다중 평면의 3차원 모서리를 이용한 레이저 거리센서 및 카메라의 정밀 보정

This study identifies a way to accurately estimate extrinsic calibration parameters between stereo vision camera and 2D laser range finder (LRF) based on 3D reconstruction of monochromatic calibration board and geometric co-planarity constraints between the views from these two sensors. It supports automatic extraction of plane-line correspondences between camera and LRF using monochromatic board, which is further improved by selecting optimal threshold values for laser scan dissection to extract line features from LRF data. Calibration parameters are then obtained by solving co-planarity constraints between the estimated plane and line. Furthermore, the obtained parameters are refined by minimising reprojection error and error from the co-planarity constraints. Moreover, calibration accuracy is achieved because of extraction of reliable plane-line correspondence using monochromatic board which reduces the impact of range-reflectivity-bias observed in LRF data on checkerboard . As the proposed method supports to automatically extract feature correspondences, it provides a major reduction in time required from an operator in comparison to manual methods. The performance is validated by extensive experimentation and simulation, and estimated parameters from the proposed method demonstrate better accuracy than conventional methods.

This paper proposes a framework for a real-time car detection method using calibrated system of camera and Laser Range Finder (LRF). Car candidates are extracted from the LRF data using a gridding method. The points sensed by LRF are grouped into 2D grid. Two adjacent occupied grid elements are marked with same label, forming an object. The objects formed by the labeling method are filtered out based on their size. A region of interest (ROI) in camera image is generated for each object located in 2D grid using the property of the calibrated camera and LRF system. From each ROI, Histogram of oriented gradient (HOG) features are extracted. In order to achieve a faster computation time, the dimension of the HOG feature is reduced using genetic algorithm approach, with a machine learning approach as the validation method. Experiments result shows that the proposed framework achieves around 68 fps of processing speed.

The combination of a camera and a 2D laser rangefinder (LRF) is widely used in robotics, mapping, and unmanned driving to simultaneously obtain the 3D geometry and color texture of a scene. However, data misregistration between the camera and the LRF frequently occurs due to the difficulty of precise installation and alignment between them. Extrinsic calibration between the camera and the LRF is necessary. In this study, a photogrammetric control field is used to perform a robust and accurate calibration between the camera and the LRF which requires only one shot. With the use of the photogrammetric control field as the common reference, extrinsic calibration between two sensors is decoupled by calibrating each sensor separately. First, using the room corner of the control field, the LRF is calibrated with respect to the control field by solving a simplified perspective-three-point problem. Then, a large number of control points are used to obtain the robust and accurate extrinsic parameters of the camera with respect to the control field. Experiments with simulated and real data were performed. The experimental results show that the proposed scheme is accurate, precise, and robust under different noise levels, and the calibration results can be utilized in indoor and outdoor applications.

<p>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.</p>

This paper describes the design, characterization and preliminary testing of a relative navigation module based on electro-optical sensors and tailored for close-proximity operations with respect to passively cooperative targets of the CubeSat class. The proposed unit relies on a laser range finder with a relatively large beam divergence and a visual camera. The laser range finder operates in the near-infrared band and is exploited to both provide direct range measurements and actively illuminate the scene. The visual sensor and its optics are coherently chosen and allow obtaining full pose (i.e., relative position and attitude) measurements by processing the acquired target’s images. The pose estimation procedure is based on the detection and identification of a set of fiducial markers installed on the target surface and highly reflective in the near-infrared band. Experimental tests are carried out at components level to both characterise the laser range finder and preliminarily assess the performance of the proposed pose determination approach. The results show that the proposed approach is robust to a large number of outliers, produced by highly reflective insulation layers typically covering satellite surfaces. Also, in all the considered test cases pose estimates with pixel-level reprojection errors, corresponding to sub-millimetre accuracy, are obtained.

Purpose This paper aims to propose a measurement principle and a calibration method of measurement system integrated with serial robot and 3D camera to identify its parameters conveniently and achieve high measurement accuracy. Design/methodology/approach A stiffness and kinematic measurement principle of the integrated system is proposed, which considers the influence of robot weight and load weight on measurement accuracy. Then an error model is derived based on the principle that the coordinate of sphere center is invariant, which can simultaneously identify the parameters of joint stiffness, kinematic and hand-eye relationship. Further, considering the errors of the parameters to be calibrated and the measurement error of 3D camera, a method to generate calibration observation data is proposed to validate both calibration accuracy and parameter identification accuracy of calibration method. Findings Comparative simulations and experiments of conventional kinematic calibration method and the stiffness and kinematic calibration method proposed in this paper are conducted. The results of the simulations show that the proposed method is more accurate, and the identified values of angle parameters in modified Denavit and Hartenberg model are closer to their real values. Compared with the conventional calibration method in experiments, the proposed method decreases the maximum and mean errors by 19.9% and 13.4%, respectively. Originality/value A new measurement principle and a novel calibration method are proposed. The proposed method can simultaneously identify joint stiffness, kinematic and hand-eye parameters and obtain not only higher measurement accuracy but also higher parameter identification accuracy, which is suitable for on-site calibration.

One major challenge of 3DTV is content acquisition. Here, we present a method to acquire a realistic, visually convincing 3D model of indoor environments based on a mobile platform that is equipped with a laser range scanner and a panoramic camera. The data of the 2D laser scans are used to solve the simultaneous localization and mapping problem and to extract walls. Textures for walls and floor are built from the images of a calibrated panoramic camera. Multiresolution blending is used to hide seams in the generated textures. The scene is further enriched by 3D-geometry calculated from a graph cut stereo technique. We present experimental results from a moderately large real environment. 1

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

This paper describes a method of generating 3D indoor environment model by a mobile robot with laser range finder. Generally, there are more complicated objects in indoor environment than those in outdoor environment, and it is an issue how we model them. First, we obtain 3D range data by moving the laser range finder which is put on an autonomous mobile robot verticaly. Next, we rearrange the data in order to make it easier to generate planes from the rearranged data. Then we generate planes from data. Finally, we make textures which are suitable for planes from the images of cameras, and map them on the planes.

A sensor-based model of a service robot's environment is a prerequisite for interaction. Such a model should contain the positions of the robot's interaction partners. Additionally to the actual positions of the partners it is important for the service robot to predict their possible future positions. This knowledge could for example be used to realize efficient path planning for delivery tasks. In this paper we propose an extensible framework for systems that combine different sensor modalities in a general tracking system. Exemplarily, a tracking system is implemented that fuses tracking algorithms in laser range scans as well as in camera images by a particle filter. We point out the real-time capabilities of our tracking algorithms implementation. Practical experiments show that multimodality increases the system's robustness to incorrect measurements of single sensors. Furthermore, the observed trajectories are generalized to trajectory patterns by a novel method which uses self organizing maps. Those patterns are used to predict trajectories of the currently observed persons. It is also demonstrated that a self organizing map is suitable for learning and generalizing trajectories. Convenient predictions of future trajectories are presented which are deduced from these generalizations

The road geometry is one of the most important indices that reflect the road condition. In order to collect this data, scholars developed several detection methods in recent years. This paper introduced a Road Geometry Detection System based on section measurement method. The detection system applies a inertial measurement unit (IMU) and laser rangefinders (LRFs) fixed on a measuring platform to capture the data of the slope and cross slope of the pavement. However, the detection system is limited as it needs to calibrate the system before a measuring task and this problem affect the measurement accuracy seriously. This paper proposed an efficient method to calculate the angles between measuring platform and each LRFs, which improve the detection accuracy effectively. Finally, a case study showed the proposed detection system and calibrating method was feasible and effective.

Development of a Vehicle-Mounted Road Surface 3D Measurement System Kiichiro Ishikawa, Takashi Onishi, Yoshiharu Amano, Takumi Hashizume, Jun-ichi Takiguchi, Takashi Fujishima, Yoichi Tanaka Pages 569-573 (2006 Proceedings of the 23rd ISARC, Tokyo, Japan, ISBN 9784990271718, ISSN 2413-5844) Abstract: In recent years, automation system based on IT technology becomes very popular not only for manufacturing industries but also for the construction and civil engineering industries. The conventional location survey method in the civil engineering construction site is pinpoint location survey for a broad objective area. The conventional method has severe constraints when it comes to measure the broad objective area as macro or to estimate general distinctive geometrical features. In this study, a unique vehicle-mounted mobile measurement system which can measure dense 3D point-cloud data for wide construction site is presented. The proposed MMS (Mobile Mapping System) is equipped with the carrier phased D-GPS/DR combined navigation system, the GPS gyro, and a laser scanner. The MMS, which measures unleveled round surface at 40 [km/h] using laser scanner's range data and vehicle attitude stabilization algorithm, can construct continuous dense road surface 3D model. The functional and performance test as a mobile measurement instrument is revealed. Keywords: GPS-Gyro/IMU, Mobile Mapping System, as-built management, 3D mesurement DOI: https://doi.org/10.22260/ISARC2006/0107 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley

This paper presents a sensor fusion framework that incorporates a monocular vision system, laser range finder and an inertial navigation sensor (INS) for localization of mobile robots and manipulators. The proposed method is particularly useful for applications in which there is a featured wall or floor in proximity to the robots. Examples include unmanned ground vehicles (UGV), unmanned aerial vehicles (UAV), and field inspection robots. In essence, the proposed method fuses image mosaicing and dead reckoning where the sequential images of a digital camera are stitched together with the help of an inertial navigation system (INS). In the proposed model, the processing load of image mosaicing is reduced significantly, and at the same time accumulation of error of the INS is prevented. The localization method was developed as a standalone module and tested using a hardware-in-the-loop simulator explained in this paper. This module will eventually be used for autonomous navigation of a pipe inspection robot capable of carrying nondestructive testing (NDT) sensors and visual inspection instruments into large water mains.

This paper proposes a calibration method for monocular cameras and 2D Lidar external parameters based on automatic detection of features. The triangle calibration target is used, and the point and line features are automatically extracted. The point line constraint and the proposed European transformation invariance constraint are applied simultaneously in the calibration process to obtain more accurate and effective external parameters. First, a line feature extraction method is proposed. The triangle region in the image is extracted by Hough transform and K-means clustering, and the 3D pose of the triangle calibration target is calculated. Second, we analyze the effects of image feature extraction and Lidar measurement range errors on rotating and translational external parameters, respectively, through simulation experiments. The error characteristics of camera-lidar external parameters are analyzed and compared with those of Zhang and Pless. Finally, real-data experiments show that our method can obtain accurate external calibration.

A number of studies have been conducted to enhance the pedestrian detection accuracy of intelligent surveillance systems. However, detecting pedestrians under outdoor conditions is a challenging problem due to the varying lighting, shadows, and occlusions. In recent times, a growing number of studies have been performed on visible light camera-based pedestrian detection systems using a convolutional neural network (CNN) in order to make the pedestrian detection process more resilient to such conditions. However, visible light cameras still cannot detect pedestrians during nighttime, and are easily affected by shadows and lighting. There are many studies on CNN-based pedestrian detection through the use of far-infrared (FIR) light cameras (i.e., thermal cameras) to address such difficulties. However, when the solar radiation increases and the background temperature reaches the same level as the body temperature, it remains difficult for the FIR light camera to detect pedestrians due to the insignificant difference between the pedestrian and non-pedestrian features within the images. Researchers have been trying to solve this issue by inputting both the visible light and the FIR camera images into the CNN as the input. This, however, takes a longer time to process, and makes the system structure more complex as the CNN needs to process both camera images. This research adaptively selects a more appropriate candidate between two pedestrian images from visible light and FIR cameras based on a fuzzy inference system (FIS), and the selected candidate is verified with a CNN. Three types of databases were tested, taking into account various environmental factors using visible light and FIR cameras. The results showed that the proposed method performs better than the previously reported methods.