LiDAR-camera calibration is a precondition for many heterogeneous systems that fuse data from LiDAR and camera. However, the constraint from the common field of view and the requirement for strict time synchronization make the calibration a challenging problem. In this paper, we propose a hybrid LiDAR-camera calibration method aiming to solve these two difficulties. The configuration between LiDAR and camera is free from their common field of view as we move the camera to cover the scenario observed by LiDAR. 3D visual reconstruction of the environment can be achieved from the sequential visual images obtained by the moving camera, which later can be aligned with the single 3D laser scan captured when both the scene and the equipment are stationary. Under this design, our method can further get rid of the influence from time synchronization between LiDAR and camera. Moreover, the extended field of view obtained by the moving camera can improve the calibration accuracy. We derive the conditions of minimal observability for our method and discuss the influence on calibration accuracy from different placements of chessboards, which can be utilized as a guideline for designing high-accuracy calibration procedures. We validate our method on both simulation platform and real-world datasets. Experiments show that our method can achieve higher accuracy than other comparable methods.
Unlike the approaches above, @cite_27 utilizes the reflectance intensity to estimate the corners of the chessboard from the 3D laser point cloud. If the corners of the 3D laser point cloud are identified, the extrinsic calibration is converted to a 3D-2D matching problem. However, the noise and sparsity of the point cloud data present a challenge in this approach. Moreover, like the other appearance-based methods, this method has a limitation where there must be a shared common field of view. Even in the application scenario where the condition is met, the requirement of the common field of view constrains the scale of the scene and limits the number of targets that can be detected, thus affecting the accuracy of the calibration.
@cite_27
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This paper presents a novel method for fully automatic and convenient extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally printed chessboard. The proposed method is based on the 3D corner estimation of the chessboard from the sparse point cloud generated by one frame scan of the LiDAR. To estimate the corners, we formulate a full-scale model of the chessboard and fit it to the segmented 3D points of the chessboard. The model is fitted by optimizing the cost function under constraints of correlation between the reflectance intensity of laser and the color of the chessboard’s patterns. Powell’s method is introduced for resolving the discontinuity problem in optimization. The corners of the fitted model are considered as the 3D corners of the chessboard. Once the corners of the chessboard in the 3D point cloud are estimated, the extrinsic calibration of the two sensors is converted to a 3D-2D matching problem. The corresponding 3D-2D points are used to calculate the absolute pose of the two sensors with Unified Perspective-n-Point (UPnP). Further, the calculated parameters are regarded as initial values and are refined using the Levenberg-Marquardt method. The performance of the proposed corner detection method from the 3D point cloud is evaluated using simulations. The results of experiments, conducted on a Velodyne HDL-32e LiDAR and a Ladybug3 camera under the proposed re-projection error metric, qualitatively and quantitatively demonstrate the accuracy and stability of the final extrinsic calibration parameters. Large scale 3D image localization requires computationally expensive matching between 2D feature points in the query image and a 3D point cloud. In this paper, we present a method to accelerate the matching process and to reduce the memory footprint by analyzing the view-statistics of points in a training corpus. Given a training image set that is representative of common views of a scene, our approach identifies a compact subset of the 3D point cloud for efficient localization, while achieving comparable localization performance to using the full 3D point cloud. We demonstrate that the problem can be precisely formulated as a mixed-integer quadratic program and present a pointwise descriptor calibration process to improve matching. We show that our algorithm outperforms the state-of-theart greedy algorithm on standard datasets, on measures of both point-cloud compression and localization accuracy. One of the main applications of the terrestrial laser scanner is the visualization, modeling and monitoring of man-made structures like buildings. Especially surveying applications require on one hand a quickly obtainable, high resolution point cloud but also need observations with a known and well described quality. To obtain a 3D point cloud, the scene is scanned from different positions around the considered object. The scanning geometry plays an important role in the quality of the resulting point cloud. The ideal set-up for scanning a surface of an object is to position the laser scanner in such a way that the laser beam is near perpendicular to the surface. Due to scanning conditions, such an ideal set-up is in practice not possible. The different incidence angles and ranges of the laser beam on the surface result in 3D points of varying quality. The stand-point of the scanner that gives the best accuracy is generally not known. Using an optimal stand-point of the laser scanner on a scene will improve the quality of individual point measurements and results in a more uniform registered point cloud. The design of an optimum measurement setup is defined such that the optimum stand-points are identified to fulfill predefined quality requirements and to ensure a complete spatial coverage. The additional incidence angle and range constraints on the visibility from a view point ensure that individual scans are not affected by bad scanning geometry effects. A complex and large room that would normally require five view point to be fully covered, would require nineteen view points to obtain full coverage under the range and incidence angle constraints. Abstract. Three-dimensional (3D) models of historical buildings are created for documentation and virtual realization of them. Laser scanning and photogrammetry are extensively used to perform for these aims. The selection of the method that will be used in threedimensional modelling study depends on the scale and shape of the object, and also applicability of the method. Laser scanners are high cost instruments. However, the cameras are low cost instruments. The off-the-shelf cameras are used for taking the photogrammetric images. The camera is imaging the object details by carrying on hand while the laser scanner makes ground based measurement. Laser scanner collect high density spatial data in a short time from the measurement area. On the other hand, image based 3D (IB3D) measurement uses images to create 3D point cloud data. The image matching and the creation of the point cloud can be done automatically. Historical buildings include more complex details. Thus, all details cannot be measured by terrestrial laser scanner (TLS) due to the blocking the details with each others. Especially, the artefacts which have complex shapes cannot be measured in full details. They cause occlusion on the point cloud model. However it is possible to record photogrammetric images and creation IB3D point cloud for these areas. Thus the occlusion free 3D model is created by the integration of point clouds originated from the TLS and photogrammetric images. In this study, usability of laser scanning in conjunction with image based modelling for creation occlusion free three-dimensional point cloud model of historical building was evaluated. The IB3D point cloud was created in the areas that could not been measured by TLS. Then laser scanning and IB3D point clouds were integrated in the common coordinate system. The registration point clouds were performed with the iterative closest point (ICP) and georeferencing methods. Accuracy of the registration was evaluated by convergency and its standard deviations for the ICP and residuals on the control points for the georeferencing method.