Extrinsic Calibration Research Articles

Quantitative Microscale Thermometry in Droplets Loaded with Gold Nanoparticles.

Gold nanoparticles (AuNPs) are increasingly used for their thermoplasmonic properties, i.e., their ability to convert light energy into heat through plasmon resonance. However, measuring temperature gradients generated at the microscale by assemblies of AuNPs remains challenging, especially for random 3D distributions of AuNPs. Here, we introduce a label-free thermometry approach, combining quantitative wavefront microscopy and numerical simulations, to infer the heating power dissipated by a 3D model system consisting of emulsion microdroplets loaded with AuNPs. This approach gives access to the temperature reached in the droplets under laser irradiation without the need for extrinsic calibration. This versatile thermometry method is promising for noninvasive temperature measurements in various 3D microsystems involving AuNPs as colloidal heat sources, including photothermal drug delivery systems.

Extrinsic Calibration of a Binocular Camera and LiDAR Based on Neural Networks

Extrinsic Calibration of a Binocular Camera and LiDAR Based on Neural Networks

Extrinsic calibration of non-overlapping multi-camera system with high precision using circular encoded point ruler

In the field of visual metrology, achieving coordinate system unification among non-overlapping multi-camera systems in wide field-of-view scenarios poses significant challenges due to the absence of overlapping fields of view. This paper presents a novel calibration method for such multi-camera systems utilizing a ruler equipped with double-layer circular encoded landmarks. The fabrication of the ruler is simple, and its length covers the entire multi-camera measurement system, rendering the calibration process convenient. To calibrate the cameras, the ruler is positioned in front of the target cameras, and images of the ruler are captured by the multi-cameras. By establishing the initial transformation relationships between the non-overlapping cameras and the ruler's coordinate system, the proposed method efficiently determines the inter-camera transformations. Subsequently, by moving the ruler to different positions and capturing corresponding images, the distances between the encoded center points on the ruler and other encoded points, as captured by the non-overlapping cameras, are kept constant. An optimization-based objective function is formulated to minimize the three-dimensional distances between the measured encoded center points, thereby refining the initial transformation relationships. Combining the coplanarity constraint of the coding center points with the extrinsic results obtained through distance optimization, a secondary optimization was conducted to obtain the final transformation parameters. Experimental results demonstrate the simplicity and versatility of this method, which does not rely on complex constraints. Moreover, it exhibits high calibration accuracy, with maximum distance errors between ruler points and a fitting standard plane error both below 0.275 mm and 0.132 mm, respectively. Following multiple experimental iterations, the average disparity between the radius of the standard cylinder measured via line laser scanning and its true value was found to be 0.022 mm. The average maximum distance from the point cloud to the fitted cylinder surface was determined to be 0.281 mm, while the average distance, computed across multiple instances, was 0.048 mm. Consequently, the proposed approach proves effective for calibrating non-overlapping camera systems in various applications.

A Method for Synchronous Automated Extrinsic Calibration of LiDAR and Cameras Based on a Circular Calibration Board

A Method for Synchronous Automated Extrinsic Calibration of LiDAR and Cameras Based on a Circular Calibration Board

LCE-Calib: Automatic LiDAR-Frame/Event Camera Extrinsic Calibration With a Globally Optimal Solution

The combination of light detection and rangings (LiDARs) and cameras enables a mobile robot to perceive environments with multimodal data, becoming a key factor in achieving robust perception. Traditional frame cameras are sensitive to changing illumination conditions, motivating us to introduce novel event cameras to make LiDAR-camera fusion more complete and robust. However, to jointly exploit these sensors, the challenging extrinsic calibration problem should be addressed. This article proposes an automatic checkerboard-based approach to calibrate extrinsics between a LiDAR and a frame/event camera, where the following four contributions are presented: 1) we present an automatic feature extraction and checkerboard tracking method from LiDAR's point clouds; 2) we reconstruct realistic frame images from event streams, applying traditional corner detectors to event cameras; 3) we propose an initialization-refinement procedure to estimate extrinsics using point-to-plane and point-to-line constraints in a coarse-to-fine manner; 4) we introduce a unified and globally optimal solution to address two optimization problems in calibration. Our approach has been validated with extensive experiments on 19 simulated and real-world datasets and outperforms the state-of-the-art.

Online Self-Calibration for Visual-Inertial Navigation: Models, Analysis, and Degeneracy

As sensor calibration plays an important role in visual-inertial sensor fusion, this article performs an in-depth investigation of online self-calibration for robust and accurate visual-inertial state estimation. To this end, we first conduct complete observability analysis for visual-inertial navigation systems (VINS) with full calibration of sensing parameters, including inertial measurement unit (IMU)/camera intrinsics and IMU-camera spatial-temporal extrinsic calibration, along with readout time of rolling shutter (RS) cameras (if used). We study different inertial model variants containing intrinsic parameters that encompass most commonly used models for low-cost inertial sensors. With these models, the observability analysis of linearized VINS with full sensor calibration is performed. Our analysis theoretically proves the intuition commonly assumed in the literature—that is, VINS with full sensor calibration has four unobservable directions, corresponding to the system's global yaw and position, while all sensor calibration parameters are observable given fully excited motions. Moreover, we, for the first time, identify degenerate motion primitives for IMU and camera intrinsic calibration, which, when combined, may produce complex degenerate motions. We compare the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online</i> self-calibration on commonly used IMUs against the state-of-art <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">offline</i> calibration toolbox Kalibr, showing that the proposed system achieves better consistency and repeatability. Based on our analysis and experimental evaluations, we also offer practical guidelines to effectively perform online IMU-camera self-calibration in practice.

Extrinsic Calibration of Visual and Inertial Sensors for the Autonomous Vehicle

The extrinsic calibration of visual-inertial sensors has always been a complex problem for autonomous vehicles because the vehicle usually moves on a plane, rendering the calibration problem degenerate. Even if the vehicle drives on a slope, many great works are ineffective due to insufficient excitation. To address these challenges, we propose a method for calibrating the extrinsic parameters of camera and IMU sensors for vehicles, which involves two key contributions. The first is the introduction of a virtual vehicle reference frame, acting as a bridge to calibrate rotation in degenerate case. This involves formulating two least-squares problems to estimate the 3D relative rotation of the camera-IMU. Secondly, we propose an approach for estimating camera-IMU translation when a vehicle is navigating on a slope. This is done by solving a linear system, and these values are then refined using an algorithm that takes into account the gravitational magnitude. Extensive experimental results show that our proposed method effectively estimates rotation in planar motion and translation in sloping motion. Moreover, it achieves competitive accuracy and consistency when compared to state-of-the-art approaches.

A fast and accurate camera-IMU calibration method for localization system

&lt;p&gt;Autonomous driving has spurred the development of sensor fusion techniques, which combine data from multiple sensors to improve system performance. In particular, a localization system based on sensor fusion, such as Visual Simultaneous Localization and Mapping (VSLAM), plays a crucial role in environment perception and serves as the foundation for decision-making and motion control in intelligent vehicles. The accuracy of extrinsic calibration parameters between the camera and IMU is of utmost importance for precise positioning in VSLAM systems. However, existing calibration methods are often time-consuming, rely on complex optimization techniques, and are sensitive to noise and outliers, leading to potential degradation in system performance. To address these challenges, this paper presents a fast and accurate camera-IMU calibration method based on space coordinate transformation constraints and SVD (Singular Value Decomposition) tricks. The method involves constructing constraint equations by ensuring the equality of rotation and transformation matrices between camera frames and IMU coordinates at different time instances. Subsequently, the external parameters of the camera-IMU system are solved using quaternion transformation and SVD techniques. To validate the proposed method, experiments were conducted using the ROS (Robot Operating System) platform, where camera images and velocity, acceleration, and angular velocity data from the IMU were recorded in a ROS bag file. The results demonstrate that the proposed method achieves reliable camera-IMU calibration parameters, requiring less tuning time and exhibiting reduced uncertainty.&lt;/p&gt;

Accurate extrinsic calibration of solid-state light detection and ranging and camera system by coarse-to-fine grid-aligning

Accurate extrinsic calibration of solid-state light detection and ranging and camera system by coarse-to-fine grid-aligning

Tightly Coupled, Graph-Based DVL/IMU Fusion and Decoupled Mapping for SLAM-Centric Maritime Infrastructure Inspection

In this article, we address the problem of simultaneous localization and mapping (SLAM)-centric maritime infrastructure inspection [using unmanned surface vehicles (USVs)] via novel approaches in tightly-coupled, graph-based DVL/IMU fusion and decoupled mapping. As our first contribution, we formalize the preintegration of linear velocity measurements, obtained by a Doppler velocity log (DVL), in combination with angular velocity measurements, obtained by an inertial measurement unit (IMU), as binary factors encoding relative position. To evaluate state estimation improvements imparted by DVL/IMU fusion, we implement our proposed factor within a state-of-the-art, graph-based lidar-visual-inertial (LVI) SLAM system as our second contribution. Accuracy and robustness improvements are demonstrated in simulation by comparing maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> pose estimates with and without DVL/IMU fusion against ground truth poses. As our third contribution, we propose a map generation framework for downstream inspection applications decoupled from SLAM. In our framework, volumetric data (captured by sonar, lidar, etc.) is transformed into a common world coordinate frame using extrinsic calibrations and SLAM pose estimates as input. Our framework operates over the complete set of raw volumetric data, whereas SLAM systems (both online and offline) typically operate over a subset of down-sampled volumetric data. To address the processing of additional volumetric data, we present innovations in refined pose correction and staged filtering for user-controlled denoising. We experimentally evaluate our map generation framework against the LVI SLAM system adopted for this study using real-world data and demonstrate improvements to map quality metrics important to inspection.

Pengaruh Penurunan Aktivitas Cobalt-57 Terhadap QC (Quality Control) Harian SPECT (Single Photon Emission Computed Tomography) di Instalasi Kedokteran Nuklir RSUD A. W Sjahranie Samarinda

The utilization of radiation in the field of nuclear medicine uses several main tools, one of them is the gamma camera. In its use, the gamma camera must always be in prime condition in accordance with the regulation of the Badan Pengawas Tenaga Nuklir (BAPETEN) No. 17 of 2012. To ensure that the gamma camera is always in prime condition, it is necessary to carry out several QCs (Quality Control) within a certain period of time with reference to international standards and recommendations from manufacturers. QC results show the measured value still within the required range (acceptance criteria), so it can be said that the gamma camera is in prime condition and ready to be used. The Nuclear Medicine Installation of RSUD A. W Sjahranie Samarinda use Cobalt-57 flood source plates to measure extrinsic uniformity during daily QC. This study used data in the form of tables of extrinsic calibration measurements and verification measurements as well as graphs of Cobalt-57 activity against QC time with extrinsic calibration methods and verification measurements were shown in this study. The conclusion is the lower the activity, the longer the QC times. Besides, the QC times based on the extrinsic callibration and verification measurements are not much different.

Dual LIDAR online calibration and mapping and perception system

Making autonomous mobile devices capable of autonomous positioning and map building in a GPS-denied environment as well as being able to circumvent people in the operating area simultaneously, is essential for many UGV appliances. In this paper, a mapping and positioning system using dual LIDAR is proposed for positioning and building maps. The system may also detect people in the operating space of the equipment using infrared heat maps. The localization and mapping information from each LIDAR is unified in a loosely coupled approach after simultaneous extrinsic calibration. We propose a method for constructing factor graph using LIDAR point cloud geometric features to optimally solve the dual LIDAR extrinsic in real time. We tested the localization accuracy with a publicly available dataset and three real-world scenarios and compared it with three existing methods. The test results in open-source datasets and real scenarios show that our proposed method improves the position and attitude estimation by up to 50% compared to the three existing methods, and the proposed dual LIDAR extrinsic calibration method can achieve an estimation accuracy of 0.05 m for the translational extrinsic and 1deg for the rotational extrinsic. In addition, our proposed method achieves well position and attitude estimation when other existing methods show severe drift in trajectory estimation in real scenarios. Thus, our proposed method is suitable for obtaining high accuracy measurements of LIDAR extrinsic parameters and for dense mapping and accurate localization in the environment of GNSS-denied and human mobility.

Globally Optimal Robust Radar Calibration in Intelligent Transportation Systems

Radar is among the most popular sensors in modern Intelligent Transportation Systems (ITSs), enabling weather-robust perception. The orientation and position of the traffic radar relative to the ITS coordinate system are necessary for the perception fusion in ITSs. However, due to the unknown target association, sparseness and noisiness of traffic radar measurements, the robust and accurate extrinsic calibration of traffic radar is challenging. In this paper, we propose a targetless traffic radar calibration method based on GPS to overcome the inconvenience during ITS operation, because the installation of a dedicated calibration target on the highway is impractical and dangerous. On the other hand, the high-precision GPS device installed on the moving vehicle can provide traffic radar with accurate positioning information of the detection target. Furthermore, during the optimization process of extrinsic calibration, we propose a globally optimal registration method, which is robust to noise and outliers in radar measurements, and is called Gaussian Mixture Robust Branch and Bound (GMRBnB). Specifically, we first construct the robust objective function by utilizing the Gaussian Mixture Model (GMM). Then, we derive novel relaxation bounds and present the GMRBnB algorithm that overcomes the susceptibility to local minima and the dependence on initialization of traditional optimization methods. Compared with existing methods, extensive experiments in synthetic and real-world data demonstrate that our method is not only globally optimal, but also more accurate and robust.

Extrinsic calibration of 2D laser range finder and camera using photogrammetric test field and point-on-plane constraint

Extrinsic calibration of 2D laser range finder and camera using photogrammetric test field and point-on-plane constraint

High-Accuracy Patternless Calibration of Multiple 3-D LiDARs for Autonomous Vehicles

This paper proposes a new method for estimating the extrinsic calibration parameters between any pair of multi-beam LiDAR sensors on a vehicle. Unlike many state-of-the-art works, this method does not use any calibration pattern or reflective marks placed in the environment to perform the calibration; in addition, the sensors do not need to have overlapping fields of view. An iterative ICP-based process is used to determine the values of the calibration parameters, resulting in better convergence and improved accuracy. Furthermore, a setup based on the CARLA simulator is introduced to evaluate the approach, enabling quantitative assessment with ground-truth data. The results show an accuracy comparable with other approaches that require more complex procedures and have a more restricted range of applicable setups. This work also provides qualitative results on a real setup, where the alignment between the different point clouds can be visually checked. The open-source code is available at https://github.com/midemig/pcd_calib.

Targetless Extrinsic Calibration of Camera and Low-Resolution 3-D LiDAR

Autonomous driving heavily relies on LiDAR and camera sensors, which can significantly improve the performance of perception and navigation tasks when fused together. The success of this cross-modality fusion hinges on accurate extrinsic calibration. In recent years, targetless LiDAR-camera calibration methods have gained increasing attention thanks to their independence from external targets. Nevertheless, developing a targetless method for low-resolution LiDARs remains challenging due to the difficulty in extracting reliable features from point clouds with limited LiDAR beams. In this paper, we propose a robust targetless method to solve this struggling problem. It can automatically estimate accurate LiDAR and camera poses and solve the extrinsic matrix through hand-eye calibration. Moreover, we also carefully analyze pose estimation issues existing in the low-resolution LiDAR and present our solution. Real-world experiments are carried out on a UGV-mounted multi-sensor platform containing a CCD camera and a VLP-16 LiDAR. For evaluation, we use a state-of-the-art target-based calibration approach to generate the ground-truth extrinsic parameters. Experimental results demonstrate that our method achieves low calibration error in both translation (3 cm) and rotation (0.59 degrees).

Extrinsic calibration of a camera and a 2D laser range finder using ping pong balls and the corner of a room

Three different methods for extrinsic calibration of a 2D laser range finder and a camera using ping pong balls and the corner of a room are presented and compared with four recent state-of-the-art methods. In the first method, a servo motor and gimbal were used to rotate the laser rangefinder to produce a 3D point cloud from the test field. In the second method, the laser range finder was solely used to get a 2D point cloud of the ping pong balls. In the third approach by employing the pyramid that was built in the room's corner and resolving the perspective-three-point problem, the 2D LRF calibration parameters with respect to the test field were determined with just one scan. In comparison to the estimated values produced by the third approach, the extrinsic calibration parameters derived with the 3D point cloud were more accurate. The second approach produced the worst outcomes, although it was the most straightforward approach.

An incremental LiDAR/POS online calibration method

Extrinsic Calibration between LiDAR and POS (position and orientation system) is a fundamental prerequisite for varieties of MLS (mobile laser scanner) applications. Due to the sparse structure of LiDAR data, the current calibration methods relying on common point feature matching are unreliable, and the low accuracy POS results make the extrinsic calibration of MLS system more challenging. In this paper, we propose an incremental estimation method of six degree of freedom extrinsic transformation of LiDAR and POS. Firstly, the POS-SLAM is used to accumulate LiDAR scans as online sub maps. Attitudes of the carrier are calculated by using GNSS/INS loose combination method of bidirectional adjustment, and scans are associated with sub map based on the time interpolation. Then, the extrinsic calibration parameters are estimated by optimizing corresponding points difference between SLAM and MLS coordinate frame. Finally, field tests have been conducted to the proposed method. RMS between the map by the calibrated MLS and by the static measurement is 0.57 cm. The results demonstrate that the accuracy and robustness of our calibration approach are sufficient for mapping requirement of MLS.

Flexible structured light system calibration method with all digital features.

We propose an innovative method for single-camera and single-projector structured light system calibration in that it eliminates the need for calibration targets with physical features. Instead, a digital display such as a liquid crystal display (LCD) screen is used to present a digital feature pattern for camera intrinsic calibration, while a flat surface such as a mirror is used for projector intrinsic and extrinsic calibration. To carry out this calibration, a secondary camera is required to facilitate the entire process. Because no specially made calibration targets with real physical features are required for the entire calibration process, our method offers greater flexibility and simplicity in achieving accurate calibration for structured light systems. Experimental results have demonstrated the success of this proposed method.

Observability-Aware Online Multi-Lidar Extrinsic Calibration

Accurate and robust extrinsic calibration is necessary for deploying autonomous systems which need multiple sensors for perception. In this paper, we present a robust system for real-time extrinsic calibration of multiple lidars in vehicle base frame without the need for any fiducial markers or features. We base our approach on matching absolute GNSS (Global Navigation Satellite System) and estimated lidar poses in real-time. Comparing rotation components allows us to improve the robustness of the solution than traditional least-square approach comparing translation components only. Additionally, instead of comparing all corresponding poses, we select poses comprising maximum mutual information based on our novel observability criteria. This allows us to identify a subset of the poses helpful for real-time calibration. We also provide stopping criteria for ensuring calibration completion. To validate our approach extensive tests were carried out on data collected using Scania test vehicles (7 sequences for a total of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 6.5 Km). The results presented in this paper show that our approach is able to accurately determine the extrinsic calibration for various combinations of sensor setups.