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Abstract In complicated urban environments, Global Navigation Satellite System (GNSS) signals are frequently affected by building reflection or refraction, resulting in Non-Line-of-Sight (NLOS) errors. In severe cases, NLOS errors can cause a ranging error of hundreds of meters, which has a substantial impact on the precision and dependability of GNSS positioning. To address this problem, we propose a reliable NLOS error identification method based on the Light Gradient Boosting Machine (LightGBM), which is driven by multiple features of GNSS signals. The sample data are first labeled using a fisheye camera to classify the signals from visible satellites as Line-of-Sight (LOS) or NLOS signals. We then analyzed the sample data to determine the correlation among multiple features, such as the signal-to-noise ratio, elevation angle, pseudorange consistency, phase consistency, Code Minus Carrier, and Multi-Path combined observations. Finally, we introduce the LightGBM model to establish an effective correlation between signal features and satellite visibility and adopt a multifeature-driven scheme to achieve reliable identification of NLOSs. The test results show that the proposed method is superior to other methods such as Extreme Gradient Boosting (XGBoost), in terms of accuracy and usability. The model demonstrates a potential classification accuracy of approximately 90% with minimal time consumption. Furthermore, the Standard Point Positioning results after excluding NLOSs show the Root Mean Squares are improved by 47.82%, 56.68%, and 36.68% in the east, north, and up directions, respectively, and the overall positioning performance is significantly improved.

In complicated urban environments, Global Navigation Satellite System (GNSS) signals are frequently affected by building reflection or refraction, resulting in Non-Line-of-Sight (NLOS) errors. In severe cases, NLOS errors can cause a ranging error of hundreds of meters, which has a substantial impact on the precision and dependability of GNSS positioning. To address this problem, we propose a reliable NLOS error identification method based on the Light Gradient Boosting Machine (LightGBM), which is driven by multiple features of GNSS signals. The sample data are first labeled using a fisheye camera to classify the signals from visible satellites as Line-of-Sight (LOS) or NLOS signals. We then analyzed the sample data to determine the correlation among multiple features, such as the signal-to-noise ratio, elevation angle, pseudorange consistency, phase consistency, Code Minus Carrier, and Multi-Path combined observations. Finally, we introduce the LightGBM model to establish an effective correlation between signal features and satellite visibility and adopt a multifeature-driven scheme to achieve reliable identification of NLOSs. The test results show that the proposed method is superior to other methods such as Extreme Gradient Boosting (XGBoost), in terms of accuracy and usability. The model demonstrates a potential classification accuracy of approximately 90% with minimal time consumption. Furthermore, the Standard Point Positioning results after excluding NLOSs show the Root Mean Squares are improved by 47.82%, 56.68%, and 36.68% in the east, north, and up directions, respectively, and the overall positioning performance is significantly improved. [ABSTRACT FROM AUTHOR]

Neural radiance fields (NeRF) have become an effective method for encoding scenes into neural representations, allowing for the synthesis of photorealistic views of unseen views from given input images. However, the applicability of traditional NeRF is significantly limited by its assumption that images are captured for object-centric scenes with a pinhole camera. Expanding these boundaries, we focus on driving scenarios using a fisheye camera, which offers the advantage of capturing visual information from a wide field of view. To address the challenges due to the unbounded and distorted characteristics of fisheye images, we propose an edge-aware integration loss function. This approach leverages sparse LiDAR projections and dense depth maps estimated from a learning-based depth model. The proposed algorithm assigns larger weights to neighboring points that have depth values similar to the sensor data. Experiments were conducted on the KITTI-360 and JBNU-Depth360 datasets, which are public and real-world datasets of driving scenarios using fisheye cameras. Experimental results demonstrated that the proposed method is effective in synthesizing novel view images, outperforming existing approaches. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a leader-follower formation control of multiple mobile robots by position-based method using a fisheye camera. A fisheye camera has a wide field of view and recognizes a wide range of objects. In this paper, the fisheye camera is first modeled on spherical coordinates and then a position estimation technique is proposed by using an AR marker based on the spherical model. This paper furthermore presents a method for estimating the velocity of a leader robot based on a disturbance observer using the obtained position information. The proposed techniques are combined with a formation control based on the virtual structure. In this paper, the formation controller and velocity estimator can be designed independently, and the stability analysis of the total system is performed by using Lyapunov theorem. The effectiveness of the proposed method is demonstrated by simulation and experiments using two real mobile robots.

Ground target detection and positioning systems based on lightweight unmanned aerial vehicles (UAVs) are increasing in value for aerial reconnaissance and surveillance. However, the current method for estimating the target's position is limited by the field of view angle, rendering it challenging to fulfill the demands of a real-time omnidirectional reconnaissance operation. To address this issue, we propose an Omnidirectional Optimal Real-Time Ground Target Position Estimation System (Omni-OTPE) that utilizes a fisheye camera and LiDAR sensors. The object of interest is first identified in the fisheye image, and then, the image-based target position is obtained by solving using the fisheye projection model and the target center extraction algorithm based on the detected edge information. Next, the LiDAR's real-time point cloud data are filtered based on position–direction constraints using the image-based target position information. This step allows for the determination of point cloud clusters that are relevant to the characterization of the target's position information. Finally, the target positions obtained from the two methods are fused using an optimal Kalman fuser to obtain the optimal target position information. In order to evaluate the positioning accuracy, we designed a hardware and software setup, mounted on a lightweight UAV, and tested it in a real scenario. The experimental results validate that our method exhibits significant advantages over traditional methods and achieves a real-time high-performance ground target position estimation function. [ABSTRACT FROM AUTHOR]

To enhance the real-time performance and reduce CPU usage in feature-based visual SLAM, this paper introduces a lightweight tightly coupled stereo-inertial SLAM with fisheye cameras, incorporating several key innovations. First, the stereo-fisheye camera is treated as two independent monocular cameras, and the SE(3) transformation is computed between them to minimize the CPU burden during stereo feature matching and eliminate the need for camera rectification. Another important innovation is the application of maximum-a-posteriori (MAP) estimation for the inertial measurement unit (IMU), which effectively reduces inertial bias and noise in a short time frame. By optimizing the system's parameters, the constant-velocity model is replaced from the beginning, resulting in improved tracking efficiency. Furthermore, the system incorporates the inertial data in the loop closure thread. The IMU data are employed to determine the translation direction relative to world coordinates and utilized as a necessary condition for loop detection. Experimental results demonstrate that the proposed system achieves superior real-time performance and lower CPU usage compared to the majority of other SLAM systems. [ABSTRACT FROM AUTHOR]

Abstract To enhance the real-time performance and reduce CPU usage in feature-based visual SLAM, this paper introduces a lightweight tightly coupled stereo-inertial SLAM with fisheye cameras, incorporating several key innovations. First, the stereo-fisheye camera is treated as two independent monocular cameras, and the SE(3) transformation is computed between them to minimize the CPU burden during stereo feature matching and eliminate the need for camera rectification. Another important innovation is the application of maximum-a-posteriori (MAP) estimation for the inertial measurement unit (IMU), which effectively reduces inertial bias and noise in a short time frame. By optimizing the system’s parameters, the constant-velocity model is replaced from the beginning, resulting in improved tracking efficiency. Furthermore, the system incorporates the inertial data in the loop closure thread. The IMU data are employed to determine the translation direction relative to world coordinates and utilized as a necessary condition for loop detection. Experimental results demonstrate that the proposed system achieves superior real-time performance and lower CPU usage compared to the majority of other SLAM systems.

This paper presents a leader-follower formation control of multiple mobile robots by position-based method using a fisheye camera. A fisheye camera has a wide field of view and recognizes a wide range of objects. In this paper, the fisheye camera is first modeled on spherical coordinates and then a position estimation technique is proposed by using an AR marker based on the spherical model. This paper furthermore presents a method for estimating the velocity of a leader robot based on a disturbance observer using the obtained position information. The proposed techniques are combined with a formation control based on the virtual structure. In this paper, the formation controller and velocity estimator can be designed independently, and the stability analysis of the total system is performed by using Lyapunov theorem. The effectiveness of the proposed method is demonstrated by simulation and experiments using two real mobile robots. [ABSTRACT FROM AUTHOR]

Monocular depth estimation is a task aimed at predicting pixel-level distances from a single RGB image. This task holds significance in various applications including autonomous driving and robotics. In particular, the recognition of surrounding environments is important to avoid collisions during autonomous parking. Fisheye cameras are adequate to acquire visual information from a wide field of view, reducing blind spots and preventing potential collisions. While there have been increasing demands for fisheye cameras in visual-recognition systems, existing research on depth estimation has primarily focused on pinhole camera images. Moreover, depth estimation from fisheye images poses additional challenges due to strong distortion and the lack of public datasets. In this work, we propose a novel underground parking lot dataset called JBNU-Depth360, which consists of fisheye camera images and their corresponding LiDAR projections. Our proposed dataset was composed of 4221 pairs of fisheye images and their corresponding LiDAR point clouds, which were obtained from six driving sequences. Furthermore, we employed a knowledge-distillation technique to improve the performance of the state-of-the-art depth-estimation models. The teacher–student learning framework allows the neural network to leverage the information in dense depth predictions and sparse LiDAR projections. Experiments were conducted on the KITTI-360 and JBNU-Depth360 datasets for analyzing the performance of existing depth-estimation models on fisheye camera images. By utilizing the self-distillation technique, the AbsRel and SILog error metrics were reduced by 1.81% and 1.55% on the JBNU-Depth360 dataset. The experimental results demonstrated that the self-distillation technique is beneficial to improve the performance of depth-estimation models. [ABSTRACT FROM AUTHOR]

Vehicle re-identification (Re-ID) is a critical component of the autonomous driving perception system, and research in this area has accelerated in recent years. However, there is yet no perfect solution to the vehicle re-identification issue associated with the car's surround-view camera system. Our analysis identifies two significant issues in the aforementioned scenario: (1) It is difficult to identify the same vehicle in many picture frames due to the unique construction of the fisheye camera. (2) The appearance of the same vehicle when seen via the surround vision system's several cameras is rather different. To overcome these issues, we suggest an integrative vehicle Re-ID solution method. On the one hand, we provide a technique for determining the consistency of the tracking box drift with respect to the target. On the other hand, we combine a Re-ID network based on the attention mechanism with spatial limitations to increase performance in situations involving multiple cameras. Finally, our approach combines state-of-the-art accuracy with real-time performance. We will soon make the source code and annotated fisheye dataset available. [ABSTRACT FROM AUTHOR]

The fisheye camera, with its large viewing angle, can acquire more spatial information in one shot and is widely used in many fields. However, a fisheye image contains large distortion, resulting in that many scholars have investigated its accuracy of orthorectification, i.e., generation of digital orthophoto map (DOM). This paper presents an orthorectification method, which first determines the transformation relationship between the fisheye image points and the perspective projection points according to the equidistant projection model, i.e., determines the spherical distortion of the fisheye image; then introduces the transformation relationship and the fisheye camera distortion model into the collinearity equation to derive the fisheye image orthorectification model. To verify the proposed method, high accuracy of the fisheye camera 3D calibration field is established to obtain the interior and exterior orientation parameters (IOPs/EOPs) and distortion parameters of the fisheye lens. Three experiments are used to verify the proposed orthorectification method. The root mean square errors (RMSEs) of the three DOMs are averagely 0.003 m, 0.29 m, and 0.61 m, respectively. The experimental results demonstrate that the proposed method is correct and effective. [ABSTRACT FROM AUTHOR]

Cameras are the primary sensor in automated driving systems. They provide high information density and are optimal for detecting road infrastructure cues laid out for human vision. Surround-view camera systems typically comprise of four fisheye cameras with 190°+ field of view covering the entire 360° around the vehicle focused on near-field sensing. They are the principal sensors for low-speed, high accuracy, and close-range sensing applications, such as automated parking, traffic jam assistance, and low-speed emergency braking. In this work, we provide a detailed survey of such vision systems, setting up the survey in the context of an architecture that can be decomposed into four modular components namely Recognition, Reconstruction, Relocalization, and Reorganization. We jointly call this the 4R Architecture. We discuss how each component accomplishes a specific aspect and provide a positional argument that they can be synergized to form a complete perception system for low-speed automation. We support this argument by presenting results from previous works and by presenting architecture proposals for such a system. Qualitative results are presented in the video at https://youtu.be/ae8bCOF77uY. [ABSTRACT FROM AUTHOR]

This research represents localization of an aerial robot using fisheye cameras on walls in a simulation environment. The virtual testbed in this work is a quadrotor that is simulated in MATLAB Simulink. Subsequently, the simulation outputs as flight records are used in a virtual lab, which is developed in 3DsMAX. Then, the virtual fisheye cameras (here two) are installed in some different points on the walls and the related images from the cameras are received offline. The gathered images will be processed by OpenCV in a C++ environment. For external calibration, each fisheye camera takes an image from a known pattern consist of some lights placed in the virtual lab. We execute Perspective-n-Point method on the images to obtain pierce direction/position of the camera. For more, the aerial robot is localized by computing the nearest point between two lines of sight. In brief, the outcomes exhibit an accuracy of 4cm in the center of the virtual-room room.

The fisheye camera has a field of view (FOV) of over 180°, which has advantages in the fields of medicine and precision measurement. Ordinary pinhole models have difficulty in fitting the severe barrel distortion of the fisheye camera. Therefore, it is necessary to apply a nonlinear geometric model to model this distortion in measurement applications, while the process is computationally complex. To solve the problem, this paper proposes a model-free stereo calibration method for binocular fisheye camera based on neural-network. The neural-network can implicitly describe the nonlinear mapping relationship between image and spatial coordinates in the scene. We use a feature extraction method based on three-step phase-shift method. Compared with the conventional stereo calibration of fisheye cameras, our method does not require image correction and matching. The spatial coordinates of the points in the common field of view of binocular fisheye camera can all be calculated by the generalized fitting capability of the neural-network. Our method preserves the advantage of the broad field of view of the fisheye camera. The experimental results show that our method is more suitable for fisheye cameras with significant distortion.

We propose a one-step person detector for topview omnidirectional indoor scenes based on convolutional neural networks (CNNs). While state of the art person detectors reach competitive results on perspective images, missing CNN architectures as well as training data that follows the distortion of omnidirectional images makes current approaches not applicable to our data. The method predicts bounding boxes of multiple persons directly in omnidirectional images without perspective transformation, which reduces overhead of pre- and post-processing and enables realtime performance. The basic idea is to utilize transfer learning to fine-tune CNNs trained on perspective images with data augmentation techniques for detection in omnidirectional images. We fine-tune two variants of Single Shot MultiBox detectors (SSDs). The first one uses Mobilenet v1 FPN as feature extractor (moSSD). The second one uses ResNet50 v1 FPN (resSSD). Both models are pre-trained on Microsoft Common Objects in Context (COCO) dataset. We fine-tune both models on PASCAL VOC07 and VOC12 datasets, specifically on class person. Random 90-degree rotation and random vertical flipping are used for data augmentation in addition to the methods proposed by original SSD. We reach an average precision (AP) of 67.3%with moSSD and 74.9%with resSSD on the evaluation dataset. To enhance the fine-tuning process, we add a subset of HDA Person dataset and a subset of PIROPO database and reduce the number of perspective images to PASCAL VOC07. The AP rises to 83.2% for moSSD and 86.3% for resSSD, respectively. The average inference speed is 28 ms per image for moSSD and 38 ms per image for resSSD using Nvidia Quadro P6000. Our method is applicable to other CNN-based object detectors and can potentially generalize for detecting other objects in omnidirectional images.

Significant progress has been made for face detection from normal images in recent years; however, accurate and fast face detection from fisheye images remains a challenging issue because of serious fisheye distortion in the peripheral region of the image. To improve face detection accuracy, we propose a light-weight location-aware network to distinguish the peripheral region from the central region in the feature learning stage. To match the face detector, the shape and scale of the anchor (bounding box) is made location dependent. The overall face detection system performs directly in the fisheye image domain without rectification and calibration and hence is agnostic of the fisheye projection parameters. Experiments on Wider-360 and real-world fisheye images using a single CPU core indeed show that our method is superior to the state-of-the-art real-time face detector RFB Net. [ABSTRACT FROM AUTHOR]

Rectilinear face recognition models suffer from severe performance degradation when applied to fisheye images captured by 360° back-to-back dual fisheye cameras. We propose a novel face rectification method to combat the effect of fisheye image distortion on face recognition. The method consists of a classification network and a restoration network specifically designed to handle the non-linear property of fisheye projection. The classification network classifies an input fisheye image according to its distortion level. The restoration network takes a distorted image as input and restores the rectilinear geometric structure of the face. The performance of the proposed method is tested on an end-to-end face recognition system constructed by integrating the proposed rectification method with a conventional rectilinear face recognition system. The face verification accuracy of the integrated system is 99.18% when tested on images in the synthetic Labeled Faces in the Wild (LFW) dataset and 95.70% for images in a real image dataset, resulting in an average accuracy improvement of 6.57% over the conventional face recognition system. For face identification, the average improvement over the conventional face recognition system is 4.51%. [ABSTRACT FROM AUTHOR]

This paper proposes a system for tracking human position and head direction using fisheye camera mounted to the ceiling. This is believed to be the first system to estimate head direction from ceiling-mounted fisheye camera. Fisheye histograms of oriented gradients descriptor is developed as a substitute to the histograms of oriented gradients descriptor which has been widely used for human detection in perspective camera. Human body and head are detected by the proposed descriptor and tracked to extract head area for direction estimation. Direction estimation using randomized ferns is adapted to work with fisheye images by using the proposed descriptor, guided by the direction of movement. With experiments on available dataset and new dataset with ground truth, the direction can be estimated with average error below 40 ∘ , with head position error half of the head size. [ABSTRACT FROM AUTHOR]

This paper presents a feature-based simultaneous localization and mapping (SLAM) system for panoramic image sequences obtained from a multiple fisheye camera rig in a wide baseline mobile mapping system (MMS). First, the developed fisheye camera calibration method combines an equidistance projection model and trigonometric polynomial to achieve high-accuracy calibration from the fisheye camera to an equivalent ideal frame camera, which warrants an accurate transform from the fisheye images to a corresponding panoramic image. Second, we developed a panoramic camera model, corresponding bundle adjustment with a specific back propagation error function, and linear pose initialization algorithm. Third, the implemented feature-based SLAM pipeline consists of several specific strategies and algorithms in initialization, feature matching, frame tracking, and loop closing to overcome the difficulties in tracking wide baseline panoramic image sequences. We conducted experiments on large-scale MMS datasets of more than 15 km trajectories and 14,000 panoramic images as well as small-scale public video datasets. Our results show that the developed panoramic SLAM system PAN-SLAM can achieve fully-automatic camera localization and sparse map reconstruction in both small-scale indoor and large-scale outdoor environments including challenging scenes (e.g., dark tunnel) without the aid of any other sensors. The localization accuracy, which was measured by the absolute trajectory error (ATE), resembled the high-accuracy GNSS/INS reference of 0.1 m. PAN-SLAM also outperformed several feature-based fisheye and monocular SLAM systems with incomparable robustness in various environments. The system could be considered as a robust complementary solution and an alternative to expensive commercial navigation systems, especially in urban environments where signal obstruction and multipath interference are common. Source code and demo are available at http://study.rsgis.whu.edu.cn/pages/download/. [ABSTRACT FROM AUTHOR]

Image processing and computer vision have been a focus of researchers for decades in various application domains. This research is continuously rising with the rise of Artificial Intelligence in the fourth industrial revolution. One of the important digital image processing applications is to produce panorama images. The wide range of view a panorama image provides can be used in a variety range of applications which may include surveillance applications and remote robot operations. A panorama image is a combination of several individual natural looking images into a composite one to provide a wide field of view that may reach 360 degrees horizontally without any distortion. Wide-angle lenses provide a wide field of view, but using them alone does not necessarily make a panorama image. In this work the design and implementation of a wide-angle stereo vision system that suites many real-time applications is proposed. The system makes use of two wide-angle fisheye cameras where each camera covers around 170 degrees field of view. The horizontal angle between the cameras is 140 degrees. The cameras acquire the instantaneous overlapping images continuously and transmits them to a base station via a communication link. The base station calibrates, corrects, correlates and stitches the non-overlapping corrected images to a composite one. The resultant final image covers 310 degrees field of view. The system is of low computational complexity compared with previously implemented systems. It is tested on a laptop and on a standalone embedded computing device. The processing speed for the panorama image stitching including the correction of the fisheye barrel distortion on the laptop computer and the embedded computer is 11 fps, and 6 fps, respectively. [ABSTRACT FROM AUTHOR]

Surveying wide areas with only one camera is a typical scenario in surveillance and automotive applications. Ultra wide-angle fisheye cameras employed to that end produce video data with characteristics that differ significantly from conventional rectilinear imagery as obtained by perspective pinhole cameras. Those characteristics are not considered in typical image and video processing algorithms such as motion estimation, where translation is assumed to be the predominant kind of motion. This contribution introduces an adapted technique for use in block-based motion estimation that takes into the account the projection function of fisheye cameras and thus compensates for the non-perspective properties of fisheye videos. By including suitable projections, the translational motion model that would otherwise only hold for perspective material is exploited, leading to improved motion estimation results without altering the source material. In addition, we discuss extensions that allow for a better prediction of the peripheral image areas, where motion estimation falters due to spatial constraints, and further include calibration information to account for lens properties deviating from the theoretical function. Simulations and experiments are conducted on synthetic as well as real-world fisheye video sequences that are part of a data set created in the context of this paper. Average synthetic and real-world gains of 1.45 and 1.51 dB in luminance PSNR are achieved compared against conventional block matching. Furthermore, the proposed fisheye motion estimation method is successfully applied to motion compensated temporal resolution enhancement, where average gains amount to 0.79 and 0.76 dB. [ABSTRACT FROM AUTHOR]

Accurately self-localizing a vehicle is of high importance as it allows to robustify nearly all modern driver assistance functionality, e.g., lane keeping and coordinated autonomous driving maneuvers. We examine vehicle self-localization relying only on video sensors, in particular, a system of four fisheye cameras providing a view surrounding the car, a setup currently growing popular in upper-class cars. The presented work aims at an autonomous parking scenario. The method is based on park markings as orientation marks since they can be found in nearly every parking deck and require only little additional preparation. Our contribution is twofold: (1) we present a new real-time capable image processing pipeline for topview systems extracting park markings and show how to obtain a reliable and accurate ego pose and ego motion estimation given a coarse pose as starting point. (2) The aptitude of this often neglected sensor array for vehicle self-localization is demonstrated. Experimental evaluation yields a precision of 0.15 ± 0.18 m and 2.01 ∘ ± 1.91 ∘ . [ABSTRACT FROM AUTHOR]

Fisheye images with a far larger Field of View (FOV) have severe radial distortion, with the result that the associated image feature matching process cannot achieve the best performance if the traditional feature descriptors are used. To address this challenge, this paper reports a novel distorted Binary Robust Independent Elementary Feature (BRIEF) descriptor for fisheye images based on a spherical perspective model. Firstly, the 3D gray centroid of feature points is designed, and the position and direction of the feature points on the spherical image are described by a constructed feature point attitude matrix. Then, based on the attitude matrix of feature points, the coordinate mapping relationship between the BRIEF descriptor template and the fisheye image is established to realize the computation associated with the distorted BRIEF descriptor. Four experiments are provided to test and verify the invariance and matching performance of the proposed descriptor for a fisheye image. The experimental results show that the proposed descriptor works well for distortion invariance and can significantly improve the matching performance in fisheye images.

Highlights • Use single fisheye camera to cover a hemisphere FOV of MAV. • Use original fisheye images to implement object detection. • Propose a detector that is more accurate and faster than baselines on TX2. • Fuse fisheye model, detection results, attitude and height to localize objects. Abstract In robotic systems, the fisheye camera can provide a large field of view (FOV). Usually, the traditional restoring algorithms are needed, which are computational heavy and will introduce noise into original data, since the fisheye images are distorted. In this paper, we propose a framework to detect objects from the raw fisheye images without restoration, then locate objects in the real world coordinate by fusing attitude information. A deep neural network architecture based on the MobileNet and feature pyramid structure is designed to detect targets directly on the fisheye raw images. Then, the target can be located based on the fisheye visual model and the attitude of the camera. Compared to traditional approaches, this approach has advantages in computational efficiency and accuracy. This approach is validated by experiments with a fisheye camera and an onboard computer on a micro-aerial vehicle (MAV). [ABSTRACT FROM AUTHOR]

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Heading angle is a vital parameter in maintaining a vessel's track along a planned course and should be guaranteed in a stable and reliable way. An innovative method of heading determination based on a fisheye camera, which is almost totally unaffected by electromagnetism and geomagnetism, is proposed in this paper. In addition, unlike traditional astronomical methods, it also has a certain degree of adaptability to cloudy weather. Utilising the super wide Field Of View (FOV) of the camera, it is able to simultaneously image the Moon and the horizon. The Moon is treated as the observed celestial body and the horizon works as the horizontal datum. Two experiments were conducted at sea, successfully proving the feasibility of this method. The proposed heading determination system has the merits of automation, resistance to interference and could be miniaturised, making application viable. [ABSTRACT FROM AUTHOR]

Solar radiation is one of the most significant environmental factors that regulates the rate of photosynthesis, and consequently, growth. Light intensity in the forest can vary both spatially and temporally, so precise assessment of canopy and potential solar radiation can significantly influence the success of forest management actions, for example, the establishment of natural regeneration. In this case study, we investigated the possibilities and perspectives of close-range photogrammetric approaches for modeling the amount of potential direct and diffuse solar radiation during the growing seasons (spring-summer), by comparing the performance of low-cost Unmanned Aerial Vehicle (UAV) RGB imagery vs. Hemispherical Photography (HP). Characterization of the solar environment based on hemispherical photography has already been widely used in botany and ecology for a few decades, while the UAV method is relatively new. Also, we compared the importance of several components of potential solar irradiation and their impact on the regeneration of Pinus sylvestris L. For this purpose, a circular fisheye objective was used to obtain hemispherical images to assess sky openness and direct/diffuse photosynthetically active flux density under canopy average for the growing season. Concerning the UAV, a Canopy Height Model (CHM) was constructed based on Structure from Motion (SfM) algorithms using Photoscan professional. Different layers such as potential direct and diffuse radiation, direct duration, etc., were extracted from CHM using ArcGIS 10.3.1 (Esri: California, CA, USA). A zonal statistics tool was used in order to extract the digital data in tree positions and, subsequently, the correlation between potential solar radiation layers and the number of seedlings was evaluated. The results of this study showed that there is a high relation between the two used approaches (HP and UAV) with R2 = 0.74. Finally, potential diffuse solar radiation derived from both methods had the highest significant relation (-8.06% bias) and highest impact in the modeling of pine regeneration. [ABSTRACT FROM AUTHOR]

Cameras are a crucial exteroceptive sensor for self-driving cars as they are low-cost and small, provide appearance information about the environment, and work in various weather conditions. They can be used for multiple purposes such as visual navigation and obstacle detection. We can use a surround multi-camera system to cover the full 360-degree field-of-view around the car. In this way, we avoid blind spots which can otherwise lead to accidents. To minimize the number of cameras needed for surround perception, we utilize fisheye cameras. Consequently, standard vision pipelines for 3D mapping, visual localization, obstacle detection, etc. need to be adapted to take full advantage of the availability of multiple cameras rather than treat each camera individually. In addition, processing of fisheye images has to be supported. In this paper, we describe the camera calibration and subsequent processing pipeline for multi-fisheye-camera systems developed as part of the V-Charge project. This project seeks to enable automated valet parking for self-driving cars. Our pipeline is able to precisely calibrate multi-camera systems, build sparse 3D maps for visual navigation, visually localize the car with respect to these maps, generate accurate dense maps, as well as detect obstacles based on real-time depth map extraction. [ABSTRACT FROM AUTHOR]

This paper presents a practical application of a technique that uses a vertical optical flow with a fisheye camera to generate dense point clouds from a single planimetric station. Accurate data can be extracted to enable the measurement of tree trunks or branches. The images that are collected with this technique can be oriented in photogrammetric software (using fisheye models) and used to generate dense point clouds, provided that some constraints on the camera positions are adopted. A set of images was captured in a forest plot in the experiments. Weighted geometric constraints were imposed in the photogrammetric software to calculate the image orientation, perform dense image matching, and accurately generate a 3D point cloud. The tree trunks in the scenes were reconstructed and mapped in a local reference system. The accuracy assessment was based on differences between measured and estimated trunk diameters at different heights. Trunk sections from an image-based point cloud were also compared to the corresponding sections that were extracted from a dense terrestrial laser scanning (TLS) point cloud. Cylindrical fitting of the trunk sections allowed the assessment of the accuracies of the trunk geometric shapes in both clouds. The average difference between the cylinders that were fitted to the photogrammetric cloud and those to the TLS cloud was less than 1 cm, which indicates the potential of the proposed technique. The point densities that were obtained with vertical optical scanning were 1/3 less than those that were obtained with TLS. However, the point density can be improved by using higher resolution cameras. [ABSTRACT FROM AUTHOR]

人群运动估计是人群行为分析的重要步骤。特定场景的人群运动分析和监控，是维护公共安全和社会稳定的一个必要措施，也是视频监控领域的一个研究难点。利用鱼眼相机视场大、无视觉盲区的优点，提出了一种基于特征点光流的人群运动估计方法。首先，采用一种基于面积反馈机制的混合高斯背景差分方法，对原始视频图像进行预处理，并利用圆拟合的方法获取兴趣区域；其次，为了在保证准确描述人群目标的同时提高算法的实时性，提出一种基于边缘密度非均匀采样的人群特征点提取方法来描述运动的人群目标，并利用Lucas & Kanade光流法计算光流场；最后，为了解决远近人群的尺寸大小不一致的问题和鱼眼相机的畸变问题，采用鱼眼相机的透视加权模型，计算人群运动加权统计直方图，获取人群在鱼眼图像中的全局运动方向和速度。实验结果表明，针对密集的人群，该方法能有效、实时地估计人群的运动方向和速度，为人群行为分析提供有力的研究基础。 [ABSTRACT FROM AUTHOR]

This study presents an approach for estimating the fisheye camera parameters using three vanishing points corresponding to three sets of mutually orthogonal parallel lines in one single image. The authors first derive three constraint equations on the elements of the rotation matrix in proportion to the coordinates of the vanishing points. From these constraints, the rotation matrix is calculated under the assumption of the image centre known. The experimental results with synthetic images and real fisheye images validate this method. In contrast to the existing methods, the authors method needs less image information and does not know the three‐dimensional reference point coordinates.