Search

In the context of autonomous driving, the fusion of LiDAR and camera sensors is essential for robust obstacle detection and distance estimation. However, accurately estimating the transformation matrix between cost-effective low-resolution LiDAR and cameras presents challenges due to the generation of uncertain points by low-resolution LiDAR. In the present work, a new calibration technique is developed to accurately transform low-resolution 2D LiDAR points into camera pixels by utilizing both static and dynamic calibration patterns. Initially, the key corresponding points are identified at the intersection of 2D LiDAR points and calibration patterns. Subsequently, interpolation is applied to generate additional corresponding points for estimating the homography matrix. The homography matrix is then optimized using the Levenberg–Marquardt algorithm to minimize the rotation error, followed by a Procrustes analysis to minimize the translation error. The accuracy of the developed calibration technique is validated through various experiments (varying distances and orientations). The experimental findings demonstrate that the developed calibration technique significantly reduces the mean reprojection error by 0.45 pixels, rotation error by 65.08%, and distance error by 71.93% compared to the standard homography technique. Thus, the developed calibration technique promises the accurate transformation of low-resolution LiDAR points into camera pixels, thereby contributing to improved obstacle perception in intelligent autonomous driving systems. [ABSTRACT FROM AUTHOR]

Recent advances in the fields of driverless cars, intelligent robots and remote-sensing measurement have shown that the use of LiDAR fused with cameras can provide more comprehensive and reliable sensing of surroundings. However, since it is difficult to extract features from sparse LiDAR data to create 3D–2D correspondences, finding a method for accurate external calibration of all types of LiDAR with cameras has become a research hotspot. To solve this problem, this paper proposes a method to directly obtain the 3D–2D correspondences of LiDAR–camera systems to complete accurate calibration. In this method, a laser detector card is used as an auxiliary tool to directly obtain the correspondences between laser spots and image pixels, thus solving the problem of difficulty in extracting features from sparse LiDAR data. In addition, a two-stage framework from coarse to fine is designed in this paper, which not only can solve the perspective-n-point problem with observation errors, but also requires only four LiDAR data points and the corresponding pixel information for more accurate external calibration. Finally, extensive simulations and experimental results show that the effectiveness and accuracy of our method are better than existing methods. [ABSTRACT FROM AUTHOR]

RGB and depth cameras are extensively used for the 3D tracking of human pose and motion. Typically, these cameras calculate a set of 3D points representing the human body as a skeletal structure. The tracking capabilities of a single camera are often affected by noise and inaccuracies due to occluded body parts. Multiple-camera setups offer a solution to maximize coverage of the captured human body and to minimize occlusions. According to best practices, fusing information across multiple cameras typically requires spatio-temporal calibration. First, the cameras must synchronize their internal clocks. This is typically performed by physically connecting the cameras to each other using an external device or cable. Second, the pose of each camera relative to the other cameras must be calculated (Extrinsic Calibration). The state-of-the-art methods use specialized calibration session and devices such as a checkerboard to perform calibration. In this paper, we introduce an approach to the spatio-temporal calibration of multiple cameras which is designed to run on-the-fly without specialized devices or equipment requiring only the motion of the human body in the scene. As an example, the system is implemented and evaluated using Microsoft Azure Kinect. The study shows that the accuracy and robustness of this approach is on par with the state-of-the-art practices. [ABSTRACT FROM AUTHOR]

Calibrating intrinsic and extrinsic camera parameters is a fundamental problem that is a preliminary task for a wide variety of applications, from robotics to computer vision to surveillance and industrial tasks. With the advent of Internet of Things (IoT) technology and edge computing capabilities, the ability to track motion activities in large outdoor areas has become feasible. The proposed work presents a network of IoT camera nodes and a dissertation on two possible approaches for automatically estimating their poses. One approach follows the Structure from Motion (SfM) pipeline, while the other is marker-based. Both methods exploit the correspondence of features detected by cameras on synchronized frames. A preliminary indoor experiment was conducted to assess the performance of the two methods compared to ground truth measurements, employing a commercial tracking system of millimetric precision. Outdoor experiments directly compared the two approaches on a larger setup. The results show that the proposed SfM pipeline more accurately estimates the pose of the cameras. In addition, in the indoor setup, the same methods were used for a tracking application to show a practical use case. [ABSTRACT FROM AUTHOR]

It is too difficult to directly obtain the correspondence features between the two-dimensional (2D) laser-range-finder (LRF) scan point and camera depth point cloud, which leads to a cumbersome calibration process and low calibration accuracy. To address the problem, we propose a calibration method to construct point-line constraint relations between 2D LRF and depth camera observational features by using a specific calibration board. Through the observation of two different poses, we construct the hyperstatic equations group based on point-line constraints and solve the coordinate transformation parameters of 2D LRF and depth camera by the least square (LSQ) method. According to the calibration error and threshold, the number of observation and the observation pose are adjusted adaptively. After experimental verification and comparison with existing methods, the method proposed in this paper easily and efficiently solves the problem of the joint calibration of the 2D LRF and depth camera, and well meets the application requirements of multi-sensor fusion for mobile robots. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel extrinsic calibration method for camera networks by analyzing tracks of pedestrians. First of all, we extract the center lines of walking persons by detecting their heads and feet in the camera images. We propose an easy and accurate method to estimate the 3D positions of the head and feet w.r.t. a local camera coordinate system from these center lines. We also propose a RANSAC-based orthogonal Procrustes approach to compute relative extrinsic parameters connecting the coordinate systems of cameras in a pairwise fashion. Finally, we refine the extrinsic calibration matrices using a method that minimizes the reprojection error. While existing state-of-the-art calibration methods explore epipolar geometry and use image positions directly, the proposed method first computes 3D positions per camera and then fuses the data. This results in simpler computations and a more flexible and accurate calibration method. Another advantage of our method is that it can also handle the case of persons walking along straight lines, which cannot be handled by most of the existing state-of-the-art calibration methods since all head and feet positions are co-planar. This situation often happens in real life. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel extrinsic calibration method for camera networks using a sphere as the calibration object. First of all, we propose an easy and accurate method to estimate the 3D positions of the sphere center w.r.t. the local camera coordinate system. Then, we propose to use orthogonal procrustes analysis to pairwise estimate the initial camera relative extrinsic parameters based on the aforementioned estimation of 3D positions. Finally, an optimization routine is applied to jointly refine the extrinsic parameters for all cameras. Compared to existing sphere-based 3D position estimators which need to trace and analyse the outline of the sphere projection in the image, the proposed method requires only very simple image processing: estimating the area and the center of mass of the sphere projection. Our results demonstrate that we can get a more accurate estimate of the extrinsic parameters compared to other sphere-based methods. While existing state-of-the-art calibration methods use point like features and epipolar geometry, the proposed method uses the sphere-based 3D position estimate. This results in simpler computations and a more flexible and accurate calibration method. Experimental results show that the proposed approach is accurate, robust, flexible and easy to use. [ABSTRACT FROM AUTHOR]

Knowledge of precise camera poses is vital for multi-camera setups. Camera intrinsics can be obtained for each camera separately in lab conditions. For fixed multi-camera setups, the extrinsic calibration can only be done in situ. Usually, some markers are used, like checkerboards, requiring some level of overlap between cameras. In this work, we propose a method for cases with little or no overlap. Laser lines are projected on a plane (e.g., floor or wall) using a laser line projector. The pose of the plane and cameras is then optimized using bundle adjustment to match the lines seen by the cameras. To find the extrinsic calibration, only a partial overlap between the laser lines and the field of view of the cameras is needed. Real-world experiments were conducted both with and without overlapping fields of view, resulting in rotation errors below 0.5°. We show that the accuracy is comparable to other state-of-the-art methods while offering a more practical procedure. The method can also be used in large-scale applications and can be fully automated. [ABSTRACT FROM AUTHOR]

Extrinsic camera calibration is essential for any computer vision task in a camera network. Typically, researchers place a calibration object in the scene to calibrate all the cameras in a camera network. However, when installing cameras in the field, this approach can be costly and impractical, especially when recalibration is needed. This paper proposes a novel, accurate and fully automatic extrinsic calibration framework for camera networks with partially overlapping views. The proposed method considers the pedestrians in the observed scene as the calibration objects and analyzes the pedestrian tracks to obtain extrinsic parameters. Compared to the state of the art, the new method is fully automatic and robust in various environments. Our method detect human poses in the camera images and then models walking persons as vertical sticks. We apply a brute-force method to determines the correspondence between persons in multiple camera images. This information along with 3D estimated locations of the top and the bottom of the pedestrians are then used to compute the extrinsic calibration matrices. We also propose a novel method to calibrate the camera network by only using the top and centerline of the person when the bottom of the person is not available in heavily occluded scenes. We verified the robustness of the method in different camera setups and for both single and multiple walking people. The results show that the triangulation error of a few centimeters can be obtained. Typically, it requires less than one minute of observing the walking people to reach this accuracy in controlled environments. It also just takes a few minutes to collect enough data for the calibration in uncontrolled environments. Our proposed method can perform well in various situations such as multi-person, occlusions, or even at real intersections on the street. [ABSTRACT FROM AUTHOR]