Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard

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.Comment: 20 pages, submitted to the journal of Remote Sensin

Preliminary survey of historic buildings with wearable mobile mapping systems and uav photogrammetry

In cultural heritage, three-dimensional documentation of historic buildings is fundamental for conservation and valorisation projects. In recent years, the consolidated tools and methods: Terrestrial Laser Scanning (TLS) and close-range photogrammetry, have been joined by portable Mobile Mapping Systems (MMSs), which can offer significant advantages in terms of speed of survey operations at the price of reduced accuracy. The reduction of survey times and, therefore, costs makes the application of MMS techniques ideal for the preliminary stages of analysis of historical artifacts, when a rapid survey is indispensable for estimating the costs of conservation interventions. In this paper, we present a methodology for the expeditious survey of historic buildings and the surrounding urban fabric that is based on the use of an MMS and an Unmanned Aerial Vehicle (UAV). The MMS is the Gexcel Heron MS Twin color. It was used to survey two architecture of interest and the urban context surrounding them from the ground level. The UAV is the DJI Mini 2, used to integrate the terrestrial survey by acquiring the buildings' roofs. The case study presented in the paper is the survey of San Clemente and San Zeno al Foro churches, two historic churches in the city centre of Brescia (Italy). The result are a complete point cloud of the two buildings and a metric virtual tour of all spaces. These results were made available to the architects through the Cintoo web platform to plan future activities

LO-Net: Deep Real-time Lidar Odometry

We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar odometry estimation. Unlike most existing lidar odometry (LO) estimations that go through individually designed feature selection, feature matching, and pose estimation pipeline, LO-Net can be trained in an end-to-end manner. With a new mask-weighted geometric constraint loss, LO-Net can effectively learn feature representation for LO estimation, and can implicitly exploit the sequential dependencies and dynamics in the data. We also design a scan-to-map module, which uses the geometric and semantic information learned in LO-Net, to improve the estimation accuracy. Experiments on benchmark datasets demonstrate that LO-Net outperforms existing learning based approaches and has similar accuracy with the state-of-the-art geometry-based approach, LOAM