a parallax-aware pixel mapping between different camera modalities is then presented. Such a mapping can then be used to transfer annotations, features, and results between highly differing camera modalities to facilitate feature extraction and deep detection and segmentation methods.